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(I Research in Education Seventh Edition JOHN W. BEST Butler University, Emeritus JAMES V. KAHN University of Illinois at Chicago PrenticeHaU of India öwi New Dethi-110001 1995 — — *_,It. ''

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___ BRIEF CONTENTS Preface Part One IntrodUction to Educational Research: Definitions, Research Problems, Proposals, and Report Writing 1 The Meaning of Research 2 SelectIng a Problem an4 4JarIngih Propimal 3 The Research Report Part Two Research MethodsL 4 Historical Research S Descriptive Studies: Assessment, Evaluation, and Research 6 Experimental and Quasi-experimental Research 7 Single-Subject Experimental Research 8 Qualitative Research 9 Methods and Tools of Research Part Three Data Analysis 10 Descriptive Data Analysis 11 Inferential Data Analysis 12 Computer Data Analysis xl 1 3 31 61 81 85 105 133 165 183 207 271 273 323 371 III _wm ___ SECL.DAW NATVD' TrI.LI8RARY J - di AGG.
... p ___CONTENTS Preface Part One Introduction to Educational Research: Definitions, Research Problems, Proposals, and Report Writing 1 The Meaning of Research The Search for Knowledge 3 Science.

7 The Role of Theory 9 Operational Definitions of Variables 10 The Hypothesis 11 Sampling 13 Randomness 13 What Is Research?
20 Purposes of Research 23 Assessment, Evaluation, and Descriptive Research 25 Types of Educational Research 26 Summary 27 Exercises 28 References 29 Additional Readings 30 xl 3 2 Selecting a Problem and Preparing a Research Proposal 31 The Academic Research Problem 32 The Research Proposal 38 Ethics in Human Experimentation 43 Using the Library 47 V vi Finding Related Literature 47 Note Taking 49 The First Research Project 51 Submitting a Research Proposal to a Funding Agency 55 Summary 56 Exercises 57 References 58 Additional Readings 58 3 The Research Report Style Manuals 61 Format of the Research Report 62 The Thesis or Dissertation 67 Style of Writing 67 Typing the Report 68 Reference Form 69 Pagination 71 Tables 71 Figures 73 Evaluating a Research Report 78 Summary 79 References 80 Additional Readings 80 Part Two Research Methods 4 HIstorical Research The History of American Education 86 History and Science 88 The Historical Hypothesis 91 Sources of Data 93 Historical Crltlci.m 95 Writing the Historical Report 100 Summary 100 ExercIses 102 61 85 vii 105 References 102 Additional Readings 103 5 Descriptive Studies: Assessment, Evaluation, and Research Assessment Studies 107 Evaluation Studies 113 Assessment and Evaluation in Problem Solving 116 The Follow-up Study 118 Descriptive Research 120 Replication and Secondary Analysis 124 The Post Hoc Fallacy 127 Summary 129 Exercises 129 References 130 Additional Readings 131 6 Experimental and Quasi-experimental Research 133 Early Experimentation 134 Experimental and Control Groups 135 Variables 137 Controlling Extraneous Variables 138 Experimental Validity 140 Experimental Design 146 Summary 162 Exercises 163 References 163 7 Single-Subject Experimental Research 165 General Procedures 167 Assessment 171 Basic Designs 173 Evaluating Data 177 Summary 179 Exercises 180 References 181 vhf 8 Qualitative Research Themes of Qualitative Research 184 Research Questions 187 Theoretical Traditions 188 Research Strategies 190 Data Collection Techniques 198 Summary 204
 Exercises 205 References 205 Additional Readings 206 9 Methods and Tools of Research 207 Reliability and Validity of Research Tools 208 Quantitative Studies 208 Qualitative Studies 211 Psychological Tests and Inventories 212 Qualities of a Good Test or Inventory 217 Observation 221 Inquiry Forms: The Questionnaire 229 Inquiry Forms: The Opinionnaire 245 The Interview 251 Q Methodology 254 Social Scaling 256 Organization of Data Collection 260 Limitations and Sources of Error 265 Summary 266 Exercises 267 References 267 Additional Readings 268 Part Three Data Analysis 10 Descriptive Data Analysis What Is Statistics?
274 Parametric and Nonparametric Data 275 271 273 Descriptive and Inferential Analysis 276 The Organization of Data 277 Statistical Measures 278 Normal Distribution 287 Measure of Relative Position: Standard Scores 293 Measures of Relationship 297 Interpretation of a Correlation Coefficient 308 Standard Error of Estimate 312 A Note of Caution 314 Summary 316 Exercises 316 References 320 11 Inferential Data Analysis Statistical Inference 323 The Central Limit Theorem 324 Parametric Tests 327 Testing Statistical Significance 328 Decision Making 331 Student's Distribution (t) 336 Homogeneity of Variances 338 Analysis of Variance (ANOVA) 345 Analysis of Covariance (ANCOVA) and Partial Correlation 351 Multiple Regression and Correlation 352 Nonparametric Tests 356 Summary 365 Exercises 365 References 369 Additional Readings 369 12 Computer Data Analysis The Computer 371 Data Organization 373 Computer Analysis of Data 376 Summary 386 Suggested References 387 323 371 Appendix A Statistical Formulas and Symbols Appendix B: Percentage of Area Lying Between the Mean and Successive Standard Deviation Units Under the Normal Curve Appendix C: Critical Values for Pearson's Product-Moment Correlation (r) 397 Appendix D: Critical Values of Student's Distribution (t) 399 Appendix E: Abridged Table of Critical Values for Chi Square Appendix F: Critical Values of the F Distribution Appendix G: Research Course Report Evaluation Appendix H: Answers to Statistics Exercises Appendix I: Selected Indexes, Abstracts, and Reference Materials Author Index Subject Index 401 403 407 409 413 425 429 PREFACE The seventh edition of Research in Education has the same goals as the previous editions.
The book has been written to be used as a research reference or as a text in an introductory course in research methods.

It is appropriate for graduate students enrolled in a research seminar, for those writing a thesis or dissertation, or for those who carry on research as a professional activity.

All professional workers should be familiar with the methods of research and the analysis of data.
If only as consumers, profes- sionals should understand some of the techniques used in identifying problems, forming hypotheses, constructing and using data-gathering in- struments, designing research studies, and employing statistical proce- dures to analyze data.

They should also be able to use this information to interpret and critically analyze research reports that appear in profes- sional journals and other publications.
No introductory course can be expected to confer research compe- tence, nor can any book present all relevant information.

Research skill and understanding are achieved only through the combination of course- work and experience.

Graduate students may find it profitable to carry on a small-scale study as a way of learning about research.

This edition expands and clarifies a number of ideas presented in previous editions.

The book has been reorganized into three parts with a reordering of the chapters.
A new chapter on qualitative research methods has been added which greatly expands upon the information contained in the previous edition.

Additional concepts, procedures, and examples have been added.

Nothing has been deleted from the sixth edition, al- though a few topics have been moved.

This edition, as also was true of the sixth edition, has been written to conform to the guidelines of the American Psychological Assodation's (APA) Publications Manual (3rd ed.).
The writing style suggested in Chapter 3 is also in keeping with the APA manual.

Many of the topics covered in this book may be peripheral to the course objectives of some instructors.

It is not suggested that all of the topics in this book be included in a single course.

It is recommended that instructors use the topics selectively and in the sequence that they find most appropriate.
The portion of the book not used in those courses can x 1 xii Preface then be used by the student in subsequent courses, to assist in carrying out a thesis, and/or as a reference.

This revision benefited from the comments of the second author's students who had used the earlier editions of this text.

To them and to the anonymous manuscript reviewers, we express our appreciation.
We wish to acknowledge the cooperation of the University of Illinois atChi- cago Computer Center, SPSS, Inc., and SAS Institute, Inc.

Finally, we are grateful to our wives, Solveig Ager Best and Kathleen Cuerdon-Kahn for their encouragement and support.

J.W.B.

J.V.K.
___ PART ONE Introduction to Educational Research: Definitions, Research Problems, Proposals, and Report Writing THE FIRST THREE CHAPTERS of this book are intended to provide the student with the historical underpinnings of educational research, define some basic concepts, describe the processes of selecting a research problem to be investigated and writing a research proposal, and provide the student with a style of writing that can be used to write research reports, research pro- posals, and term papers.
Chapter 1 is designed to introduce the reader to the research endeavor.

Such matters as methods of science, the importance of theory, the formulation methodologies of hypotheses, sampling techniques, and an overview of used in educational research are described.

I 2 Part One Introduction to Educational Research Chapter 2 describes the process by which a research problem is 'den- tified.
This is one of the most difficult steps in the research process for beginners, and sometimes for experienced researchers as well.

This chapter also discusses the ethics of conducting research with humans, some sug- gestions for library research, and how to write a research proposal.

Chapter 3 describes one style for writing a research report, the style of the American Psychological Association.
This style was selected because it is the most commonly accepted by journals in the field of education and psychology.

The description includes writing style, typing the manuscript, referencing other works, tables, and figures.

In the previous edition, this chapter was at the end of the book.

Jt was moved because the style suggested can be useful for writing literature reviews and research proposals as well as research reports.
This chapter also briefly describes an approach to eval- uating research reports written by others.

__ 1 The Meaning of Research The Search for Knowledge Human beings are the unique product of their creation and evolution.

In contrast to other forms of animal life, their more highly developed nervous system has enabled them to develop sounds and symbols (letters and numbers) that make possible the communication and recording of their questions, observations, experiences, and ideas.
It is understandable that their greater curiosity, implemented by their control of symbols, would lead them to speculate about the operation of the universe, the great forces beyond their own control.

Over many cen- turies, people began to develop what seemed to be plausible explanations.

Attributing the forces of nature to the working of supernatural powers, they believed that the gods, at their whims, manipulated the sun, stars, wind, rain, and lightning.
The appearance of the medicine man or priest, who claimed special channels of communication with the gods, led to the establishment of a system of religious authority passed on from one generation to another.

A rigid tradition developed, and a dogma of nature'sprocesses, explained in terms of mysticism and the authority of the priesthood, became firmly rooted, retarding further search for truth for centuries.
But gradually people began to see that the operations of the forces of nature were not as capricious as they had been led to believe.

They began to observe an orderliness in the universe and certain cause-and- effect relationships; they discovered that under certain conditions events could be predicted with reasonable accuracy.

However, these explanations ere often rejected if they seemed to conflict with the dogma of religious authority.
Curious persons who raised questions were often punished and 3 4 Part One Introduction to Educational Research even put to death when they persisted in expressing doubts suggested by such unorthodox explanations of natural phenomena.

This reliance on empirical evidence or personal experience challenged the sanction of vested authority and represented an important step in the direction of scientific inquiry.
Such pragmatic observation, however, was largely unsystematic and further limited by the lack of an objective method.

Observers were likely to overgeneralize on the basis of incomplete ex- perience or evidence, to ignore complex factors operating simultaneously, or to let their feelings and prejudices influence both their observations and their conclusions.

It was only when people began to think systematically about thinking itself that the era of logic began.
The first systematic approach to reason- ing, attributed to Aristotle and the Greeks, was the deductive method.

The categorical syllogism was one model of thinking that prevailed among early philosophers.

Syllogistic reasoning established a logical relationship between a major premise, a minor premise, and a conclusion.
A major premise is a self-evident assumption, previously established by metaphysical truth or dogma, that concerns a relationship; a minor premise is a particular case related to the major premise.

Given the logical relationship of these premises, the conclusion is inescapable.

A typical Aristotelian categorical syllogism follows: Major Premise .

.

.

.

All men are mortal.

Minor Premise.

.

.

.

Socrates is a man.

Conclusion.

.

.

.

Socrates is mortal.


This deductive method, moving from the general assumption to the spedflc application, made an important contribution to the development of modern problem solving.

But it was not fruitful in arriving at new truths.

The acceptance of incomplete or false major premises that were based on old dogmas or unreliable authority could only lead to error.

Semantic difficulties often resulted from shifting definitions of the terms involved.
Centuries later, Francis Bacon advocated direct observation of phe- nomena, arriving at conclusions or generalizations through the evidence of many individual observations.

This inductive process of moving from specific observations to the generalization freed logic from some of the hazards and limitations of deductive thinking.
Bacon recognized the ob- stade that the deductive process placed in the way of discovering new truth: It started with old dogmas that religious or intellectual authorities had already accepted and thus could be expected to arrive at few new truths.

These impediments to the discovery of truth, which he termed "idols," were exposed in his Novum Organum, written in 1620.
The following story, attributed to Bacon, expresses his revolt against the authority of the written word, an authority that dominated the search for truth during the Middle Ages: Chapter 1 The Meaning of Research 5 In the year of our Lord, 1432, there arose a grievous quarrel among the brethren over the number of teeth in the mouth of a horse.

For thirteen days the disputation raged without ceasing.
All the ancient books and chronicles were fetched out, and wonderful and ponderous erudition was made manifest.

At the beginning of the fourteenth day a youthful friar of goodly bearing asked his learned superiors for permission to add a word, and straightway, to the wonder of the disputants, whose deep wisdom he sorely vexed, he beseeched them in a manner coarse and unheard of, to look in the mouth of a horse and find answers to theirS questionings.
At this, their dignity being grievously hurt, they waxed exceedingly wroth; and joining in a mighty uproar they flew upon him and smote him hip and thigh and cast him out forthwith.

For, said they, "Surely Satan hath tempted this bold neophyte to declare unholy and unheard-of ways of finding truth, contrary to all the teachings of the fathers."
After many days of grievous strife the dove of peace sat on the assembly, and they, as one man, declaring the problem to be an everlasting mystery because of a dearth of historical and theological evidence thereof, so ordered the same writ down.

(Mees, 1934, pp.
(Mees, 1934, pp.

13—14) The method olinductive reasoning proposed by Bacon, a method new to the field of logic but widely used by the scientists of his time, was not hampered by false premises, by the inadequacies and ambiguities of verbal symbolism, or by the absence of supporting evidence.

But the inductive method alone did not provide a completely satis- factory system for the solution of problems.

Random collection of indi- vidual observations without a unifying concept.
or focus often obscured investigations and therefore rarely led to a generalization or theory.

Also, the same set of observations can lead to different conclusions and support different, even opposing theories.

The deductive method of Aristotle and the inductive method of Bacon were fully integrated in the work of Charles Darwin in the nineteenth century.

During his early career his observations of animal life failed to lead to a satisfactory theory of man's development.
The concept of the struggle for existence in Thomas Maithus' Essay on Population intrigued Darwin and suggested the assumption that natural selection explains the origin of different species of animals.

This hypothesis provided a needed focus for his investigations.

He proceeded to deduce specific consequences suggested br the hypothesis.
The evidence gathered confirmed the hy- pothesis that biological change in the process of natural selection, in which favorable variations were preserved and unfavorable ones destroyed, re- sulted in the formation of new species.

The major premise of the older deductive method was gradually replaced by an assumption or hypothesis that was subsequently tested by the collection and logical analysis of data.

This deductive-inductive method is now recognized as an example of a scientific approach.
6 Part One Introduction to Educational Research John Dewey (1938) suggested a pattern that is helpful in identifying the elements of a deductive—inductive process: - A Method of Science 1.

Identification and definition of the problem 2.

Formulation of a hypothesis—an idea as to a probable solution to the problem, an intelligent guess or hunch 3.

Collection, organization, and analysis of data 4.

Formulation of conclusions 5.


Verification, rejection, or modification of the hypothesis by the test of its consequences in a specific situation Although this pattern is a useful reconstruction of some methods of scientific inquiry, it is not to be considered the only scientific method.

There are many ways of applying logic and observation to problem solv- ing.

An overly rigid definition of the research process would omit many ways in which researchers go about their tasks.
The planning of a study may include a great deal of exploratory activity, which is frequently in- tuitive or speculative and, at times, a bit disorderly.

Although researchers must eventually identify a precise and significant problem, their object may initially be vague and poorly defined.

They may observe situations that seem to suggest certain possible cause-and-effect relationships and even gather some preliminary data to examine for possible relevancy to their vaguely conceived problem.
Thus, much research begins with the inductive method.

At this stage, imagination and much speculation are essential to the formulation of a clearly defined problem that is susceptible to the research process.

Many students of research rightly feel that prob- lem identification is one of the most difficult and most crucialsteps of the research process.

Frequently researchers are interested in complex problems, the full investigation of which requires a series of studies.
This approach is known as programmatic research and usually combines the inductive and deductive methods in a continuously alternating pattern.

The researcher may begin with a number of observations from which a hypothesis is derived (in- ductive reasoning).

Then the researcher proceeds deductively to determine the consequences that are to be expected if the hypothesis is true.

Data are then collected through the inductive method to verify, reject, or modify the hypothesis.
Based on the findings of this- study, the researcher goes on to formulate more hypotheses to further inyestigate the complex prob- lem under study.

Thus, the researcher is continuaJly moving back and forth between the inductive method of observation and data collection and the deductive method of hypothesizing the anticipated consequences to events.

Chapter 1 The Meaning of Research The term science may be thought of as an approach to the gathering of knowledge rather than as a field of subject matter.
Science, put simply, consists of two primary functions: (1) the development of theory and (2) the testing of substantive hypotheses that are deduced from theory.

The scientist, therefore, is engaged in the use, modification, and/or cre- ation of theory.

The scientist may emphasize an empirical approach in which data collection is the primary method, a rational approach in which logical and deductive reasoning is primary, or a combination of these approaches, which is most common.
Regardless of the emphasis, the scientist begins with a set of ideas that dIrect the effort and with a goal that entails the development or testing of theory.

By attempting to apply the rigorous, systematic observation and analysis used in the physical and biological sciences to areas of social behavior, the social sciences have grown and have advanced humanity's knowledge of itself.
The fields of anthropology, economics, education, political science, psychology, and social psychology have become recog- nized as sciences by many authorities.

To the extent that these fields are founded on scientific methodology, they are sciences.

Some reject this concept, still defining science in terms of subject matter rather than meth- odology.

Historically their position can be readily explained.
Since scien- tific methods were first used in the investigation of physical phenomena, tradition has identified science with the physical world.

Only within the last century has the methodology of science been applied to the study of various areas of human behavior.

Since these are newer areas of investi- gation, their results have not achieved the acceptance and status that come with the greater maturity and longer tradition of the physical sciences.
The uniformity of nature is a reasonable assumption in the world of physical objects and their characteristics, but in the area of social behavior such assumptions are not warranted.

Human nature is much more com- plex than the sum of its many discrete elements, even if they could be isolated and identified.

Because human nature is so complex, it is much more difficult to develop sound theories of human behavior than to predict occurrences in the physical world.
Research on human subjects has nu- merous problems.

1.

No two persons are alike in feelings, drives, or emotions.

What may be a reasonable prediction for one may be useless for another.

2.

No one person is completely consistent from one moment to another.

Human behavior is influenced by the interaction of the individual with every changing element in his or her environment, often in a way tlat is difficult to predict.

8 Part One Introduction to Educational Research 3.


Human beings are influenced by the research process iLseif.

They are influenced by the attention that is focused on them when under investigation and by the knowledge that their behavior is being observed.

4.

The behavioral sciences have been limited by a lack of adequate definition.

Accurate operational definitions are essential to the de- velopment of a sophisticated science.
Such traits as inteffigence, learning, hostility, anxiety, or motivation are not directly observable and are generally referred to as "constructs," implying that they are constructions of the scientist's imagination, constructs cannot be seen, heard, or felt.

They can only be inferred by,, phenomena such as test scores or by observed hostile or aggressive acts, skin re- sponses, pulse rates, or persistence at a task.
But even constructs for which useful descriptive instruments are available account for only limited sources of variation; they yield only partial definitions.

For example, intelligence, as defined by a score on an inteffigence test, is not a satisfactory measure of the type of intelligence that individuals are called upon to demonstrate in a variety of situations outside a formal academic environment.
In the physical sciences, many complex constructs have been more effectively defined in operational terms.

Time is one such construct: Time is a function of the motion of the earth in relation to the sun, measured by the rotation of a hand on the face of a circular scale in precise units.

Weight is a construct involving the laws of gravitation, measured by springs, torsion devices, levers, or electronic adaptations of these instruments.
The instruments which measure such constructs are devised so that they are consistent, to a maximum degree, with known physical laws and forces, and yield valid descriptions in a variety of situations.

An inter- national bureau prescribes standards for these devices so that they may provide precise operational definitions of the constructs.

Although the problems of discovering theories of human behavior are difficult, it is possible to do so.
Behavioral scientists need to carry on their investigations as carefully and rigorously as have physical scientists.

However, one must not overestimate the exáctñess of the physical sci- ences, for theoretical speculations and probability estimates are alsoin- herent characteristics.

Today we live in a world that has benefited greatly from progress made by the biological and physical sciences.

Infant mortality is decreas- ing, and life expectancy continues to increase.
Surgery is now performed on fetuses in utero to correct such conditions as hydrocephalus.

Children born prematurely weighing less than 1000 grams (approximately 2 pounds) survive and generally thrive.

The Salk and Sabin vaccines promise to rid the world of poliomyelitis.

Many forms of cancer are being conquered by early detection and chemotherapy.
Improved nutrition, antibiotics, in- Chapter 1 The Meaning of Research 9 novative surgical techniques, and countless other accomplishments allow us to lead longer, healthier lives.

Automation and computerization touch every aspect of our lives, reducing our physical labor and increasing our leisure time.
The splitting of the atom, space travel, and developments in the field of electronics such as the laser, superconductivity, and the sfficon chip promise improvements and adventures that are beyond the scopeof most people's imagination.

All these improvements have resulted from the investigation of biological and physical sciences.

However, there is less confidence about the improvement of the nonphysical aspects of our world.
Despite all their marvelous gadgets, there is some doubt whether people are happier or more satisfied or whether their basic needs are being fulfilled more effectively today than they were a century ago The fear of nuclear plant failures and the un- certainty about the safe, disposal of nuclear waste are uppermostin the minds of people throughout the world.

Our apparent inabffity to solve various social problems raises the spector of malnutrition, terrorism, and illiteracy.
There is great concern that our children are not learning suffi- ciently to compete in our more technologically complex society.

StandaId.

scores indicate that high school children are less prepared for college today than were their parents and older siblings.

Scientific methods must be applied with greater vigor and imagi- nation to the behavioral aspects of our culture.
The development of the behavioral sciences and their application to education and other human affairs present some of our greatest challenges.

The Role of Theory At this stage in the discussion, a statement about theory is appropriate.

To many people the term theorysuggests an ivory tower, something unreal and of little practical value.

On the contrary, a theory establishes a cause and effect relationship between variables with the purpose of explaining and predicting phenomena.
Those who engage in pure research devote their energies to the formulation and reformulation of theories and may not be concerned with their practical applications.

However, when athe- ory has been established, it may suggest many applications of practical value.

John Dewey once said that there was nothing more practical than a good theory.
Theories about the relationship between the position of the earth and other moving celestial bodies were essential to the successful launch- ing and return of manned space vehides.

Theoriesof the behavior of gases were essential to the development of refrigeration and air conditioning.

Controlled atomic energy could not have been achieved without the es- tablishment of theories about the nature of mass and energy and the structure of the atom.
The real purpose of scientific methods is prediction, 10 Part One Introduction to Educational Research the discovery of certain theories or generalizations that anticipate future occurrences with maximum probability.

Piaget's theory of cognitive development is a good example of a theory that has been developed with little or no concern for application.
Only one of Piaget's many books discussed education in any great detail (Piaget, 1970), and even this book does not deal with the specifics that most teachers need.
However, innumerable books, chapters, and articles written by followers of Piaget have explicated the usefulness of his theory for teaching practices from preschoci (e.g., Kamii, 1973; Lavateffi, 1973) to high school (e.g., Karplus et a!., 1977; Stayer & Gabel, 1979), and even for teaching mentally retarded (e.g., Kahn, 1984, 1987) and other handi- capped children (e.g., Wolirisky, 1970).
So although Piaget's aim was to understand the cognitive structures and functioning of children and adults, his theory has been embraced by educators and psychologists who have investigated ways in which his theory could be used to improve educa- tional practice.

But what do we mean by the term theory?

A theory is an attempt to develop a general explanation for some phenomenon.
A theory defines nonobservable constructs that are inferred from observable facts and events and that are thought to have an effect on the phenomenon under study.

A theory describes the relationship among key variables for purposes of explaining a current state or predicting future occurrences.

A theory is primarily concerned with explanation and therefore focuses on determin- ing cause—effect relationships.
Operational Definitions of Variables Such variables as giftedness, academic achievement, and creativity are conceptualizations that are defined in dictionary terms.

But becausethey cannot be observed directly, they are vague and ambiguous and provide a poor basis for identifying variables.

Much more precise and unambig- uous definitions of variables can be 'stated in operational form, which stipulates the operation by which they can be observed and measured.
Giftedness could be operationally defined asa score two or more standard deviations above the mean on the Wechsler AdultIntelligence Scale, academic achievement as a score on the 1973 edition of theStanford Achievement Test, or creativity as a score on the Torrance Tests of Creative Thinking.

When an operational definition is used, there is no doubt about what the researcher means.

To be useful, however, operational definitions must be based upon a theory that is generally recognized as valid.
Operational terms do not always prove to be useful in describing variables, for they could conceiv- ably be based upon irrelevant behavior.

Defining degree of self-esteemin terms of the number of times an individual smiles per minute would not Chapter 1 The Meaning of Research 11 be a useful or realistic definition, even though such behavior could easily be observed and recorded.
The Hypothesis Two important functions that hypotheses serve in scientific inquiry are the development of theory and the statement of parts of an existing theory in testable form.

Snow (1973) describes six levels of theory, with the first level being hypothesis formation.

At this initial level, the theory developer .obeervatian&, andlor in jormatien-gaied--frurnothers.

A hypothesis is formulated in such a way that thisj hçjested.

8aed uponThe findings of the subsequeM researchthe.pQthes, is supported.
or rejected and more hypotheses aziornwlated to continue the process of buildinga.cohesive theory.

a lv based on theory,.

pa&t-eperi.

The most common use of hypotheses is to test whether an existing theory can be used to solve a prob1em4aevesyaysitualions, those who confront problems often prepose-informal4ypethees that can be tested directly.
For example, when a laip,p fails to 1ighjwhen the switch is turned on, several hypotheses come to mind basediipon our understanding of electricity and our past experiences with lamps: 1.

The plug is not properly connected to the wall outlet.

2.

The bulb is burned out.

3.

The fuse is burned out or the circuit breaker has been tripped.

4.

There has been a power failure in the neighborhood.
Each of these speculations ç sted_directlyjy_checking the plug connection, substituting a bulb known to be in working condition, inspecting the fuse or circuit breaker, or by noting whether or not other lights in the house or in neighbors' houses are on.

The Research Hypothesis The research or scientific hypothesis is a formal affirmative statement .pre dictingasügle-esearch-outecme, a tentative explanation of the relation- ship between two or more variables.
For the hypothesis to be testable, the vriables must be operationally defined,.

That is, the researcher spec- ifies what operations were conducted,.

or tests used, to measure each variable.

Thus, and 4eterrniiwswhat.

observaUonsr-ormeasuresobeused* A number of years ago the hypothesis was formulated that there is a positive causal relationship between cigarette smoking and the incidence of coronary heart disease.
This hypothesis proposed a tentative expla- 12 Part One Introduction to Educational Research nation that led to many studies comparing the incidence of heart disease among cigarette smokers and nonsmokers.

As a result of these extensive studie the medical profession now generally accepts that this relationship has been established.

In the behavioral sciences, the variables may be abstractions that cannot be observed.
These variables must be defined operationally by describing some samples of actual behavior that are concrete enough to be observed directly.

The relationship between these observable incidents may be deduced as consistent or inconsistent with the consequences of the hypothesis.

Thus, the hypothesis may be judged to be probably true or probably false.
For example, one might propose the hypothesis that third-grade children taught the Chisanbop hand-calculating process would learn to perform the basic arithmetic processes more effectively (that is, score higher on a specified measure or test of arithmetic processing) than those using the conventional method.

Children would be randomly assigned in two groups, one taught the Chisanbop system (experimental group) and the other using the conventional method (control group).
The experiment would be carried on for a period of nine months.

If the hypothesis were true, the experimental group's mean scores on a standardized arithmetic achievement test would be significantly higher than those of the control group.

The Null Hypothesis (H0) At the beginning of their study, researchers state an affirmative scientific or research hypothesis as a prediction of the outcome that they propose to test.
Most often, this research hypothesis suggests that a difference of some kind (e.g., one group will do better thananother) will occur.

Later, t the stage of the statistical analysis of the observed data, they restate this hypothesis in neave 0 .
Forinstance, the previously stated hypothesis, that third-grade children taught the Chisanbop method would score higher on a specified test of arithmetic than those using the con- ventional method, would be restated: There is no significant difference between the arithmetic achievement of the two groups.

Of course, in rare cases the research hypothesis could be the same as the null hypothesis.
For instance, Kahn (1985) hypothesized that mildly mentally retarded children and children of average inteffigence, equated for mental age, would perform similarly on Riagetian tests.

The null hypothesis relates to a statistical method of interpreting conclusions about population haracteristics that are inferred from the variable relationships observed in samples.

The null hypothesis asserts that observed differences or relationships merely result from chance errors inherent in the sampling process.
Most hypotheses are the opposite of Chapter 1 The Meaning of Research 13 the null hypothesis.

In such a case, if the researcher rjects the null hypothesis, he or she accepts the research hypQthesis, concluding that the magnitude of the observed variable relationship is probably too great to attribute to sampling error.
The logic of the use of the null hypothesis, which may be confusing to students, is explained in greater detail in the discussions of sampling error and the central limit theorem in Chapter 9.

Sampling The primary purpose of research is to discover principles that have uni- versal application, but tQ studyawjj,je pogiJaUon.

to arre atgenerali- zations would be imlracticahle, if not impossible.
Some populations are diäre thai theifharacteristics cannot be measured; before the meas- urement could be completed, the populations would have changed.

Imagine the difficulty of conducting a reading expenment with all American fifth-grade children as subjects.

The study of a population of this size woul require the services of thousands of researchers, the ex- penditure of llions of dollars, and hundreds of thousands of class hours.
Fortunately, the process of sampling makes it possible to draw valid - inferences or generalizations on the basis of careful observation of variables within a relaUvely small proportion 9f the population.

A measured value based upon sample data is a statistic.

A population value inferred from a statistic is a parameter.

A population is any group of individuals that have ne or more char- actenstics in common that are of interest to the researcher.
The population may be all th1idividuals of a particular type, or a more restricted part of that group.

All public schoolteachers, all male secondary schoolteachers, all elementary schoolteachers, or all Chicago kindergarten teachers may be populations.

A sample is a small proportion of a population selected for obseration and analysis.

By oBihg the charactéritics of the sample, one can make certain inferences about the characteristics of the population from which it is drawn.
Contrary to some popular opinion, samples are not selected haphazardly; they are chosen in a systematically random way, so that chance or the operation of probability can be utilized.

Randomness The concept of randomness has been basic to scientific observation and research.

It is based on the assumption that, while individual events cannot be predicted with accuracy, aggregate events can.
For instance, 14 Part One Introduction to Educational Research although it may not predict with great accuracy an individual's academic achievement, it will predict accurately the average academic performance of a group.

Randomization has two important applications in research: 1.

Selecting a group of individuals for observation who are represent- ative of the population about which the researcher wishes to gen- eralize; or 2.

Equating experimental and control groups in an experiment.
As- signing individuals by random assignment is the best method of providing for their equivalence.

It is important to note that a random sample is not necessarily an identical representation of the population.

Characteristics of successive random samples drawn from the same population may differ to some degree, but it is possible to estimate their variation from the population characteristics and from each other.
The variation, known as sampling error, does not suggest that a mistake has been made in the sampling process.

Rather, sampling error refers to the chance variations that occur in sam- pling; with randomization, these variations are predictable and taken into account in data analysis techniques.

The topic of sampling error is considered in greater detail in Chapter 11 in the discussion of the central limit theorem, the standard error of the mean, and the level of significance.
The Simple Random Sample The Individual observations or individuals are chosen in such a way that each has an equal chance of being selected, and each choice is independent of any other choice.

If we wished to draw a sample of 50 indIviduals from a population of 600 students enrolled in a school, we could place the 600 names in a container and, blindfolded, draw one name at a time until the sample of 50 was selected.

This procedure is cumbersome and is rarely used.


Random Numbers A more convenient way of selecting a random sample, or assigning In- dividuals to experimental and control groups so that they are equated, is by the use of a table of random numbers.

Many such tables have been generated by computers producing a random sequence of digits.
The million random digits with 100,000 normal deviates of the Rand Corporation (1965) and Fisher and Yates (1963) Statistical tables for biological, agricultural and medical research are frequently used.

Chapter 1 The Meaning of Research TABLE 1—1 An Abbreviated Table of Random Numbers 50393 09038 82066 91340 13330 31974 06997 84979 92982 22381 44590 39117 07442 24289 23445 89344 63378 72341 72731 46694 02050 61530 61407 95596 The Sample 503 .9!
333 092 ,982 074 426 337 802 050 090 383 197 422 381 242 i97 234 161 530 820 660 699 744 590 .2a( 457 273 407 408 497 117 .893' 444 669 In selecting this sample, eight numbers were deleted.

Numbers 931, 982, 897, 913, 939, and 893 were deleted because they were larger than the population of 835 described.

Numbers 234 and 161 were deleted because they duplicate previous selections.
When a table is used, it is necessary to assign consecutive numbers to each member of the population from which the sample is to be selected.

Then, entering the table at any page, row, or column, the researcher can select the sample from 001 to 999, three digits; and from 0001 to 9999, four digits.

When a duplicated number or a number larger than the pop- ulation size is encountered, it is skipped and the process continues until the desired sample size is selected.
As an illustration, let us assume that a sample of 30 is to be selected from a serially numbered population of 835.

Using a portion of a table of random numbers reproduced here, 30 three-digit numbers are selected by reading from left to right.

When using the table of random numbers to select a sample, one must number the population members serially.

Then, enter the table at any page, row, or column at random, and the sample can be selected by reading to the left, right, up, down, or diagonally.
For populations up to 99 in number, two digits are selected; from 001 to 999, three digits; and from 0001 to 9999, four digits.

These 30 numbered members of the population comprise the sample.

If this group were to be divided into two equated groups of 15 each, the first 15 could compose one group and the second 15 the other, There are many varieties of random assignment, such as assigning the odd numbers to one group (1, 3, 5, 7, .

.

. )

and the even nuMbers (2, 4, 6, 8, .

, . )

to the other.


For those with access to a computer, many packaged computer pro- grams include the capability to produce a random numbers table.

A simple program can generate a table of random numbers designed for a particular study.

As an example, assume that a random sample of 30 Is to be selected 16 Part One Introduction to Educational Research from a serially numbered population of 585 (1 to 585).
The sample is generated on an Apple computer with this program: 100 HOME 110 FOR I 120 X — INT (RND (1) * 585 + 1) 130 PRINT X 140 NEXT I to 30 1 This program will then randomly produce 30 numbers ranging from a possible 1 to 585.

The output looks like this: it is apparent that in order to select a random sample, one must not consciously select any particular individual or observation.
The size of the sample may or may not be significantly related to its adequacy.

A large sample, carelessly selected, may be biased and inaccurate, whereas a smaller one, carefully selected, may be relatively unbiased and accurate enough to make satisfactory inference possible.

However, a well-selected large sample will be more representative of the population than a well- selected smaller sample.
This is explained in greater detail in the discus- sions of sampling error and the central limit theorem in Chapter 11.

In addition to caution in the sampling process, definition of the population about which inferences are to be made is extremely important.

When the now defunct Literary Digest drew its sample for thepurpose of predicting the results of the 1936 presidential election, subjects were cho- sen from the pages of telephone directories and from automobile regis- tration lists.
The prediction of Alfred Landon's victory over Franklin D. Roosevelt proved to be wrong, and a postelection analysis revealed that the population for which the prediction was made was not the same population sampled.

Large numbers of eligible voters did not own auto- mobiles and were not telephone subscribers, and consequently were not included in the sample.

in fact, the resulting sample was systematically biased to overrepresent the wealthy and underrepresent the poor and unemployed.
Chapter 1 The Meaning of Research 17 The Systematic Sample 1 If a population can be accurately listed or is finite, a type of systematic selection will provide what approximates a random sample.

yfc jzth term from a lit.

For exathjle, sample co te1epectFwtth if a samppf 2Q_wem,be_seIected 2jjgs, one would select the fst name by selecting a randomly selected name from a randomly selected page.

Then every thousandth name would be selected.

until the sample of 200 names was complete.


If the last page were reached before the desired number had been selected, the count would continue from the first page of the directory.

Systematic samples of automobile owners could be selected in similar fashion from a state licensing bureau list or file, or a sample of eighth-grade students from a school attendance roll.
The StratifIed Random Sample At times itJathiable to subdivide the populafion into smaller homo- geñëöis groups to get more acc fépresentajipn Thr,.mej.hodxesu1ts irrtheiriitif1ed random sample For example, in an income study of wage éäriiërs in a community, a true sample would approximate the same relative number from each socioeconomic level of the whole community.
If, in the community, the proportion were 15 percent professional workers, 10 percent managers, 20 percent skilled workers, and 55 percent unskilled workers, the sample should include approximately the same proportions in order to be considered representative.

Within each subgroup a random selection should be used.
Thus, for a sample of 100, the researcher would randomly select 15 professional workers from the subpopulation of all professional workers in the community, 10 managers from that subpopula- tion, and so on.

This process gives the researcher a more representative sample than one selected from the entire community, which might be unduly weighted by a preponderance of unskilled workers.
In addition to, or instead of, socioeconomic status, such character- istics as age, sex, extent of formal education, racial origin, religious or political afffliation, or rural-urban residence might provide a basis for choosing a stratified sample.

The characteristics of the entire population, together with the purposes of the study, must be carefully considered before a stratified sample is decided upon.
The Area or Cluster Sample ample is a variation of the simplranisanp1e that The area or ci is particularly appropriate when the populati of interest is infinite, when 18 Part One Introduction to Educational Research a list of the members of the populajpji does 5phic distribution of the individuals is wi4ely ,scattered.

A simple éxahp1iould be if we wanted to survey students in 100 h'terooms in secondary schools in a large school district.
We could first randomly select 10 schools from all of the secondary schools in the district.

Then from a list of homerooms in the 10 schools we could randomly select 100.

A more likely scenario in which cluster sampling would be needed is if, for the purpose of a survey, we wanted to select a sample of all public school elementary teachers in the United States.

A simple random sample would be impracticable.

From the 50 states a random sample of 20 could be selected.
From these 20 states, all counties could be listed and a random sample of 80 counties selected.

From the 80 counties, all the school districts could be listed and a random sample of 30 school districts selected.

It would not be difficult to compile a list of all elementary teachers from the 30 school districts and to select a random sample of 500 teachers.
This successive random sampling of states, counties, school districts, and finally, of individuals would involve a relatively efficient and inexpensive method of selecting a sample of individuals.

This method of sampling is likely to introduce an element of sample bias because of the unequal size of some of the subsets selected.

Only when a simple random sample would be impracticable is this method recommended.
Nonprobability Samples pJ1!2robability samples are thosftiat-tzse-wbtever subjs are avjable, rather tlñ1oThowing-a--spcific sjectselectionproess.

Some nonprob- 5illliamplir öurs-niay-prtduce samples that do not accurately reflect the characteristics of a population of interest.

Such samples may lead to unwarranted generalizations and should not be used if random selection is practicable.
Educational researchers, because of administrative limitations in ran- domly selecting and assigning individuals to experimental and control groups, often use available classes as samples.

The status of groups may be equated by such statistical means as the analysis of covariance (dis- cussed in Chapter 11).

In certain types of descriptive studies, the use of available samples may restrict generalizations to similar populations.
For example, when a psychology professor uses students from Introduction to Psychology classes as subjects, the professor may safely generalize only to other similar groups of psychology students.

A sample made up of those who volunteer to participate in a study may represent a biased sample.

Volunteers are not representative of a total population, for volunteering results in a selection of individuals who are different and who really represent a population of volunteers.
In a Chapter 1 The Meaning of Research 19 sense, those who respond to a mailed questionnaire are volunteers and may not reflect the characteristics of all who were on the mailing list.

It may be desirable to send another copy of the instrument to nonrespon- dents with an appeal for their participation.

Sample Size There is usually a trade-off between the desirability of a large sample and the feasibility of a small one.
tiq1o3QpUlati0fl9tit which the researcher an a4 wisenerizgflall enouglobe seIT1fl fëiñicof sj tava1i!jl eneinJh time and rnony.

acom- is ed nnge of subjects thãUdetermines the size of an adequate sample.

It nature of the population of intere or the data t be gathered and j- lrzed.

A national opInion poll randomly selects a sample of about 1500 subjects as a reflection of the opinions of a population of more than 150 million United States citizens of voting age, with an error factor from 2 to 3 percent.
Before the second decade of the twentieth century, statisticians be- lieved that samples should be relatively large so that the normal probability table could be used to estimate sampling error, explained by the central limit theorem.

(See Chapter 11 for a discussion of sampling error and student's distribution.)

The work of William Sealy Gosset in 1915, in which he developed data on the probability distribution of small sample means (student's t distribution), led to the effective useof small samples.
Gosset's contribution made feasible research studies that necessarily had to be limited to a small number of subjects.

Sn 1LsampJaaeafQh hasmade a. igicnt, cgnjU1jQq to statistical analysisof ret, pticu1arl in experimental studies.

samples j 30. oftiu pJ.ç isapp.rox- lrge samples and thQe.
with fewer thai imately at this sample size of 30 that the magnitude of student's i critical ch the z critical, values, of the normal values for small samples a probability table for large sa) (See Chapter 11 for a discussion of z and t critical values.)

I4ore irnprtant taze is the care with which the sample is s- lected.

The ideal method is random selection, letting chance or the laws of probability determine which members of the population are to be se- lected.
When random sampling is employed, whether the sample is large or small, the errors of sampling may be estimated, giving researchers an idea of the confidence that they may place in their findings In summary, several practical observations about sample size are listed: 20 Part One Introduction to Educational Research 1.

The 2.

Survey-tyt stiifies.

probably should have jrgç saniples than needed rtijesample, in experimental studies.

3.
3.

When sample groups are to be subdivided into smallergroups to be compared, the researcher initially should select large enough samples so that the subgroups are of adequate size for his or her purpose: 4.

In mailed estionnaire studies, because thepercentage of responses ibe as low as 20 to 0 percent, a 1aiger initial sample milin is indicated.

5.

J ct-aya osj factors are legitimate considerations in 4iiy deijriing appropriate sanp1ee.

What Is Research?

How is research related to scientific method?


The terms research and sci- entific method are sometimes used synonymously In educational discus- sions.

Although it is true that the terms have some common elements of meaning, a distinction is helpful.

For the purposes of this discussion, research is consideredto be the more formal, systematic, and intensive process of carrying on a scientific method of analysis.
Scientific method in problem solving may be an in- formal application of problem identification, hypothesis formulation, ob- servation, analysis, and conclusion.

You could reach a conclusion why your car wouldn't start or why a fire occurred in an unoccupied house by employing a scientific method, but the processes involved probably would not be as structured as those of research.

Research is a more systematic activity that is directed toward discovery and thedevelopment of an organized body of knowledge.
Research may be defined as the systematic ions that may lead to the principles, or theories, reuitmjifØrdwwn and eznajjsis and recojn4 trotle4 possibly Because definitions of this sort are rather abstract, a summary of some of the characteristics of research may help to clarify its spirit and meaning.

1.

Research is directed toward the solution of a probi.
The ul- timate gQal iso rIaôuships between yariables, thàiih researchers often have to settle for the useful disoveiy-of a -sys- tematic relationship cause the evidence for a cause-and-effect relation- ship is insufficient.

2.
2.

Ra emphasizes the development QLgejalizations pjjn- ciples, or theones that wilt-be Iiglpful in predicting future occurrences Réiéarch usually goes beyond the specific objects, groups, or situations investigated and infers characteristics of a target population from the rT wrIwI' ',' - *.

s 1 The Meaning of Research 21 sample observed.

Research is more than information retrieval, the simple gathering of informon.
Although many school reseiiiefltS gather nd tabulate statistical information that may be useful in dedsion making, these activities are not properly termed research.

3. chJsiaseci jtpon-ibservabk jjce ore.mpiricaLevi- dence.

Certain interesting questions do not lend themselves to research procedures because they cannot be observed.

Researcrj_ectieYe1iOfl and dogma as methods of establishing knowledge and accepts only what can be verified by observation.

4.
4.

Research demands accurate observation and de rign.

Re- devices when pos- sible.

When this is not possible or appropriate to answer the researchers' question, they may choose from a variety of qualitative, or nonquantita- five, descriptions of their observations.

Good research utilizes valid and reliable data gathering procedures.

5. re arc.

my ivesgring new data frompzary or first-hd sources or using existing d3ta4Q.
a new_jpse Teachers frequently assign a sá1ied reearch project thIf Involves writing a paper dealing with the life of a prominent person.

The students are expected to read a number of encyclopedias, books, or periodical references and to synthesize the information in a written report.

This is not research, for the data are not new.

Merely reorganizing or restating what is alreadyknown and has already been written, valuable as it may be as a learning experience, is not research.
It adds nothing to what is known.

6.

Although research activity mayttimes be some hat random and unsy eIiitI iimoreoften characterized by carefully desed pures that pply ip analysis Although trial and error are often fi'olved, research is rarely a blind, shotgun investigation or an experiment just to see what happens.

7.

Research re uires ex ertise.

The researcher knows what is al- ready known a out the problem an how others have investigated it.
He or she has searched the related literature carefully and is also thoroughly grounded in the terminology, concepts, and technical skills necessary to understand and analyze the data gathered.

8. search strives to be objeciveand logical, applying eve empIoyedThe data co1Iët the sible test t vJidate th The researcher attempts to elinulnate personal bias.

There is no attempt to persuade or to prove an emotionallyheld convic- tion.

The emphasis is on testing rather than on proving the hypothesis.
Although absolute objectivity is as elusive as pure righteousness, the researcher tries to suppress bias and emotion in his or her analysis.

9.

.Research involves the quest for answersp unsolvepiQklemS.

Pushing back the frntters of ignoraflC Is its goTandhgmahtY is frequently the quality of a goodcearch project.
However, previous im- portant studies are deliberately repeated, using identical or similar pro- 22 Part One Introduction to Educational Research cedures, with different subjects, different settings, and at a different time.

This process is replication, a fusion of the words repetition and duplication.

ReI,lication is always desirable to confirm or to raise questions.

about the conclusions of a previous study.

10.

Research is characterized by patient and unhurried activity.
It is rarely spediiTa, and researchers must expect disapointinerit and dis- couragement as they pursue the answers to difficult questions.

11. sear carfujiyxeco Each important term is defined, limiting factors are recogniied, procedures are described in detail, references are carefully documented, results are objectively re- corded, and conclusions are presented with scholarly caution and re- straint.
The written report and accompanying data are made available to the scrutiny of associates or other scholars.

Any competent scholar will have the information necessary to analyze, evaluate, and even replicate the study.

and 12.

Research The history of science re- veals tha i5Tiinportant discoveries were made in spite of the opposition of political and religious authorities.
The Polish scientistCopernicus (1473— 1543) was condemned by church authorities when he announced his con- clusion concerning the nature of the solar system.

His theory, in direct conflict with the older Ptolemaic theory, held that the sun, not the earth, was the center of the solar system.

Copernicus angered supporters of pre- vailing religious dogma, who viewed his theory as a denial of the story of creation as described in the book of Genesis.
Modern researchers in such fields as genetics, sexual behavior, and even business practices have aroused violent criticism from those whose personal convictions, experiences, or observations were in conflict with some of the research conclusions.

The rigorous standards of scientific research are apparent from an examination of these characteristics.

The research worker should be a scholarly, imaginative person of the highest integrity, who is willing to spend long hours painstakingly seeking truth.
However, it must be rec- ognized that researchers are human beings.

The ideals that have been listed are probably never completely realized.

Like righteousness, they are goals to strive for and are not all achieved by every researcher.

Many people have a superficial concept of research, picturing re- search workers as strange introverted individuals who, shunning the com- pany of their fellows, find refuge in their laboratory.
There, surrounded by test tubes, retorts, beakers, and other gadgets, they carry on their mysterious activities.

In reality the picture is quite different.

Research is not all mysterious, and it is carried on by thousands of quite normal individuals, more often in teams than alone, very often in the factory, the school, or the community, as well as in the laboratory.
Its importance is attested to by the tremendous amounts of time, manpower, and money spent on research by industry, universities, government agencies, and the Chapter 1 The Meaning of Research 23 professions.

The key to the cultural development of the Western world has been research, the reduction of areas of ignbrance by discovering new truths, which in turn lead to better predictions, better ways of doing things, and new and better products.
We recognize the fruits of research: better consumer products, better ways of preventing and treating disease, better ways of understanding the behavior of individuals and groups, and a better understanding of the world in which we live.

In the field of education, we identify research with a better understanding of the indi- vidual and a better understanding of the teaching—learning process and the conditions under which it is most successfully carried on.
Purposes of Research Fundamental or Basic Research To this point we have described research in its more formal aspects.

Research has drawn its pattern and spirit from the physical sciences and has represented a rigorous, structured type of analysis.

We have presented the, goal of research as the development of theories by the discovery of broad generalizations or principles.

We have employed careful sampling procedures in order to extend the findings beyond the group or situation studied.
So far, our discussion has shown little concern for the application of the findings to actual problems in areas considered to be the concern of people other than the investigator.

Such an approach, which often leads to knowledge for knowledge's sake, is the approach of basic or fundamental research.

Fundamental research is usually carried on in a laboratory or other sterile environment, sometimes with animals.
This type of research, which has no immediate or planned application, may later result in further research of an applied nature.

Applied Research Applied research has most of the characteristics of fundamental research, including the use of sampling techniques and the subsequent inferences about the target population.

However, its purpose is improving a prod- uct or a process—testing theoretical concepts in aqal problem situa- tions.
Most educational research is a lied resear ,for it attempts to_ genera izations about teaching—leamin processes and insthc-.

tional materials.

Fundamental research in the behavioral sciences may be concerned with the development and testing of theories of behavor.

Educational research is concerned with the development and testing of theories of how students behave in an educational setting.
24 Part One Introduction to Educational Research Action Research Since the late 1930s the fields of social psychology and education have shown great interest in what has been called action research.

In education this movement has had as its goal the involvement of both research specialist and classroom teacher in the study and application of research to educational problems in a particular classroom setting.
Action research is focused on immediate application, not on the development of theory or on general application.

It has placed its emphasis on a problem here and now in a local setting.

Its findings are to be evaluated in terms of local applicability, not universal validity.
Its purpose is to improve school practices and, at the same time, to improve those who try to improve the practices: to combine the research processes, habits of thinking, ability to work harmoniously with others, and professional spirit.

If most classroom teachers are to be involved in research activity, it will probably be in the area of action research.

Modest studies may be made for the purpose of trying to improve local classroom practices.
It is not likely that many teachers will have the time, resources, or technical background to engage in the more formal aspects of research activity.

Fundamental research must continue to make its essential contribution to behavioral theory, and applied research to the improvement of edu- cational practices.
These activities, however, will be primarily the function of research specialists, many of them subsidized by universities, private and government agencies, professional associations, and philanthropic foundations.

- Many observers have deprecated action research as nothing more than the application of common sense or good management.
But whether or not it is worthy of the term research, it does apply scientific thinking, and methods to real-life problems and represents a great improvement over teachers' subjective judgments and decisions based upon folklore and limited personal experiences.

In concluding this discussion, it is important to realize that research may be carried on at various levels of complexity.

Respectable research studies may be the simple descriptive fact-finding variety that lead to useful generalizations.
Actually, many of the early studies in the behav- ioral sciences were useful in providing needed generalizations about the behavior or characteristics of individuals and groups.

Subsequent exper- imental studies of a more complex nature needed this groundwork infor- mation to suggest hypotheses for more precise analysis.
For example, descriptive studies of the intellectually gifted, carried on since the early 1920s by the late Lewis M. Terman and his associates, have provided useful generalizations about the characteristics of this segment of the school population.

Although these studies did not explain the factors Chapter 1 The Meaning of Research 25 underlying giftedness, they did provide many hypotheses to be investi- gated by more sophisticated experimental methods.
Assessment, Evaluation, and Descriptive Research The term descriptive research has often been used incorrectly to describe three types of investigation that are basically different.

Perhaps their su- perficial similarities have obscured their differences.

Each of them employs the process of disciplined inquiry through the gathering and analysis of empirical data and each attempts to develop knowledge.

To be done competently, each requires the expertise of the careful and systematic investigator.
A brief explanation may serve to put each one in proper perspective.

Assessment is a fact-finding activity that describes conditions that exist at a particular time.

No hypotheses are proposed or tested, no vari- able relationships are examined, and no.

recommendations for action are suggested.

The national census is a massive assessment type of investigation conducted by the Bureau of the Census, a division of the United States Department of Commerce.
Every 10 bears an enumeration of the popu- lation is conducted, with data classified by nationality, citizenship, age, sex, race, marital status, educational level, regional and community res- idence, employment, economic status, births, deaths, and other charac- teristics.

These data provide a valuable basis for social analysis and gov- ernment action.

Ineducation, assessment may be concerned with the determination of progress that students have made toward educational goals.
The Na- tional Assessment of Educational Progress (NAEP), originally known as the Committee on Assessment of the Progress of Education, has been financed by the National Center for Educational Statistics.
Since 1969 a nationwide testing program has been conducted in such fields as science, mathemat- ics, literature, reading, and social studies, in four age groupings, in various geographical areas of the country, in communities of various sizes, and in particular states, and has reported interesting evidence of the degree to which learning goals have or have riot been realized.
Evaluation is concerned with the application of its findings and im- plies some judgment of the effectiveness, social utility, or desirabffity of a product, process, or program in terms of carefully defined and agreed- upon objectives or values.

It may involve recommendations for action.

It is not concerned with generalizations that may be extended to other set- tings.
In education, it may seek answers to such questions as: How well is the science program developing the competencies that have been agreed upon by the faculty curriculum committee?

Should the program in vo- 26 Part One Introduction to Educational Research cational agriculture education be dropped?

Are the library facilities ade- quate?

Should the reading textbook series currently in use be retained?
Descriptive research, unlike assessment and evaluation, is concerned with all of the following: hypothesis formulation and testing, the analysis of the relationships between nonmanipulated variables, and the devel- opment of generalization.

It is this last characteristic that most distin- guishes descriptive research from assessment and evaluation.
While as- sessment and evaluation studies may include other characteristics of descriptive research, only descriptive research, of the three, has gener- alization as its goal.

Unlike the experimental method, in which variables are deliberately arranged and manipulated through the intervention of the researcher, in descriptive research variables that exist or have already occurred are selected and observed.
This process is described as ex post facto, explanatory observational, or causal-comparative research in Chapter 5.

Both descriptive and experimental methods employ careful sampling pro- cedures so that generalizations may be extended to other individuals, groups, times, or settings.

Types of Educational Research Any attempt to classify types of educational research poses a difficult problem.
The fact that practically every textbook suggests a different sys- tem of classification provides convincing evidence that there is no gen- erally accepted scheme.

To systematize a method of presentation, however, some pattern is desirable.

At the risk of seeming arbitrary, and with a recognition of the danger of oversimplification, we suggest a framework that might clarify understanding of basic principles of research methodology.
It should be noted that the system of classification is not important in itself but only has value in making the analysis of research processes more comprehensible.

Actually, all research involves the elements of observation, descrip- tion, and the analysis of what happens under certain circumstances.

A rather simple four-point analysis may be used to classify educational re- search.

Practically all studies fall under one, or a combination, of these types.

1.

Historical research describes what was.


The process involves investi- gating, recording, analyzing, and interpreting the events of the past for the purpose of discovering generalizations that are helpful in understanding the past and the present, and, to a limited extent, in anticipating the future.

2.

Quantitative descriptive research uses quantitative methods to describe what is, describing, recording, analyzing, and interpreting conditions that exist.
It involves some type of comparison or contrast and at- Chapter 1 The Meaning of Research 27 tempts to discover relationships between existing nonmanipulated variables.

Some form of statistical analysis is used to describe the results of the study.

3.

Qualitative descriptive research uses nonquantitative methods to de- scribe what is.

Qualitative descriptive research uses systematic pro- cedures -to discover nonquantifiable relationships between existing variables.

4.
4.

Experimental research describes what will be when certain variables are carefully controlled or manipulated.

The focus is on variable rela- tionships.

As defined here, deliberate manipulation is always a part of the experimental method.

A complete chapter is devoted to each of these types of research, to techniques of data gathering, to areas of application, and to methods of analysis.
Summary Human beings' desire to know more about their world has led them from primitive superstition to modem scientific knowledge.

From mysticism, dogma, and the limi- tations of unsystematic observation based upon personal experience, they have ex- amined the process of thinking itself to develop the method of deductive—inductive thinking, which has become the foundation of scientific method.
Although first applied as a method of the physical sciences, the process of scientific inquiry has also become the prevailing method of the behavioral sciences.

There is no single scientific method, for scientists carry on their investigations in a number of ways.

However, accuracy of observation and the qualities of imagi- nation, creativity, objectivity, and patience are some of the common ingredients of all scientific methods.
The hypothesis is an essential research device that gyes a focus to the in- r research- vestigation and permits rese !ct!QrJQIe8CbPrQbab'YcQ!f!9fl ers formulate an affirmative research hypothesis at the outset of their project, they restate the hypothesis in negathe or null form for the purposes of statistical analysis of their observations.

This procedure facilitates inferring population characteristics from observed variable relationships as they relate to the error inherent in the sampling process.
Sampling, a deliberate rather than haphazard method of selecting subjects for observation, enables the scientist to infer conclusions about a population of Interest from the observed characteristics of a relatively small number of cases.

Simple ran- dom, systematic, stratified random, area or cluster, and available (nonprobability) samples have been described.

Methods of determining the size of an appropriate sample are suggested and the sources of sample bias are considered.
Research has been dfined as the systematic and objective analysis and recording of con trailed observations that may lead to the development of genera!- izations, principles, or theories, resulting in prediction and possibly ultimate control of events.

The chracteristiCS of research that may help to clarify its spirit and meaning have been presented.
28 Part One Introduction to Educational Research Fundamental or basic research is the formal and systematic process of deductive—jnductive analysis, leading to the development of theories.

Applied re search adapts the theories, developed through fundamental research, to the solution of problems.
Action research, which may fail to attain the rigorous qualities of fun- damental and applied research, attempts to apply the spirit of scientific method to the solution of problems in a particular setting, without any assumptions about the general application of findings beyond the situation studied.

In this chapter we have established assessment, evaluation, and descriptive research as three distinct types of investigation, and we have classified research as historical, descriptive, or experimental.
Remember that research is essentially an intellectual and creative activity.

The mastery of techniques and processes does not confer research competence, though these skills may help the creative problem-solver to reach his or her objectives more efficiently.

Exercises 1.

Construct two syllogisms: a. one that is sound b. one that is faulty.

Indicate the nature of the fallacy.

2.

Illustrate the application of Dewey's steps in problem solving.
Choose one of the problems listed, or one of your own: a. brown patches on your lawn b. your car won't start when you leave for home C. getting an economical buy on canned peaches d. most of the members of your class failed an examination 3.

Give an example of: a. a pure research problem b. an applied research problem c. an action researcit problem 4.

To what extent have religious institutions resisted the claims of science?

5.
5.

Is there necessarily a conflict between the disciplines of the sciences and the humanities?

6.

Explain why you agree or disagree with the following statements: a.

Excessive effort is spent on the development of theories, because they don't usually work in real situations.

b.

Science is more properly thought of as a method of problem solving than as a field of knowledge.

c. Applied research is more important than pure research in contributing to human welfare.

7.
7.

How woUld you select a sample of 40 college students for a morale study from a freshman class of 320?

Chapter 1 The Meaning of Research 8.

From a metropolitan school dIstrict staff directory, you wish to select a sample of 300 teachers from a listing of 3800.

Discuss several ways that the sample could be selected, considering the issues that may be involved.

9.
9.

What are the distinctive characteristics of descriptive research as con- trasted with: a. assessment b. evaluation c. experimental research 10.

How is the term research sometimes misused in classroom assignments and television interviews?

References DEWEY, J.

(1938).

Logic: The theory of inquiry.

New York: Holt, Rinehart & Winston.

FISHER, R. A.

& YATES, F. (1963).

Statistical tables for biological, agricultural, and medical research.

Edinburgh: Oliver & Boyd.

KAHN, J. V. (1984).


Cognitive training and its relationship to the language of profoundly retarded children.

In J. M. Berg (Ed.

), Perspectives and progress in mental retardation.

Baltimore: University Park Press.

KAHN, J. V. (1985).

Evidence of the similar-structure hypothesis controlling for organicity.

American Journal of Mental Deficiency, 89, 372—378.

KAHN, J. V. (1987).

Uses of scales of psychological development with mentally retarded populations.
In I. C. Uzgiris & J. McV.

Hunt (Eds.

), Research with scales of psychological development in infancy.

Champaign, IL: University of fllin.s Press.

KMiI, C. (1973).

Piaget's interactionism and the process of teaching young chil- dren.

In M. Schwebel & J. Raph (Eds.

), Piaget in the classroom.

New York: Basic Books.

KARPLUS, R., LAWSON, A. E., WOLMAN, W., APPEL, M., BERNOFF, R. HOWE, A, RUSCH, J. J., & SULLIVAN, F. (1977).

Science teaching and the development of reasoning.


Berkeley: Lawrence Hall of Science, University of California.

LAVATELLI, C. S. (1973).

Teacher's guide: Early childhood curriculum.

New York: Amer- ican Science and Engineering Co. MEES, C. E. K. (1934).

Scientific thought and social reconstruction.

Amencan Sci- entist, 22, 13—14.

PIAGET, J.

(1970).

Science of education and the psychology of the child.

New York: Viking Press.

Rand Corporation (1965).


A million random digits with 100,000 normal deviates.

New York: Free Press.

SNOW, R. E. (1973).

Theory construction for research on teaching.

In R. M. W. Travers (Ed.

), Second handbook of research on teaching.

Chicago: Rand McNally.

STAyER, J. R. & GABEL, D. L. (1979).

The development and construct validation of a group-administered test of formal thought.

Journal of Research in Science Teaching, 16, 535—544.

WOLINSKY, G. F. (1970).


Piaget's theory of perception: Insights for educational practices with children who have perceptual difficulties.

In I. J. Athey, & D. 0.

Rubadeau (Eds.

), Educational implications of Piaget's theory.

Waltham, MA: Ginn-Blaisdell.

30 Part One Introduction to Educational Research Additional Readings BARZUN, J.

(1964).

Science: The glorious entertainment.

New York: Harper & Row.

CONANT, J.

B.

(1951).

Science and common sense.

New Haven: Yale University Press.

CONANT, J.


B.

(1964).

Two modes of thought.

New York: Simon & Schuster.

DEWEY, J.

(1933).

How we think.

Boston: Raytheon Education Co. EBEL, R. (1967).

Some limitations of basic research in education.

Phi Delta Kappan, 49, 81—84.

FEIGL, H., & BROADNECK, M. (1953).

Readings in the philosophy of science.

New York: Appleton-Century-Crofts.

Fit.r.iic G. (1961).

The validation of scientific theories.

New York: Coffier Books.

HAYs, W. L. (1967).


Quantification in psychology.

Monterey, CA: Brooks/Cole Pub- lishing Co. HELMSTADTER, G. C. (1970).

Research concepts in human behavior.

New York: Appleton- Century-Crofts.

JONES, W. T. (1965).

The sciences and the humanities: Conflict and reconciliation.

Berke- ley: University of California Press.

K.ki'LAI.J, A.

(1964).

The conduct of inquiry.

San Francisco: Chandler Publishing Co. Kuiu.i, T. S. (1970).
The structure of scientific revolutions (2nd ed.).

Chicago: University of Chicago Press.

REAGAN, M. D. (1967).

Basic and applied research: A meaningful distinction?

Science, 155, 1383—1386.

SKINNER, B. F. (1948).

Walden two.

New York: Macmillan Co. SL4vmJ, R. E. (1978).

Basic vs. applied research: A response.

Educational Researcher, 7, 15—17.

SNOW, C. P. (1959).

Two cultures and the scientific revolution.

New York: Cambridge University Press.


2 Selecting a Problem and Preparing a Research Proposal ONE OF THE MOST DIFFICULT PHASES of the graduate research project is the choice of a suitable problem.

Beginners are likely to select a problem that is much too broad in scope.

This may be due to their lack of understanding of the nature of research and systematic problem-solving activity.

It may also be due to their enthusiastic but naive desire to solve an important problem quickly and immediately.
Those who are more experienced know that research is often tedious, painfully slow, and rarely spectacular.

They realize that the search for truth and the solution of important problems takes a great deal of time and energy and the intensive application of logical thinking.

Research makes its contribution to human welfare by countless small additions to knowledge.

The researcher has some of the characteristics of the ant, which brings its single grain of sand to the anthifi.
Before considering the ways in which problems may be identified, we should discuss a few of the characteristics of research and the activities of the researcher.

Research is more often a team endeavor than an indi- vidual activity.

Researchers working in groups attack problems in different ways, pooling their knowledge and ideas and sharing the results of their efforts.

Highly publicized discoveries usually result from the cumulative efforts of many, working as teams over long periods of time.
They are rarely the product of a single individual working in isolation.

Great discoveries rarely happen by accident.

When they do, the researcher is usually well-grounded and possesses the ability, known as serendipity, to recognize the significance of these fortunate occurrences.

31 32 Part One Introduction to Educational Research He or she is imaginative enough to seize the opportunity presented and to carry it through to a fruitful conclusion.

Pasteur observed that chance favors the prepared mind.
Researchers are specialists rather than generalists.

They employ the principle of the rifle rather than the shotgun, analyzing limited aspects of broad problems.

Critics have complained that much social research con- sists of learning more and more about less and less until the researcher knows everything about nothing.

This is a clever statement but an ex- aggeration.


The opposite statement, equally clever and exaggerated, char- acterizes much ineffective problem solving: learning less and less about more and more until one knows nothing about everything.

There is a danger, however, that research activity may focus upon such fragmentary aspects of a problem that it has little relevance to the formulation of a general theory.
An analysis of the relationship among a few isolated factors in a complex situation may seem attractive as a re- search project, but it will make little or no contribution to a body of knowledge.

Research is more than compiling, counting, and tabulating data.

It involves deducing the consequences of hypotheses through careful observation and the application of rigorous logic.

It is sometimes important to discover that a generalization is probably not true.
Beginning researchers frequently associate this type of conclusion with a sense of personal failure, for they become emotionally committed to their hypotheses.

Research, however, is a process of testing rather than proving, and it implies an objectivity that lets the data lead where they will.

The Academic Research Problem Academic research projects have been subjected to much criticism, both by the academic community and by the general public.
The academic research project is usually a requirement in partial fulfillment of the re- quirements of a graduate course or for an advanced degree.

The initial motivation may not be the desire to engage in research but the practical need f meeting a requirement.

Unfortunately, few such studies make a significant contribution to the development or refinement of knowledge or to the improvement of practice.
The lack of time, financial resources, experience, and expertise of the researcher, and the academic hazard of departing from a relatively safe, short-range project are, understandably, hindrances to significant contributions.

But these projects are often jus- tified on the grounds that once students develop some research compe- tency they will use their "know-how" to seek solutions to basic problems and will make a ontribution to the body of knowledge upon which sound practices are based.
In addition, we believe that in order to be a competent consumer of others' research, one must be capable of producing research of his or her own.

Chapter 2 Selecting a Problem and Preparing a Research Proposal 33 Few graduate students in education are full-time students; conse- quently they are often victims of the competing demands of teaching, supervising student activities, attending meetings, and participating in administrative activity.
Many are not on campus while they are writing their theses or dissertations, and they miss the continuing intellectual stimulation of the university faculty, discussions with fellow graduate students, the ready availability of library resources, and the opportunity of the full-time student to absorb the scholarly atmosphere of the uni- versity community.
Thus, most graduate students tend to select narrow, practical problems that are closely related to their school experience but rarely have great theoretical significance.

When master's degree or doctoral studies are carried on under the close supervision of an advisor or major professor who is devoting his or her energies to research on a significant problem, the student's thesis may make an important contribution.
The efforts of the degree candidate can then be directed toward certain restricted phases of the major problem, making possible long-term longitudinal studies.

Such studies as those by the late Lewis M. Terman at Stanford University of gifted children, fol- lowed over 50 years, represent the cumulative attack that is likely to yield more significant results than the uncoordinated investigations of candi- dates whose efforts lack this unifying direction and continuity.
Levels of Research Projects In the light of the varied types and purposes of students' projects, choice of a problem will depend upon the level at which the research is done.

A problem appropriate for a beginner in a first course in research is different from that selected for the more rigorous requirements of the master's thesis or the doctoral dissertation.

The first topic will necessarily be a modest one that can be carried on by an inexperienced researcher in a limited period of time.
The emphasis will be placed upon the learning process of the beginning researcher rather than on his or her actual con- tribution to education.

This statement does not imply that the product is unimportant.

It merely recognizes that, because of the limitations of the first research project, the emphasis is on learning how, with the hope that subsequent investigations will progressively yield more significant con- tributions to the advancement of knowledge.
Some students choose a first problem that can be expanded later into a more comprehensive treatment at the level of the master's thesis or the doctoral dissertation.

The first study thus serves as an exploratory process.

Sources of Problems The choice of a suitable problem is always difficult.

Few beginners possess real problem awareness, and even the more experienced researcher hes- 34 Part One Introduction to Educational Research itates at this step.
It is a serious responsibility to commit oneself to a problem that will inevitably require much time and energy and that is so academically significant.

What are the most likely sources to which one may go for a suitable research problem, or from which one may develop a sense of problem awareness?
Many of the problems confronted in the classroom, the school, or the community lend themselves to investigation, and they are perhaps more appropriate for the beginning researcher than are problems more remote from his or her own teaching experience.

What organizational or management procedures are employed?

How is learning material pre- snted?

To what extent does one method yield more effective results than another?

How do teachers feel about these procedures?


How do pupils and parents feel about them?

What out-of-school activities and influences seem to affect students and the teaching—learning process?

Teachers will discover "acres of diamonds" in their own backyards, and an inquisitive and imaginative mind may discover in one of these problem areas an interesting and worthwhile research project.
Technological changes and curricular developments are constantly bringing forth new problems and new opportunities for research.

Perhaps more than ever before, educational innovations are being advocated in class- room organization, in teaching materials and procedures, and in the appli- cation of technical devices and equipment.

Such innovations as computer- assisted instruction, teaching by television, programmed instruction, mod- ified alphabets, new subject matter concepts.
and approaches, flexible scheduling, and team teaching need to be carefully evaluated through the research process.

The graduate academic experience should stimulate the questioning attitude toward prevailing practices and effectively promote problem awareness.
Classroom lectures, class discussions, seminar reports, and out-of-class exchanges of ideas with fellow students and professors will suggest many stimulating problems to be solved Students who are for- tunate enough to have graduate assistantships have a special opportunity to profit from the stimulation of close professional relationships with faculty members and fellow assistants.
Reading assignments in textbooks, special assignments, research re- ports, and term papers will suggest additional areas of nded research.

Research articles often suggest techniques and procedures for the attack on other problems.

A critical evaluation may reveal faults or defects that made published findings inconclusive or misleading.

Many research ar- tides suggest problems for further investigation that may prove fruitful.
Consultation with the course instructor, advisor, or major professor is helpful.

Although the student should not expect research problems to be assigned, consultation with a faculty member is desirable.

Most stu- dents feel insecure as they approach the choice of a research problem.

Chapter 2 Selecting a Problem and Prepañng a Research Proposal They wonder if the problem they may have in mind is significant enough, feasible, and reasonably free of unknown hazards.
To expectthe beginner to arrive at the advisor's office with a completely acceptable problem is quite unrealistic.

One of the most important functions of the research advisor is to help students clarify their thinking, achieve a sense of foc'us, and develop a manageable problem from one that may be too vague and complex.

The following list may suggest areas from which research problems may be further defined.

1.
1.

Programmed instruction; scrambled texts; teaching machines; com- puter-assisted instruction 2.

Television instruction; closed-circuit TV 3.

Modified alphabets: Unifon, Initial Teaching Alphabet 4.

Flexible scheduling 5.

Team teaching 6.

Evaluation of learning; reporting to parents 7.

Student regulation/control 8.

Learning styles 9.

Evaluation of learning; practices and philosophies 10.

Homework polides and practices 11.

Field trips 12.
Field trips 12.

School buildings and facilities; lighting; space; safety 13.

Extracurricular programs 14.

Student out-of-school activities: employment; recreation;cultural ac- tivity; reading; television viewing 15.

Teacher out-of-school activities: employment; political activity; recreation 16.

The open classroom 17.

Linguistics 18.

New approaches to biology/chemistry/Physics 19.

Language laboratories: foreign languages; reading 20.

Multiple textbooks 21.

Independent study programs 22.


Advanced placement program 23.

Audiovisual programs 24.

Sodometry 25.

Health services 36.

Guidance-counseling programs 27.

Teacher morale: annoyances and satisfactions .

Teacher welfare: salaries; merit rating; retirement; tenure 29.

Educational organizations: local, state, and national; NEA; AFF 30.

Inner-dty schools; the culturally deprived; Head Start; Upward Bound; tutoring 36 Part Orie Introduction to Educational Research 31.
Preservice education of teachers: student teaching 32.

Teacher attitudes on a variety of issues, e.g., mainstreaming 33.

In-serVice programs 34.

Racial integration: student; teacher 35.

Parochial/private school problems; tax credits 36.

Follow-up of graduates; early school leavers 37.

Religion and education: released timeprograms; dismissed time; shared time 38.

Non-school-sponsored social organizations or clubs 39.

School district reorganization 40.


Community pressures on the school: academic freedom; controversial issues 41.

Legal liability of teachers 42.

Cadet teaching; teacher recruitment 43.

Teaching internship 44.

Sex education 45.

Ability grouping: acceleration; retardation/promotion 46.

Special education: speech therapy; clinical services; social services 47.

Problems in higher education: selection; prediction Of success; grad- uate programs 48.

Work—study programs 49.


Attribution of success and failure 50.

Comparison of the effectiveness of two teaching methods/procedures 51.

Self-image analysis 52.

Vocational objectives of students 53.

History of an institution, program, or organization 54.

Factors associated with the selection of teaching/nursing/social work as a career 55.

Case studies 56.

Socioeconomic status and academic achievement 57.

Perceptions of administrative leadership 58.

The effect of stress on academic achievement 59.


Minimal competency tests for promotion and/or graduation 60.

Merit pay for teachers For those students who are not teachers, some of the problem areas listed may be appropriate in social agency, hospital, or industrial situations.

Keep in mind that the above list includes general topics that need a great deal of refinement in order to become a researchable problem.
The student will usually need the help of a faculty member in gradually re- fining the general topic into a useful statement of a research problem.

In order to take a general topic or problem, such as thosejust listed, and refine it into a researchable problem, the individual needs to define certain components of the problem: the population of interest, the situ- ation, what part of the issue is to be addressed in the first (or next) study, and so forth.
Chapter 2 Selecting a Problem and Preparing a Research Proposal 37 For example, number 49 deals with the issue of attribution of success and failure.

To make this a researchable problem requires a good deal of narrowing and refinement.
One researchable problem that can be derived from this broad topic (using the approach referred to in the previous paragraph) would ask the question, Will college freshmen who are inter- nally focused (those who attribute their successes and failures to them- selves) do better in their first year of college than those who are externally focused (those who attribute their successes and failures to external fac- tors)?
Another equally plausible research question from this same topic would be, Do learning-disabled adolescents 4iffer from nondisabled ad- olescents on a measure of attribution?

As can be seen, a large number of researchable problems can be derived from this topic.

Only by narrowing the focus (e.g., population, situation, measurenents, etc.)

can a research- able problem be derived.


Once the scope of the topic or problem has been narrowed to make it a potentially researchable problem, we can then determine its impor- tance and feasibility.

Evaluating the Problem Before the proposed research problem can be considered appropriate, several searching questions should be raised.

Only when those questions are answered in the affirmative can the problem be considered a good one.

1.
1.

Is this the type of problem that can be effectively solved through the process of research?

Can relevant data be gathered to test the theory or find the answer to the question under consideration?

2.

Is the problem significant?

Is an important principle involved?

Would the solution make any difference as far as educational theory or practic is concerned?

If not, there are undoubtedly more significant prob- lems waiting fóbeiñvestigated.

3.

Is the problem a new one?

Is the answer.


already available?

Ig- norance of prior studies may lead a student to spend time needlessly on a'problem already investigated by some other worker.

However, although novelty or originality is an important consideration, the fact that a problem has been investigated in the past does not mean that it is no longer worthy of study.
There are times when it is appropriate to replicate (repeat) a study to verify its conclusions or to extend the validity of its findings to a different situation or population.

For instance, research with nonhand- icapped children might be of great importance to replicate with mentally retarded children.

Similarly, much cross-cultural research consists of rep- licating research conducted in one country with samples in another coun- try.
Kohlberg's (1969) theory of moral reasoning has been shown to be valid in a number of countries, thereby supporting the universality of the theory.

38 Part One Introduction to Educational Research 4.

Is research on the problem feasible?

After a research project has been evaluated, there remains the problem of suitability for a particular researcher.

The student should ask: Although the problem may be a good one, is it a good problem for me?

Will I be able to carry it through to a successful conclusion?
Some of the questions the students should consider are the following: a.

Am I competent to plan and carry out a study of this type?

Do I know enough about this field to understand its significant aspects and to interpret my findings?

Am I skillful enough to develop, administer, and interpret the necessary data-gathering devices and procedures?

Am I well grounded in the necessary knowledge of research design and statistical procedures?

b.

Are pertinent data accessible?


Are valid and reliable data- gathering devices and procedures available?

Will school au- thorities permit me to contact the students, conduct necessary experiments or administer necessary tests, interview teachers, or have access to important cumulative records?

Wifi I be able to get the sponsorship necessary to open doors that otherwise would be closed to me?

c. Will I have the necessary financial resources to carry on this study?
What will be the expense involved in data-gathering equipment, printing, test materials, travel, and clerical help?

If the project is an expensive one, what is the possibility of getting a grant from a philanthropic 4oundation or from such govern- mental agencies as the National Institute of Education?

d. Will I have enough time to complete the project?

Will there be time to devise the procedures, select the data-gathering devices, gather and analyze the data, and complete the research report?
Since most academic programs impose time limitations, certain worthwhile projects of a longitudinal type are precluded.

e. Will I have the courage and determination to pursue the study in spite of the difficulties and social hazards that may be in- volved?

Wifi I be willing to work aggressively when data are difficult to gather and when others are reluctant to cooperate?
Sex education, racial integration, and other controversial prob- lem areas, however, may not be appropriate for a beginning research project.

The Research Proposal The preparation of a research proposal is an important step in the research process.

Many institutions require that a proposal be submitted before any project is approved.
This provides a basis for the evaluation of the Chapter 2 Selecting a Problem and Preparing a Research Proposal 39 project and gives the advisor a basis for assistance during the period of his or her direction.

It also provides a systematic plan of procedure for the researcher to follow.

The proposal is comparable to the blueprint that the architect pre- pares before the bids are let and building commences.
The initial draft proposal is subject to modification in the light of the analysis by the student and his or her project advisor.

Because good research must be carefully planned and systematically carried out, procedures that are im- proviséd from step to step will not suffice.

-A worthwhileresearch project is likely to result only from a well-designed proposal.

The seven-part proposal format presented here should not be con- sidered the only satisfactory sequence.
In fact, the first five parts are often contained in two major components: the statement of the problem and its significance; and the review of the literature and hypotheses, which might also include definitions, assumptions, limitations, and deliniita- tions.

Many institutions and funding agencies suggest or require other formats for the research proposal.

Still, most proposal formats include the need for all of the information requested in the seven-part format pre- sented here.
When one is submitting a proposal for funding, it is wise to follow the format suggested by the funding source.

Part 1: The Statement of the Problem.

Thisis often a declarative statement but may be in question form.

Thisattempt to focus on a stated goal gives direction to the research process.

It must be limited enough in scope to make a defirüte conclusion possible.

The major statement may be followed by minor statements.
The problem areas that previouslyhave been listed in this chapter are not statements of problems.

They are merely broad areas of concern from which problems may be selected.

A problem usually implies that a controversy or difference of opinion exists.

Problems can be derived from theory, prior research results, or personal observation and experience.
Frequently, problems are based upon a significant concern (e.g., a rate of illiteracy among adults that is unac- ceptable in modern society) and an insufficient knowledge base regarding what, to do about the concern.

Examples of problem statements are as follows: (1) Are children who have had kindergarten experience better prepared for first grade?

(2) Participation in high school competitive ath- letics may conflict with optimal academic performance.
(3) Racial segre- gation may have a damaging effect upon the self-image of minority group children.

(4) Knowledge of participation in an experiment may have a stimulating effect upon the reading achievement of participants.

These problem statements involve more than information gathering.

They sug- gest hypotheses and provide a focus for research activity.

Part2: The Significance of the Problem.


It is important that the researcher point out how the solution to the problem or the answer to the question 40 Part One Introduction to Educational Research can influence educational theory or practice.

That is, the researcher must demonstrate why it is worth the time, effort, and expense required to carry out the proposed research.
Careful formulation and presentation of the implications or possible applications of knowledge help to give the project an urgency, justifying its worth.

Failure to include this step in the proposal may well leave the re- searcher with a problem without significance__a search for data of little ultimate value.

Many of the tabulating or "social bookkeeping" research problems should be abandoned if they do not pass the critical test of significance.
Perhaps university library shelves would not groan with the weight of so many unread and forgotten dissertations if this criterion of significance had been rigorously applied.

With so many gaps in educa- tional theory, and so many areas of education practice in need of analysis, there is little justification for the expenditure of research effort on trivial or superficial investigations.

Part 3: Definitions, Assumptions, Limitations, and Delimitations.
It is im- portant to define all unusual terms that could be misinterpreted.

These definitions help to establish the frame of reference with which the re- searcher approaches the problem.

The variables to be considered should be defined in operational terms.

Such expressions as academic achieve- ment and intelligence are useful concepts, but they cannot be used as criteria unless they are defined as observable samples of behavior.
Aca- demic grades assigned by teachers or scores on standardized achievement tests are operational definitions of achievement.

A score on a standardized intelligence test is an operational definition of inteffigence.

Assumptions are statements of what the researcher believes to be facts but cannot verify.
A researcher may state the assumption that the pant observers in the çroom will establish ppprt with the students and will not have a reactive effect on the be- haviore Limitations are those conditions beyond the control __esearcier that may place restricjjQ clusions9fthe study_and their application to other situations.
Administrative policies that preclude using more than one class in an experiment, a data-gathering instrument that has not been validated, or the inability to randomly select and assign subjects to experimental and control groups are examples of limitations.

Delimitations are the boundaries of the study.

A study of attitudes toward racial minorities may be concerned only with middle-class, fifth- grade pupils, and conclusions are not to be extended beyond this popu- lation sampled.
Part 4: Review of Related Literature.

A summary of the writings of rec- ognized authorities and of previous research provides evidence that the researcher is familiar with what is already known and what is still un- Chapter 2 Selecting a Problem and Preparing a Research Proposal 41 known and untested.
Since effective research is based upon past knowl- edge, this step helps to eliminate the duplication of what has been done and provides useful hypotheses and helpful suggestions for significant investigation.

Citing studies that show substantial agreement and those that seem to present conflicting conclusions helps to sharpen and define understanding of existing knowledge in the problem area, provides a background for the research project, and makes the reader aware of the status of the issue.
Parading a long list of annotated studies relating to the problem is ineffective and inappropriate.

Only those studies that are plainly relevant, competently executed, and clearly reported should be included.

In searching related literature, the researcher should note certain important elements: 1.

Reports of studies of closely related problems that have been inves- 2.

Design of the study, including procedures employed and data- tigatéd gathering instruments used 3.
Populations that were sampled and sampling methods employed 4.

Variables that were defined 5.

Extraneous variables that could have affected the findings 6.

Faults that could have been avoided 7.

Recommendations for further research Capitalizing on the reviews of expert researchers can be fruitful in providing helpful ideas and suggestions.

Although review articles that summarize related studies are useful, they do not provide a satisfactory substitute for an independent search.
Even though the review of related literature is presented as step 4 in the finished research proposal, the search for related literature is one of the first steps in theresearch process.

It is a valuable guide to defining the problem, recognizing its significance, suggesting promising data-gathering devices, appropriate study design, and sources of data.

Part 5: The Hypothesis.

It is appropriate here to formulate a major hy- pothesis and possibly several minor hypotheses.
This approach further clarifies the nature of the problem and the logic underlying the investi- gation and gives direction to the data-gathering process.A good hypoth- esis has several basic characteristics: 1.

It should be reasonable.

2.

It should be consistent with known facts or theories.

3.

It should be stated in such a way that it can be tested and found to be probably true or probably false.

4.

It should be stated in the simplest possible terms.
42 Part One Introduction to Educational Research The research hypothesis is a tentative answer to a question.

It is an educated guess or hunch, generally based upon prior research and/or theory, to be subjected to the process of verification or disconfirmation.

The gathering of data and the loicaI analysis of data relationships provide a method of confirming or disconfirming the hypothesis by deducing its consequences.

It is important that the hypothesis be formulated before data are gathered.
Suppose that the researcher gathers some data and, on the basis of these, notes something that looks like the basis for an alternative hy- pothesis.

Since any particular set of observations may display an extreme distribution, using such observations to test the hypothesis would possibly lead to an unwarranted conclusion.

The formulation of the hypothesis in advance of the data-gathering process is necessary for an unbiased investigation.
It is not inappropriate to formulate additional hypotheses after data are collected, but they should be tested on the basis of new data, not on the old data that suggested them.

Part 6: Methods.

This part of the research proposal usually consists of three parts: subjects, procedures, and data analysis.

The subjects section details the population from which the researcher plans to select the sample.
Variables that are frequently included, depending on the type of project proposed, include: chronological age, grade level, socioeconomic status, sex, race, IQ (if other than average), mental age (if significantly different from chronological age), academic achievement level, and other pertinent attributes of the targeted population.

The number of subjects desired from the population and how they will be selected are also indicated in this section.
The reader should be able to understand exactly from where and how the subjects are to be selected.

The procedures section outlines the research plan.

It describes in detail what will be done, how it will be done, what data will be needed, and what data-gathering devices will be used (see Chapter 9).

The method of analyzing the data is described in detail in the third part of the methods section.
The information given in the data-analysis section should be specific and detailed enough to demonstrate to the reader exactly what is planned.

No details should be left open to question.

Part 7: Time Schedule.

Although this step may not be required by the study advisor, a time schedule should be prepared so that the researcher may budget his or her time and energy effectively.
Dividing the project into manageable parts and assigning dates for their completion helps to systematize the study and minimize the natural tendency tcprocrastinate.

Some phases of the project cannot be started until other phases have been completed.

Such parts of the final research report as the review of related literature can be completed and typed while waiting for the data- chapter 2 Selecting a Problem and Preparing a Research Proposal 43 gathering process.
If the project is complicated, a flow chart or time-task 'fft ib'flti chart maybe usehil In aesdi'tiing he sequence researchprojects usually involve critical time limitations and definite dead- lines for filing the completed report, the planning of procedures with definite date goals is most important.

From time to time the major pro- fessor or advisor may request a progress report.

This device also serves as a stimulus, helping the researcher to move systematically toward the goal of a completed project.
Ethics in Human Experimentation In planning a research project involving human subjects, it is important to consider the ethical guidelines designed to protect your subjeets7- In particular, medical and psychological experimentation using human sub- jects involves some element of risk, however minor, and raises questions about the ethics of the process.
Any set of rules or guidelines that attempts to define ethical limits for human experimentation raises controversy among members of the scientific community and other segments of society.

Too rigid controls may limit the effectiveness of research, possibly denying society the answers to many important questions.

On the other hand, without some restraints, experimental practices could cause serious injury and infringe upon human rights.
These issues go beyond courtesy or etiquette and concern the ap- propriate treatment of persons in a free society.

Some of these questions have been dealt with by scientists and philosophers, by enactments of legislative bodies, by codes of ethics and professional organizations, or by guidelines established by educational institutions.
In 1974, the Congress of the United States established the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research to formulate guidelines for the research activities of the National Institutes of Health and the National Institute of Mental Health.
The Commission's 4-year, monthly deliberations, supplemented by discussions held at the Smithsonian Institution's Belmont Conference Cen- ter, resulted in the publication of the Belmont report: Ethical principles and guidelines for the protection of human subjects of research (1979).

Universities have established human experiment review committees to advise academic investigators about appropriate procedures and to approve those studies that conform to their ethical guidelines.
Mostfund- ing agencies, private and governmental, require such a review prior to awarding of grants.

The university may have ad hoc committees concerned with a particular study or standing committees that deal with all experi- mental activities involving the institution or division.
In cases where there are serious risks that must be weighed against the potential benefits to 44 Part One Introduction to Educational Research society, reviews by both ad hoc and institution-wide committees may be deemed necessary.

Some faculty researchers have complained that review committees have unduly restricted their experimental activities.
It ispos- sible in some cases that particular members of the committee did not have the technical background to make sound judgments outside their own fields of competence.

Others have felt that, because the committee as- signment demanded so much of their time, they could not contribute their best effort.
However, because it is the primary function of the human experiment review committee to maintain the ethical standards of the institution and to supervise the ethical guidelines of the funding agencies, it serves a useful and necessary purpose.

In 1953, the American Psychological Association issued its first code of ethics for psychologists.

In 1963, the code was revised and its preamble contained the following statement: The psychologist believes in the dignity and worth of the individual human being.
He is committed to increasing man's knowledge of himself and others.

While pursuing this endeavor he protects the welfare ofany persons who may seek his services, or any subject, human or animal, that may be the object of his study.

He does not use his professional position or relationship, nor does he knowingly permit his own services to be used lnj others, for purposes inconsistent with these values.
While demanding for himself freedom of in- quiry and communication, he accepts the responsibility this freedom confers; for competence where he claims it, for objectivity in the report of his findings, and for the consideration of the best interests of his colleagues and of society.
(American Psychological Association, 1963, p. 2) In 1970, the Board of Directors appointed an Ad Hoc Committee on Ethical Standards in Psychological Research to bring the 1963 code up to date in light of changes in the science, in the profession, and in the broader social context in which psychologists practice.

The first draft of the com- mittee report was circulated among 18,000 members of the association.

About 5000responded with suggestions.


In addition, journal editors, staff members of research review committees, directors of research organiza- tions, writers on research ethics, and leaders in such special fields as hypnosis were interviewed.

These contributions were supplemented by discussions at regional and national meetings of the association.
Psy- chology departments of universities, hospitals, clinics, and government agencies, as well as anthropologists, economists, sociologists, lawyers, philosophers, and psychiatrists were consulted.

As a result of these conversations and correspondence with profes- sionals from all scholarly disciplines, a final draft was adopted and pub- lished in 1973..
In 1978, a Committee for the Protection of Human Subjects in Psychological Research was established and charged with making an- nual reviews and recommendations regarding the official APA position.

Chapter 2 Selecting a Problem and Preparing a Research Proposal 45 These annual reviews led to a revision, which went through a similar process of consultationkas the 1973 edition.

A final draft that incorporated various suggestions ws adopted and published in 1982.
Ten principles wre formulated that deal with the experimenter's responsibilities toward participants.

In the published report, Ethical Prin- ciples in the Conduct of 1esearch with Human Participants (American Psycho- logical Association, 1982), each principle is stated with discussion of issues, problems, and recommndations for appropriate action.

The meticulous care with which this code was developed attests to the concern of this professional organization for ethical practices in psychological research.
Readers who are interested in a more complete discussion on ethics in human experimentation are urged to read this report.

The following discussion, while not a summary of the American Psychological Association (APA) code of ethics, is consistent with the APA code.

The guidelines discussed here are not ethical absolutes.

Rather, they characterize writing in the field of ethics and a number of professional codes that the authors have examined.
The guidelines deal with the fol- lowing areas of concern: informed consent; invasion of privacy; confi- dentiality; protection from stress, harm, or danger; and knowledge of outcome.

In formed Consent.

Recruitment of volunteers for an experiment should always involve the subject's complete understanding of the procedures employed, the risks involved, and the demands that may be made upon participants.

Whenever possible, subjects should also beinformed of the purpose of the research.
When subjects are not competent to give informed consent due to age, illness, or disability, the informed consent of parents, guardians, or responsible agents must be secured.

This freedom to par- ticipate or to decline to participate is basic, and it includes the freedom to withdraw from an experiment at any time without penalty.

Coercion to participate or to remain as a participant must not be applied and any exploitation of participants is an unethical practice.
The following are examples of experiment recruitment practices that might raise ethical questions: 1.

Subjects who are inmates of penal institutions volunteer to participate in anticipation of more favorable treatment or recommendation of earlier parole.

2.

Medical students who need money are recruited for experiments by offers of financial reward.

3.
3.

Participants who do not have the mental capacity to give rational consent—persons who are mentally ill or mentally retarded, orthose with reduced capacity—are recruited in institutions or nursing homes.

4.

Members of a college class are required to participate in an experi- ment in order to meet a course requirement.

46 Part One Introduction to Educational Research Invasion of Privacy.
Ordinarily it is justifiable to observe and record be- havior that is essentially public, behavior that others normally would be in a position to observe.

It is an invasion of privacy to observe and record intimate behavior that the subject has reason to believe is private.

Con- cealed observers, cameras, microphones, or the use of private correspond- ence without the subject's knowledge and permission are invasions of privacy.
If these practices are to be employed, the researcher should explain the reasons and secure permission.

This statement is not to suggest that intimate behavior cannot be observed ethically.

The sexual behavior studies of Doctors Masters and Johnson are based upon observation and recording of the most intimate acts, but subjects volunteer to participate with full knowledge of the purposes and procedures employed.
The motivation is based upon con- fidence in the integrity of the researchers and the importance of their scientific contributions to human welfare.

Confidentiality.

The ethical researcher holds all information that he or she may gather about the subject in strict confidence, disguising the par- ticipant's identity in all records and reports.

No one should be in a position to threaten the subject's anonymity nor should any information be released without his or her permission.
Protection from Physical and Mental Stress, Harm, or Danger.

In using treatments that may have a temporary or permanent effect on the subjects, the researcher must take all precautions to protect their well-being.

Treat- ments are administered under the direction of competent professional practitioners in clinical or research facilities where effective and thorough precautions and safeguards may be assured.
Where some risk is una- voidable, the potential benefits may be sufficient to justify the research.

A balance needs to be achieved, with benefit outweighing risk, in such a case.

Knowledge of Outcome.

The participant has a right to receive an expla- nation for the reasons for the experimental procedures and the results of the investigation.
The researcher may explain the results and their sig- nificance orally, in writing, or by informing participants of the issue of the journal in which the report is published.

Ethical researchers not only observe these ethical guidelines but take complete responsibility for the actions of their coexperimenters, col- leagues, assistants, technical personnel, secretaries, and clerks involved in the project, constantly monitoring their research activities.
Researchers have obligations to their subjects, their professional colleagues, and the public.

They do not discard unfavorable data that would modify the inter- pretation of their investigation.

They make their data available to their Chapter 2 Selecting a Problem and Preparing a Research Proposal 47 professional peers so that they may verify the accuracy of the results.

They honor promises made to subjects as a consideration for their partic- ipation in a study.
They give appropriate credit to those who have aided them in their investigations, participated in the data analysis, or contrib- uted to the preparation of the research report.

They place scientific ob- jectivity above personal advantage and recognize their obligation to society for the advancement of knowledge.

Recently there have been reports in the news media of fraudulent behavior.
These news reports have related instances of fraud connected with research funded by the National Institutes of Health,.

which has its own investigators.

These instances of fraudulent behavior have resulted in a great deal of embarrassment for those involved, including a Nobel laureate who co-authored a paper that included fraudulent data (unbe- knownst to him), and a prison sentence in at least one case for misap- propriation of funds.
Some researchers have been known to justify deception, coercion, invasion of privacy, breach of confidentiality, or risks to subjects in the name of science, but one might suspect that the prestige, ambition, or ego of the experimenter was the primary motivation.

Using the Library The student should become thoroughly acquainted with the university library, the location of its varied facilities, and the services it provides.
In addition to the traditional card catalog, many• university libraries have computerized their holdings and have placed terminals in various locations for ease of finding books and periodicals.

Sometimes a student learns of a reference that is not available in the local library.
Most libraries belong to one of three major shared cataloging systems: Online Computer Library Center (OCLC) with the holdings of over 3000 libraries; Research Library Network; and, in the Pacific North- west, the Washington Library Network.

The list of books and periodicals available, and the libraries holding these materials, can be quickly accessed on a time-sharing computer system available in most libraries.
The stu- dent's library requests the books or a photocopy of the article, which is then loaned to the student by his or her library.

Finding Related Literature Students often waste time searching for references in an unsystematic way.

The search for references is an ever-expanding process, for- each reference may lead to a new list of sources.

Researchers may consider these sources as basic: 48 Part One Introduction to Educational Research 1.

The Education Index 2.

Resources in Education 3.


Current Index to Journals in Education 4.

Index to Docforal Dissertations and Dissertation Abstracts International 5.

Other specialized indexes or abstracts indicated by the area of in- vestigation (e.g., Psychological Abstracts) Appendix I lists indexes and abstracts that the student or researcher may use to find articles and books on his or her topic.
Many of these data bases, including Educational Research Information Centers (ERIC), Excep- tional Child Education Resources, Psychological Abstracts, and dozens of others can be accessed directly through one of the computer services available to libraries.

Almost all college and university libraries, and many public libraries, offer this service.

The investigator, with the help of a librarian, uses key words to let the computer system know which materials are desired.
For instance, if a researcher is reviewing the literature that has used Piagetian theory with mentally retarded persons, she or he might use the key words Piaget with mental retardation, mentally retarded, and retardation.

The computer then searches all the titles and abstracts for those containing both Piaget and one of the other key words.

The inves- tigator can then have the titles or titles and abstracts printed either "on- line" or, less expensively, overnight at the computer services facilities.
Considering the time that is saved by using a computer search facility, the cost is minimal.

Microfiche The development of microfiche has been one of the most significant con- tributions to library services by providing economy and convenience of storing and distribution of scholarly materials.

A microfiche is a sheet of film that contains microimages of printed materials.

Filmed at a reduction of 1 to 24 or higher, nearly one hundred 8" x 11" pages of copy can be reproduced on one 4" x 6" film card.
Microfiche readers that magnify the microimages to original or larger copy size are available at libraries.

Some microfiche readers provide up to 40 x magnification on screens as large as 15" x 21".

There are many document reproduction services that supply micro- fiche to libraries upon subscription or upon special order.
The Educational Resources Information Center (ERIC) has prepared for the National Institute of Education a directory of nearly 600 libraries that possess extensive collections or receive regular periodic shipments of ERIC microfiche collections.

These libraries receive approximately 17,000 microfiche per year.

Other document reproduction sources that provide microfiche to libraries or individuals are described later in this chapter.
Chapter 2 Selecting a Problem and Preparing a Research Proposal 49 Super- and Ultra-Microfiche Recent developments in the field of micro-printing will transform the process of storage, retrieval, and distribution of published materials in libraries of the future.

A super-microfiche has been developed that con- tains up to 1000 pages of printed material on a single 4" x 6" transparent card, the equivalent of two or more books.
An even more spectacular development is the ultra-microfiche that contains up to 3200 microdots on a single card.

When projected, each dot contains the equivalent of several pages.

Thus, seven to ten volumes could be contained on a single 4" X 6" transparent card.

Reader printers make hard copy printouts (8?'

x 11" reproductions) of any page in a few seconds.


Note Taking One of the most important research activities of the graduate student is note taking—putting materials in a form that can easily be recalled and used in the future.

Notes will result from speeches and lectures, class discussions, cOnversation, from solitary meditation, and from reading ref- erence materials.

In preparing term papers and research reports the notes that result from reading will be most significant.
Without a careful, sys- tematic method of note taking, much of what is read is quickly forgotten.

Reading-reference notes have been classified under four principal categories: 1.

Quotation.

The exact words of an author are reproduced, enclosed in quotation marks.

It is essential to copy each statement accurately, and to indicate the exact page reference so that the quotations may be properly referenced in the written report.

2.

Paraphrase.
2.

Paraphrase.

The reader restates the author's thoughts in his or her own words.

3.

Summary.

The reader states in condensed form the contents of the article.

4.

Evaluation.

The reader records his or her own reaction, indicating agreement or disagreement, or interpreting the point of view of the writer.

A single note card may include several of these types when it seems appropriate.

A Suggested Method for Taking Notes 1.

Skim the reference source before taking any notes.


A bird's-eye view is essential before one can decide what material to record and use.

Selecting the most significant material is a skill to be cultivated.

50 Part One Introduction to Educational Research 2.

Use 4" x 6" index cards.

They are easily sorted by subject headings and are large enough to include a reasonable amount of material.

Some students prefer 5" x 8" cards, which are less convenient to carry but provide more space for notes.

3.
3.

File each note card under a definite topic or heading.

Place the subject heading at the top of the card for convenient filing.

A complete bibliographic citation should be placed at the bottom of the note card.

If a book has been used, the call number should be indicated to facilitate library location in the future.

(See Fig.

2—1.)

4.

Include only one topic on a card.

This makes organization of notes flexible.


If the notes are lengthy, use consecutively numbered cards, and slip a rubber band around them before filing.

5.

Be sure that notes are complete and clearly understandable, for they are not likely to be used for some time after they have been taken.

6.

Distinguish clearly between a summary, a direct quotation of the au- thor, a reference to the author's source, and an evaluative statement.

7.

Do not plan to recopy or type your notes.


It wastes time and increases possibility of error and confusion.

Copy your notes carefully the first time.

ii7).

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Chapter 2 Selecting a Problem and Preparing a Research Proposal 51 8.

Keep a supply of note cards with you at all times, so that you can jot down ideas that come to you while waiting, riding the bus, or listening to a lecture or discussion.

9.

Be careful not to lose your notes.

As soon as they are copied, file them in a card index box.
If you must carry them with you, use the 4" x 6" or 5" x 8" accordion file folder, and be sure that your name and address are clearly printed on it.

10.

Keep a permanent file of your notes.

You may find the same notes useful in a number of courses or in writing a number of reports.

When taking notes, consider the advisability of making photostatic copies of book and journal pages so that they can be examined more efficiently at home.
Many book and journal materials are reproduced on microfiche cards that may include as many as 1000 pages on one 4" x 6" card.

The rapid trend toward microfilming and microfiching professional literature will continue as the constantly increasing volume of published materials burdens limited shelf space.

Coin-operated microfilm and mi- crofiche printers are found in most university libraries.

Reproduction is not expensive and the quality of the copy is excellent.
It is much more convenient to take notes from an 8k" x 11" print copy than from film projected on a screen.

References and Bibliography In preparing a journal report, paper, or research proposal the author is expected to include a list of the references that have been cited in the text.

Sometimes it is preferable to include additional materials that were used by the author but 'not actually cited in the paper.
In this case the author would provide a bibliography which includes all the relevant ref- erences, cited or not.

The most convenient way to assemble and organize references or a bibliography is by the use of bibliography cards.

The card includes the names of the authors, the facts of publication, and the annotation (see Chapter 3 for examples using the American Psychological Association system).
Placing the information on cards makes it easy to assemble the authors' names in the alphabetical order in which they are listed in the bibliography of the report.

(See Fig.

2—2.)

The First Research Project Experience has indicated that one way to understand the methodology and processes of research is to engage in research.
Such a project may be very modest in nature and necessarily limited by time, the experience of 52 Part One Introduction to Educational Research 7 L 9 )' - '' -'-"- /9_77 _3J'1Ae__ FIGURE 2—2 Bibliography Card.

the student, and many other factors associated with the graduate student's other obligations.

However, the methodology may be learned by actively engaging in the research process under the careful supervision of the instructor in the beginning course in research.
Respectable research proj- ects have been undertaken and reported on within a semester's time, even within an 8-week summer session.

Although most of these studies have been of the descriptive-survey type, some simple historical and experi- mental studies have also been completed.

The emphasis must necessarily be placed on the process rather than on the product or its contribution to the improvement of educational practice.
A study chosen for this first project, however, may not be of great enough significance to serve as an appropriate thesis problem.

The full-scale project may be either an individual or a group enter- prise.

Groups of three to five graduate students can profitably work to- gether on the planning of the study.

Data-gathering devices may be chosen or constructed through joint enterprise.
Data may be gathered within the university graduate class, or in the classrooms, schools, or communities in which the group's members teach.

However, it is recommended that the next steps—organization and analysis of data and the writing of the final report—be an individual project.

There is always the danger in a group project of "letting George do it," and incidentally letting George get all the benefit from the experience.
ThIs recommended combination of group effort in the initial stages and individual effort in the later stages represents a compromise that seems effective and enables students to carry through a study in a limited amount of time with reasonable opportunity for personal growth.

For some of those who will write a thesis in partial fulfillment of degree Chapter 2 Selecting a Problem and Preparing a Research Proposal 53 requirements, this first project may serve as preparation.
For others, it may initiate a study capable of subsequent expansion into a thesis or dissertation.

Many research-course instructors believe that a more practical re- quirement would be the preparation of a carefully designed research pro- posal rather than a limited-scope study.

There is much to be said for this point of view because the beginning research student is inexperienced, the time is short, and there is a real danger of conveying a superficial concept of sound research.
The following topics were selected by inexperienced student re- searchers who were carrying on a project or writing a proposal in partial - in educational re- fulfillment of the requirements of a beginning course search.

Most of the topics were short, action-type descriptive studies, not based upon random selection and random assignment of subjects or ob- servations.

Notice that the wording of the titles did not imply generali- zation of the conclusions to a wider population.
The primary purpose was a learning exercise, not a contribution to a field of knowledge.

Topics Used by Students in a Beginning Graduate Course in Educational Research 1.

The Attitudes of a Group of University Seniors toward Coeducational Dormitories 2 The Reading Skill Development of a Deaf First-grade Child 3.

The Status of Latin in Indiana High Schools 4.

Discipline Problems at Washington High School as Viewed by a Group of Seniors 5.
A Study of the Effectiveness of Chisanbop Calculation with a Group of Third-grade Pupils 6.

The Status of Music in the Western Yearly Meeting of the Society of Friends 7.

The Rehabffitation of a Group of Heroin Addicts in a Federally Funded Drug Treatment Center 8.

The Social Development of a 6-year-old Autistic Child 9.

The Effect of Trial Promotion on the Academic Achievement of a Group of Underachievers 10.
The Effects of Parent Visitation upon the Reading Performance of a Group of Fourth-grade Students 11.

The Predictive Value of Entrance Examinations at the Methodist School of Nursing High School 12.

The Interests of a 3-year-old Boy 13.

Student Participation in the Activity Program at Lawrence Central - 14.

A Comparison of Regular Classroom and Learning Disabled Chil- dren's Expressive Language Skills 54 Part One Introduction to Educational Research 15.
The Effect of Verbal Mediation upon the Mathematical Achievement of Learning Disabled Students 16.

The Effect of Teacher Education on Attitudes toward Mainstreaming 17.

Effect of Verbalization on Performance and On-Task Behavior of Reading Disabled Children 18.

Prevalence of Behavior Problems of Hearing Siblings of Deaf Children 19.

The Influence of Kindergarten Experience on the Subsequent Reading Achievement of a Group of Third-grade Pupils 20.
The Views of Selected Baptist Laymen, Ministers, and National Church Leaders Concerning Issues Relating to the Traditiàn of Separation of Church and State 21.

The Attitudes and Behavior of Freshmen and Seniors Regarding Classroom Dishonesty at Sheridan High School 22.

The Attitudes of a Group of Florida School Superintendents toward Mandated Minimum Competency Testing 23.

Authority Images of a Selected Group of Inner city Children 24.
The Achievement of Twins, Both Identical and Fraternal, in the Leb- anon, Indiana, Metropolitan School District 25.

A Follow-up Study of Nonpromoted Students at School #86 26.

A History of the Indiana Boy's School, Plainfield, Indiana 27.

A Comparative Analysis of the Self-Concepts of a Group of Gifted and Slow-learning Children 28.

The Attitudes of a Group of High School Seniors toward Nuclear Protest Movements 29.
The Educational Backgrounds of .129 American Celebrities Listed in the 1966 Current Biography Yearbook 30.

The Attitudes of a Group of Graduate Students toward Mandated Smoking Restrictions in Public Facilities 31.

The Influence of Entering Age upon the Subsequent Achievement.

at First-, Second-, and Third-grade Levels in Washington Township 32.

The Attitudes of a Selected Group of Black and White Parents toward Busing to Achieve Racial Integration 33.
A Study of Socioeconomic .Status in the Butler-Tarkington Area, a Racially Integrated Community 34.

A Follow-up Study of the 1970 Graduates of Grace Lutheran School 35.

The Effect of Title IX, Prohibiting Sex Discrimination in Public Schools, upon the Athletic Budgets of Illinois Public Colleges and Universities For experienced researchers, projects would necessarily be more theory-oriented, with conclusions generalized beyond the specific group observed.
At this more advanced level a careful process of randomization would be desirable, if not necessary, and the research design would be much more rigorous.
The details of some of the more sophisticated pro- cedures are partially explained in subsequent chapters of this text and in other relevant sources, particularly discussion of experimental and de- Chapter 2 Selecting a Problem and Preparing a Research Proposal 55 scriptive research processes, the selection or construction of data-gathering devices, and the statistical analysis of data.
Submitting a Research Proposal to a Funding Agency Experienced researchers may submit research proposals to foundations or government agencies to support their research.

These proposals, especially to government agencies, are usually written in response to the agency's Request for Proposals (RFP) on a specific topic (e.g., a national center on literacy) or in a general area (e.g., field-initiated research on populations with disabilities).
The RFP (or if there is no RFP, the funding guidelines provided by the agency or foundation) is usually quite specific in the goals and priorities for funding.

The RFP also provides guidelines for writing the proposal, including a suggested, format and sometimes a copy of the guidelines to be used in rating the proposal.

These review guidelines may include the number of points assigned to each component of the grant proposal and what the reviewers should be looking for in the proposal.
Usually the significance or the proposed research and the methpdology are the two most important components for being successful and receiving a grant.

Due to the highly competitive nature of funded research, it is critical that the proposal author respond carefully to the priorities of the funding agency.

In fact, it may be useful to discuss the proposal with a repre- sentative of the agency or foundation prior to dompleting and submitting it.
Agency personnel are surprisingly open and willing to provide sug- gestions.

In addition, a number of foundations will initially review only brief descriptions (one or two pages) of proposed research and then re- quest full proposals only from those descriptions that seem most prom- ising.

Government agencies in particular also will provide the author of an unsuccessful grant proposal with the reviewers' comments, which may lead to a greatly improved proposal for future submission.
The beginning researcher may find it useful to understand the type of detailed information that a government agency or foundation would expect td receive before committing funds.

The following is a list of suggestions for those who seek financial support: 1.

Write the proposal very carefully.

A carelessly written proposal sug- gests to the evaluators that the research project would be carelessly done.

It is also useful to follow the format recommended by the agency in writing the proposal.

2.
2.

Pay attention to stated goals and priorities of the foundation or agency.

It is important to point out how your study would be relevant to these goals.

56 Part One Introduction to Educational Research 3.

State your problem in such a way that the proposal evaluators, who are capable and experienced in judging research proposals but know nothing about your project, will be able to judge its worth and the likelthood of its contributing to a significant area of knowledge.

4.
4.

Indicate how your study will add to or refine present knowledge.

5.

State your hypothesis or hypotheses in both conceptual and opera- tional terms and in both substantive and null form.

6.

Indicate that you are completely familiar with the field of investi- gation and are aware of all recent studies in the problem area.

7.
7.

Indicate how you propose to test your hypotheses, describing your research design and the data-gathering instruments or procedures that you will use, indicating their known validity and reliability.

8.

Describe your sampling procedures, indicating how you will ran- domly select and randomly assign your subjects or observations.

9.

Indicate the extraneous variables that must be recognized and explain how you propose to minimize their influence.

10.
10.

Explain the statistical procedures that you will employ, indicating any computer application that you will use.

11.

Prepare a budget proposal estimating the funds required for a. wages, including any fringe benefits b. purchase or rental of special equipment or supplies c. travel expenses d. clerical expenses e. additional overhead expenses that may be involved f. publication costs 12.
Provide some tangible evidence of your competence by listing a. research projects that you have carried on or actively partici- pated in b. your scholarly journal articles, including abstracts of your studies c. your academic training and other qualifications Summary Academic research projects are usually required in partial fulfillment of the require- ments of a course or a degree program.

The motivation is not always a genuine desire to engage in research.
In addition, limitations of time, money, and experience usually preclude the consideration of problems that could make significant contri- butions to educational theory and practice.

The choice of a suitable problem is one of the most difficult tasks facing the beginning researcher.

Students tend to define problems that are too broad in scope or that deal with too fragmentary aspects of the problem.
Consultation with the course instructor or advisor is particularly helpful in identifying a problem that is manageable and significant enough to justify the time and effort that will be required.

Chapter 2 Selecting a Problem and Preparing a Research Proposal 57 Problems are found in the teachers' daily classroom, school, and community experiences.

Technological and social changes call for research evidence to chart new courses in educational practice.
Graduate academic experience helps to promote problem awareness through classroom activities, the reading of research studies, and interaction with instructors, advisors, and fellow students.

A good research problem has the qualities of significance, originality, and fea- sibility.
The researcher should evaluate a proposed problem in the light of his or her competence, the availability of data, the financial demands of the project, the limi- tations of time, and the possible difficulties and social hazards involved.

A research proposal is required by many institutions and services as a useful basis for the evaluation of a project as well as a guide for the researcher.
The proposal contains a clear and concise statement of the problem; the hypothesis or hypotheses involved; a recognition of the significance of the problem; definitions of important terms; assumptions, delimitations, and limitations; a review of related literature; an analysis of proposed research procedures; a reference list; and a time schedule.

Some advisors request a progress report from time to time to evaluate the progress of the investigation.
One way to learn about research is to conduct a study in connection with the beginning research course.

Another way is to write a research proposal which may involve all the steps in the research process except the gathering and analysis of data and the formulation of conclusions.
Either of these exercises gives a focus to the discussion about research and may help in developing some competence and the research point of view, It may even encourage some teachers to conduct modest studies in their own schools during or after the completion of their graduate programs.

Exercises 1.

The following research topics are faulty or are completely inappropriate.

Revise each one, if possible, so that it describes a feasible project or proposal for this course.

a.
a.

The Attitudes of Teachers toward Merit Rating b.. How to Teach Poetry Most Effectively c. The Best Way to Teach Spelling d. The Evils of Alcohol e. Does Ability Grouping Meet the Needs of Students?

f. The Adequacy of Law Enforcement g. The Hazards of Smoking h. Why the Discussion Method Is Better than the LectureMethod i.

The Fallacy of Evolution 2.

State a hypothesis, first in scientific or research form, and then in null or statistical form.

3.
3.

Define the following terms in operational form: a. intelligence b. creativity c. coordination 58 Part One Introduction to Educational Research d. authorization e. memory 4.

In a research study, is a hypothesis to be tested always preferable to a question to be remembered?

Why or why not?

5.

What are some of the more effective ways to- find a suitable research problem?

References American Psychological Association (1963).

Ethical standards of psychologists.

Wash- ington, D.C.: APA.


American Psychological Association (1973).

Ethical principles in the conduct of research with human participants.

Washington, D.C.: APA.

American Psychological Association (1982).

Ethical principles in the conduct of research with human participants.

Washington, D.C.: APA.

Belmont report: Ethical principles and guidelines for the protection of human subjects of research.

(1979).
(1979).

Washington, D.C.: Smithsonian Institution, Superintendent of Documents, U.S. Government Printing Office.

KOHLBERG, L. (1969).

Stage and sequence: The cognitive developmental approach to socialization.

In D. A. Goslin (Ed.

), Handbook of socialization theory and research.

Chicago: Rand McNally.

A Additional Readings A bibliography of publications about the educational resources information center.

(1978).

Washington, D.C.: National Institute of Education.

DIENER, E. & CRANDALL, R. (1978).


Ethics in social and behavioral research.

Chicago: University of Chicago Press.

ENNIS, R. (1964).

Operational definitions.

American Educational Research Journal, 1, 183—201.

Funding opportunities of the national institute of education.

(1979).

Washington, D.C.: National Institute of Educatioñ GAGE, N. L. Handbook of research on teaching.

(1963).

Chicago: Rand McNally.

Grants for research on law and government in education.

(1979).
(1979).

Washington, D.C.: National Institute of Education.

Guide to federal funding in career education, education and work, and vocational education.

(1978).

Washington, D.C.: National Institute of Education.

How to use ERIC.

(1979).

Washington, D.C.: National Institute of Education.

HOWE, H. (1980).

Two views of the new Department of Education and its first secretary: I. Phi Delta Kappan, 61, 446—447.

National Institute of Education: Summary of the reorganization plan.

(1978).
(1978).

Washington, Review of research in education.

(Annual; 1973—date).

Itasca, IL: F. E. Peacock, D.C.: National Institute of Education.

Publishers.

Submitting documents to ERIC.

(1979).

Washington, D.C.: National Institute of Education.

Chapter 2 Selecting a Problem and Preparing a Research Proposal 59 TRAVERS, R. M. W.

(Ed.).

(1973).

Handbook of research on teaching (2nd ed.).

Chicago: Rand McNally.

WARD, A. W. ET AL.

(1975).

Evaluation of published research in education.


American Educational Research Journal, 12, 109—128.

WESTBURY, I.

& BELLACK, A.

A.

(Eds.).

(1971).

Research into classroom procedures.

New York: Teachers College Press.

WrrrRoclc M, C.

(Ed.).

(1986).

Handbook of research on teaching (2nd ed.).

New York: MacMillan.

WYNN, RICHARD.

(1980).

Two views of the new Department of Education and its first secretary: II.

Phi Delta Kappan, 61, 447—448.

ZIGLER, E. (1977).


Twenty years of mental retardation research.

Mental Retardation, 15, 51—53.

3 The Research Report ALTHOUGH RESEARCH REPORTS may differ considerably in scope of treat- ment, they are expected to follow a conventional pattern of styleand form in academic circles.

These matters of style and form may seem unduly arbitrary to the student.
However, they are based upon principles of clear organization and presentation, and it is essential that graduate students in education be familiar with them if they are to •communicate their ideas effectively.

In addition, while the format and style suggested inthis chap- ter are particularly appropriate for a research report, the student also may find them useful in writing a review of the literature or a research proposal.
Style Manuals Some graduate schools or departments have designated an official manual or have established their own style manual to which their theses or dis- sertations must conform.

Students should find out which manual has been adopted officially by their institution or department.

Beginning grad- uate students are disturbed when they discover that these manuals are not always in complete agreement on matters of typography or format.
Careful examination, however, will reveal that differences concern minor details.

In general, all style manuals are in basic agreement on the prin- ciples of correct presentation.

Regardless of which manual is used as a guide, it should be followed consistently in matters of form.

and style.

The information in this chapter is consistent with one of the widely used style manuals, that of the American Psychological Association.
61 Pa One Introduction to Educational Research Format of the Research Report The research report, because of its relative brevity, differs somewhat from a thesis or dissertation.

The following outline presents the sequence of topics covered in the typical research report prepared according to the American Psychological Association's (APA) Publication manual (1983): I.

Title Page A.

Title B.

Author's name and affiliation C. Running head D. Acknowledgements (if any) II.

Abstract III.
Abstract III.

Introduction (no heading used) A.

Statement of the problem B. Background/review of literature C. Purpose and rationale/hypothesis IV.

Method A.

Subjects B.

Apparatus or instrumentation (if necessary) C. Procedure V. Results A.

Tables and figures (as appropriate) B.

Statistical presentation VI.

Discussion A.

Support or nonsupport of hypotheses B.

Practical and theoretical implications C. Conclusions VII.

References VIII.
References VIII.

Appendix (if appropriate) The APA style for typing a manuscript requires double spacing throughout the paper.

Additional spaces may be used to set off certain elements, such as the running head on the title page, but single spacing should never be used.

Leave margins of 1 inches at the top, bottom, right, and left of every page.

Number all pages, except the figures, be- ginning with the title page.

The title page and the abstract are on separate pages (pages 1 and 2, respectively).
A new page is begun for the introduc- tion, for the references, for each table and figure, and for each appendix.

The first page of the report is the title page.

This page includes the title, author's name, and author's affiliation near the top of the page, separated by double spaces.

Toward the bottom of the page are the run- ning head and acknowledgements, separated by a double space.

The title should be concise and should indicate dearly the purposes of the study.
One should keep in mind its possible usefulness to the Chapter 3 The Research Report 63 reader who may scan a bibliography in which it may be listed.

The title should not claim more for the study than it actually delivers.

It should not be stated so broadly that it seems to provide an answer that cannot be generalized, either from the data gathered or from the methodology employed.
For example, if a simple, descriptive, self-concept tudy were made of a group of children enrolled in a particular inner-city elementary school, the title should not read, "The Self-Concepts of Inner-City Chil- dren."

A more appropriate title would be "The Self-Concepts of a Group of Philadelphia Inner-City Children."

The first title implies broader gen- eralization than is warranted by the actual study.
The title should be typed in uppercase and lowercase letters, cen- tered, and, when two or more lines are needed, double spaced The.

running head, a shortened version of the title, should be a maximum of 50 characters including letters, punctuation, and spaces between words.

The running head is typed near the bottom of the page in uppercase letters.

Acknowledgements appear as unnumbered footnotes near the bot- tom of the title page.
Acknowledgements are used to indicate the basis of a study (e.g., doctoral dissertation), grant support, review of priordraft of the manuscript, and assistance in conducting the research and/or pre- paring the manuscript.

They should be clearly and directly stated.

Figure 3—1 illustrates a sample title page used in submitting a manuscript that was subsequently published (Kahn, 1982).

The abstract, on page 2 of the research report, describes the study in 100 to 150 words.
Included in this summary are the problem under study, characteristics of the subjects, the procedures used (e.g., data- gathering techniques, intervention procedures), the findings of the study, and the conclusions reached by the researcher.

A good abstract will in- crease the readership of the article because many persons start their re- views with abstracts.

Main Body of the Report The main body of the research report is divided into four major sections: introduction, method, results, and discussion.
The first of these sections, the introduction, begins a new page (page 3) and, because of its position, does not need or have a label.

A well-written introduction has three components.

The researcher must give a clear and definitive statement of the problem.

As described in Chapter 2, the problem must indicate the need for the research.

It is also necessary to indicate why the problem is importantin terms of theory and/or practice.
A review of previous literature on the topic is also an essential component of the introduction.

The researcher must demonstrate an 54 Part One Introduction to Educational Research Moral Reasoning in Irish Children and Adolescents as Measured by the Defining Issues Test James V. Kahn University of Illinois at Chicago This research was conducted while the author was a Senior Fuibright—Hays Scholar at University College, Cork, Ireland and on sabbatical leave from the University of Illinois at Chicago.
I wish to acknowledge the assistance of the computer facilities at both universities.

I also wish to thank the many children, teachers and administrators at the various schools at which the data reported were collected.

I also wish to thank Rose Naputano for her secretarial assistance and Larry Nucci for his critical comments.

RUNNING HEAD: IRISH MORAL REASONING FIGURE 3—1 Example of Title Page.

Chapter 3 The Research Report 65 understanding of the existing literature pertinent to his or her study.
However, although an exhaustive review is an appropriate part of a thesis or dissertation, it is not included in a research report.

The author should assume that the reader has some knowledge of the field being investigated.

Only research that is pertinent to the issue under investigation should be included.

The author also needs to logically connect the previous body of literature with the current work.
The final component of the introduction includes a clear rationale for the hypotheses to be proposed, definitions of the variables investigated and controlled, and a formal statement of each hypothesis.

Each hypoth- esis must be stated so that it is clear how it will be tested.

Terms must be clearly described, and predicted outcomes must be measurable.
For example, an investigation of an early intervention program with children at high risk for mental retardation should not hypothesize that "the high- risk children in the experimental group wifi have greater gains in inteffi- gence."

We need to know whom the high-risk children will be surpassing, and we need to know how intelligence is conceptualized.
A better hy- pothesis for this study would be, "high-risk children receiving the inter- vention program will have greater gains in IQ than will their control group peers."

The main body of the report continues with the method section, which follows the introduction.

It includes two or more subsections and describes in great detail just what the investigator did.

This allows the reader to determine how appropriate the procedures were and how much credence to give the results.
A well-written method section is sufficiently detailed to enable a reader to replicate the components of the study.

The method section is separated from the introduction by the centered heading, "Method."

Generally, subsections are then labeled at the left margin and underlined.

The method section always should include at least two subsections: subjects and procedures.
The subsection on subjects needs to identify the participants of the study, the number of persons included in the study, and the means by which the participants were selected.

Major demo- graphic characteristics, such as age, sex, socioeconomic status, and race, are included as they relate to the study.

Sufficient information must be provided to permit the reader to be able to replicate the sample.

The procedures subsection describes the actual steps carried out in conducting the study.
This includes the measurement devices, if no sep- arate section is provided; the experimental treatments; the assignment of subjects to conditions; the order of assessments, if more than one; the time period, if pertinent; and any design features used to control poten- tially confounding variables.

Again, enough information must be provided to permit replication.

However, procedures that are published in detail elsewhere should only be summarized with the citation given for the other publication.
66 Part One Introduction to Educational Research Additional subsections may be included as deemed necessary.

For instance, if a battery of complex tests is to be used and described, a separate subsection on instrumentation would be appropriate.

Complex designs also might be better described in a separate section.

The third section of the main body is results.

The results section presents the data and the statistical analyses without discussing the im- plications of the findings.
Individual scores or raw data are only presented in single-subject—_or very small sample size—studies.

All relevant find- ings are presented, including those that do not support the hypothesis.

Tables and figures are useful to supplement textual material.

They should be used when the data cannot readily be presented in a fewsentences in the text.

Data in the text and in tables or figures should not be redundant; rather they should be complementary.
The text should indicate what the reader should expect to see in the tables and figures so as to clarify their meaning.

The level of significance for statistical analyses should be presented.

Finally, the report's main body concludes with the discussion section.

After presenting the results it is possible to determine the implications of the study, including whether the hypotheses were supported or should be rejected.
It is appropriate to discuss both theoretical implications and practical applications of the study.

A brief discussion of limitations of the present investigation and proposals for future research is appropriate.

New hypotheses may be proposed if the data do not support the original hypotheses.

The researcher should also include conclusions that reflect whether the original problem is better understood, or even resolved, as a result of this study.
References and Appendices The reference section of the manuscript begins a new page with the label "References," centered.

References consist of all documents, includingjour- nal articles, books, chapters, technical reports, computer programs, and unpublished works that are mentioned in the text of the manuscript.
A reference section should not be confused with a bibliography: a bibliog- raphy contains everything that would be in the reference section plus other publications that are useful but were not cited in the manuscript.

Bibliographies are not generally provided for research reports; only ref- erences are usually included.

References are arranged in alphabetical order by the last names of the first-named authors.
When no author is listed, the first word of the title or sponsoring organization is used to begin the entry.

Each reference starts at the left margin of the page, with subsequent lines double spaced and indented.

No extra spaces separate the entries.

An appendix may be useful in providing detailed information that would seem inappropriate or too long for the main body of the paper.
Chapter 3 The Research Report 67 Each appendix begins on a new page with the label "Appendix" and its identifying letter, centered.

Following this label is thecentered title of the appendix and then the material.

Materials that generally should be in an appendix include: a new computer program, unpublished tests, lengthy treatments that are not available elsewhere, and so on.

The Thesis or Dissertation Research theses and dissertations follow the same outline as described for the research report.
The major difference of the thesis and dissertation is length and comprehensiveness.

Many institutions have their own style manuals for these major research papers; they may require acertain order of topics, the designating of each major (and some minor) sections as a chapter, bibliographies in place of reference sections, and more complete appendices.
Since a goal of the thesis or dissertation is to demonstrate the student's knowledge in a particular field, it is more appropriateto be complete and comprehensive than concise and brief.

Length is not an issue here as it is when submitting an article to a journal.

It may even be appropriate to include raw data and computer printout of the analyses performed.

Students should read theirinstitution's required style manual carefully before beginning their theses or dissertations.
Style of Writing The research report should be presented in a style that is creative, clear, and concise.

Although the phraseology should be dignified and straight- forward, it need not be dull or pedantic.

Even the most profound ideas can best be explained in simple language and short, coherent sentences.

Slang, hackneyed or flippant phrases, and folksy style should be avoided.

Because objectivity is the primary goal, thereshould be no ele- ment of exhortation or persuasion.
The research report should describe and explain, rather than try to convince or move to action.

In this respect the research report differs from an essay or feature article.

For years it was considered inappropriate for a researcher to use personal pronouns such as I, me, we, and so forth; people thought their use indicated a lack of objectivity.

This changed, however, when the second edition of the APA's Publication Manual was published in 1974.
Personal pronouns should be used when they are appropriate.

"I believe ."

is preferable to "The present author believes.

.

.

."

The writer should, however, refrain from using plural personal pronouns (e.g., we) unless there are multiple authors.

Only the last names of cited authorities are used.

Titles such as professor, Dr., Mr., and Dean are omitted.

The past tense should be used in describing research procedures that have been completed.
68 Part One Introduction to Educational Research Abbreviations may be used only after their ref errent has been spelled out, with the abbreviation following in parentheses.

There are a few exceptions to this rule for well-known abbreviations such as IQ.

Discussion of Quantitative Terms.

"Few in number" and "lessin quantity" are the preferred forms of expression.

Numbers beginning a sentence should always be spelled out.

Fractions and numbers of less than ten should be spelled out.
Use "one-half," but for all figures with fractions, use "4" or "4.5."

Percent (meaning "per hundred") is spelled out except in tables and figures.

Use Arabic numerals with percent ("18percent"), unless they begin a sentence.

Percentage means "proportion."

In numbers with more than three digits, commas should point off thousands or mil- lions (1,324; 12,304,000).

Ordinarily, standard statistical formulas are not presented in the research report, nor are computations included.
If a rather unusual for- mula is used in the analysis, it is appropriate to include it.

Of course, the ordinary rules of correct usage should prevail.

A good dictionary, a spelling guide, a handbook of style, and a thesaurus are helpful references.

We have frequently found in our own students' work errors ofspell- ing, nonagreement between subject and predicate, nonparallel construc- tion, and inconsistent tense sequence.
Students who have difficulty in written expression should have a competent friend or relative proofread their copy for correct usage before they type the final manuscript.

Inability to write correctly is a serious limitation.

Carelessness is an equally great fault.

Writing research reports effectively is not an easy task.

Good reports are not written hurriedly.

Even skillful and experienced writers revise many times before they submit a manuscript for publication.
Typing the Report Many students type their own term papers or research reports.

Anyone with reasonable proficiency and a willingness to learn proper procedures can do an acceptable job.

In fact, typing a report is an excellentway to learn proper form.

Typographical standards for the thesis or dissertation are more ex- acting.

Strikeovers, crossovers, insertions, and erasures are not permitted.
Therefore, only typists using a computer-based word processor or with great proficiency should attempt to prepare thesis or dissertation copy.

Although the expense of professional typing may seem high, the saving of time and excessive effort usually makes this arrangement the wise choice.

Chapter 3 The Research Report 69 It is the writer's responsibility to present manuscript material to the professional typist in proper form.
Except for minor typographical matters, the correction of major errors is not the responsibility of the typist.

After the material is received from the typist, the student should proofread it carefully before it is turned in.

Of course computers and wordprocessing programs may negate the need for hiring a typist.

Rules of Typography 1.

A good quality of bond paper, 8k" by 11" in size and of 13 to 16 pound weight, should be used.

Only one side of the sheet is used in typewritten manuscript.

2.
2.

All margins should be 1 inches—top, bottom, left, and right.

3.

All material should be double spaced.

4.

Words should not be divided at the end of the line unless completing them would definitely interfere with the margin.

A few spaces of runover is preferable.

In dividing words, consult a dictionary for correct syllabication.

5.

Direct quotations not over three typewritten lines in length are in- cluded in the text and enclosed in quotation marks.
Quotations of more than three lines are set off from the text in a double-spaced paragraph and indented five spaces from the left margin without quotation marks.

Original paragraph indentations are retained.

6.

Page numbers are given in parentheses at the end of a direct quo- tation.

7.

Underlining words or letters informs the printer, to set those words or letters in italics.

For example, book titles are underlined in a typed manuscript and printed in italics in a journal or book.
Reference Form References are cited in the text by giving the last name(s) of the author(s) and the year of the publication or, in the case of unpublished citations, the year the reference was written.

If the author's name does not appear in the text, the name and year appear in parentheses, separated by a comma.

If the author's name is used in the text, the year follows the name in parentheses.

When more than one work is cited in parentheses, the references are separated by semicolons.
Page numbers are only given, in parentheses, for direct quotations.

See examples of this style of citation throughout this book.

All materials referred to in the text, and only those, are listed al- phabetically in the reference section of the manuscript.

The Publication Manual of the American Psychological Association (1983) has specific guidelines for the format of various types of work.
The following illustrates 70 Part One Introduction to Educational Research the form that different types of references should take.

Titles underlined may be italicized instead.

1.

Book: Vaizey, J.

(1967).

Education in the modern world.

New York: McGraw- Hill.

2.

Book with multiple authors: Barzun, J.

& Graff, H. F. (1977).

The modern researcher.

New York: Harcourt, Brace, Jovanovich.

3.

Book in subsequent edition: Hallahan, D. P. & Kauffman, J. M. (1982).

Exceptional children (2nd ed.).


Englewood Cliffs, NJ: Prentice-Hall.

4.

Editor as author: Mitchell, J. V., Jr.

(Ed.).

(1985).

Mental measurement yearbook (9th ed.).

I-lighland Park, NJ: Gryphon Press.

5.

No author given: Prentice-Hall author's guide.

(1978).

Englewood Cliffs, NJ: Prentice- Hall.

6.

Corporate or association author.

American Psychological Association.

(1983).

Publication manual (3rd ed.).

Washington, DC: Author.

7.


Part of a series of books: Terman, L. M. & Oden, M. H. (1947).

Genetic studies of genius series: Vol.

4.

The gifted child grows up.

Stanford, CA: Stanford University Press.

8.

Chapter in an edited book: Kahn, J. V. (1984).

Cognitive training and its relationship to the language of profoundly retarded children.

In J. M. Berg (Ed.

), Perspectives and progress in mental retardation.

Baltimore: Uni- versity Park, 211—219.

9.

Journal article: Seltzer, M. M. (1984).


Correlates of community opposition to corn- Iiunity residences for mentally retarded persons.

American Journal of Mental Deficiency, 89, 1—8.

10.

Magazine article: Meer, J.

(1984, August).

Pet theories.

Psychology Today, pp.

60—67.

11.

Unpublished paper presented at a meeting: Schmidt, M., Kahn, J. V., & Nucci, L. (1984, May).

Moral and social conventional reasoning of trainable mentally retarded adolescents.
Paper presented at the annual meeting of the American Association on Mental Deficiency, Minneapolis, MN.

12.

Thesis or dissertation (unpublished): Best, J. W. (1948).

An analysis of certain selected factors underlying the choice of teaching as a profession.

Unpublished doctoral dis- sertation, University of Wisconsin, Madison.

Chapter 3 The Research Report 71 13.

Unpublished manuscripts: Kahn, J. V., Jones, C., & Schmidt, M. (1984).
Effect of object erence on sign learnability by severely and profoundly retarded children: A pilot study.

Unpublished manuscript, University of Illinois at Chicago.

Kahn, J. V. (1991).

Using the Uzgiris and Hunt scales to understand sign usage of children with severe and profound mental tion.

Manuscript submitted for publication.

14.

Chapter accepted for publication: Kahn, J. V. (in press).
Predicting adaptive behavior of severely and profoundly mentally retarded children with early cognitive meas- ures.

Journal of Mental Deficiency Research.

15.

Technical report: Kahn, J. V. (1981).

Training sensorimotor period and language skills with severely retarded children.

Chicago, IL: University of Illinois at Chicago.

(ERIC Document Reproduction Service, No.

ED 204 941).

Pagination Page numbers are assigned to each page of the paper or report.
The title page does not have a page number typed on it, but a number is allowed for it in the series.

Page numbers are placed in the upper right-hand corner, one inch below the top of the page and aligned with the right margin.

Pages are numbered consecutively from the title page, through the abstract, main body of the paper, and references.

After the references come the footnotes (if any), tables, figures, and appendices (if any), the numbering of pages continuing in this order.
In addition, each page except the title page has a short title (the running head) typed above the page number (usually the first two or three words of the whole title).

This is so that if the pages are separated, they can be identified with the appropriate manuscript.

Tables A table is a systematic method of presenting statistical data in vertical columns and horizontal rows, according to some classification of subject matter.
Tables enable the reader to comprehend and interpret massesof data rapidly and to grasp significant details and relationships at a glance.

Tables and figures should be used sparingly; too many will overwhelm the reader.

Good tables are relatively simple, concentrating on a limited number of ideas.

Including too much data in a table minimizes the value of tabular 72 Part One Introduction to Educational Research presentation.
It is often advisable to use several tables rather than to include too many details in a single one.

It has been said that the mark of a good table is its effectiveness in conveying ideas and relationships independently of the text of the report.

Because each table is on a separate page following the references, the desired placement of the table is indicated by the following method.
Text references should identify tables by number, rather than by such expressions as "the table above," or "the following table."

Tables should rarely be carried over to the second or third page.

If the table must be continued, the headings should be repeated at the top of each column of data on each page.

Tables should not exceed the page size of the manuscript.
Large tables that must be folded into the copy are always cumbersome and TABlE 2 Occupations of Fathers of University of Wisconsin Seniors Preparing to Peach' Occupations Business proprietor Skilled labor Farming Clerical-sales Profession Unskilled labor Nodata Men N 24 19 17 16 15 6 5 23 18 17 16 15 6 5 32 10 19 18 20 6 7 29 9 17 16 18 5 6 Total 102 100 112 100 'Adapted from Best, J.W.

(1948).

An analysis of certain selected factors underlying the choice of teaching as a profession.
Unpubljshed doctoral dissertation, University of Wisconsin, Madison.

bPercentages rounded to equal 100%.

FIGURE 3—2 A Sample Table.

Chapter 3 The Research Report 73 cannot be easily refolded and replaced.

Large tables should be reduced to manuscript page size by photostat or some other process of reproduc- tion.

Tables that are too wide for the page may be turned sideways, with the top facing the left margin of the manuscript.

See Figure 3—2 for a sample of a properly presented table.
The word table is centered between the page margins and typed in capital letters, followed by the table number in arabic numerals.

Tables are numbered consecutively throughout the entire report or thesis, including those tables that may be placed in the appendix.

The caption or title is placed one double space below the word table and centered.

No terminal punctuation is used.

The main title should be brief, dearly indicating the nature of the data presented.
Occasionally a subtitle is used to supplement a briefer main title; denoting such additional information as sources of data and measuring units employed.

Column headings, or box heads, should be clearly labeled, describing the nature and units of measure of the data listed.

If percentages are presented, the percentage symbol (%) should be placed at the top of the column, not with the number in the table.
If numbers are shortened by the omission of zeros, that fact should be mentioned in the subtitle ("in millions of dollars"; "in thousands of tons").

The "stub," or label, for the rows should be clear and concise, parallel in grammatical structure, and if possible, no longer than two lines.

Decimal points should always be carried out to the same place (e.g., tenths or hundredths) and aligned in the column.

When no data are available for a particular cell, indicate the lack by a dash, rather than a zero.
When footnotes are needed to explain items in the table, small letters are used.

Numerical superscripts would be confused with the data con- tained in the table.

Asterisks are used to indicate probabffity levels and are also placed below the table.

Figures A figure is a device that presents statistical data in graphic form.

The term figure is applied to a wide variety of graphs, charts, maps, sketches, diagrams, and drawings.
When skillfully used, figures present aspects of data in a visualized form that may be clearly and easily understood.

Figures should not be intended as substitutes for textual description, but included to emphasize certain significant relationships.

Many of the qualities that were listed as characteristics of good tables are equally appropriate when applied to figures.

1.

The title should clearly describe the nature of the data presented.

2.
2.

Figures should be simple enough to convey a clear idea and should be understandable without the aid of much textual description.

74 Part One Introduction to Educational Research 3.

Numerical data upon which the figure is based should be presented in the text or an accompanying table, if they are not included in the figure itself.

4.

Data should be presented carefully and accurately, so that oversim- plification, misrepresentation, or distortion do not result.

5.

Figures should be used sparingly.


Too many figures detract from, rather than illuminate, the presentation.

6.

Figures follow tables in the order of items in a manuscript.

The placement desired in the text is indicated in the same manner used to indicate the placement of tables.

7.

Figures should follow, not precede, the related textual discussion.

8.

Figures are referred to by number, never as "the figure above" or "the figure below."

9.
9.

The title and number of the figure is placed on a separate page that precedes the figure in the manuscript.

The Line Graph The line graph is useful in showing change in data relationships over a period of time.

The horizontal axis usually measures the independent variable, the vertical axis the measured characteristic.

Graphic arrange- ment should proceed from left to right on the horizontal axis, and from bottom to top on the vertical.
The zero point should always be represented, and scale intervals should be equal.

If a part of the scale is omitted, a set of parallel jagged lines should be used indicating that part of the scale is omitted (see Figure 3—3).

We have devised two figures, a line graph and a bar graph, that depict data relationships that were presented textually in two journal articles (see Figures 3—4 and 3—5).
When several lines are drawn, they may be distinguished by using various types of lines—solid, dotted, or alternate dots and dashes.

Black ink is used.

A smoothed curve cannot be obtained by plotting any data directly.

Only when infinite data are obtained will the lines connecting the points 0 FIGURE 3—3 A Line Graph.

Chapter 3 The Research Report 75 FIGURE 3—4 Mean Verbal SAT Scores, 1972—1977, in Four Selected States.
(Graphic Representation by the Author, Adapted from Kappan Interview with Ernest Sternglass, "The Nuclear Radiation/SAT Score Decline Connection," Phi Delta Kappan, 61 [Nov. 1979], 184.)

approach a curved line.

The figure formed by the lines connecting the points is known as a frequency polygon.

The Bar Graph or Chart The bar graph, which can be arranged either horizontally or vertically, represents data by bars of equal width, drawn to scale length.
The flu- merical data may be lettered within the bar or outside it.

A grid may be used to help quantify the graphic representation.

A divided bar graph represents the components of a whole unit in one bar (see Figure 3—6).

In bar graphs, the bars are usually separated by space.

If the graph contains a large number of items, the bars may be joined to save space.

76 Part One Introduction to Educational Research Ii Ii ii FIGURE 3—5 Decline of Mean Scholastic Aptitude Test Scores from 1963 to 1978.
(Thrpughout the 1 970s, Verbal Scores Declined .04 Standard Deviation Each Year and Mathematical Scores Declined .025 Standard Deviation.)

Horizontal bar graphs are usually used to compare components at a particular time.

Vertical bars are used when making comparisons at dif- ferent times.

The Circle, Pie, or Sector Chart Circle, pie, or sector charts show the division of a unit into its component parts.
They are frequently used to explain how a unit of government distributes its share of the tax dollar, how an individual spends his or her salary, or any other type of simple percentage distribution.

The radius is drawn vertically, and components are arranged in a clockwise direction in descending order of magnitude.

The proportion of data is indicated by the number of degrees in each section of the 360- degree circle (see Figure 3—7).
Ll I flfl Horizontal Vertical Divided FIGURE 3—6 Divided Bars for Graphs.

Chapter 3 The Research Report 77 No high school 2% High school dropout FIGURE 3—7 Educational Backgrounds of 129 American Celebrities Listed in the Current Biography 1966 Yearbook.

(Adapted from Adela Deming, "The Educational Attainments of Americans Listed in the Current Biography 1966 Yearbook."

Unpub- lished Report, Butler University, Indianapolis, Indiana, 1967, p.

7.)
7.)

This kind of data should be typed or printed within the segment if possible.

If there is insufficient room for this identification, a small arrow should point from the identification term to the segment.

Maps When geographic location or identification is important, maps may be used.

Identification may be made by the use of dots, circles, or other symbols, and density or characteristics of areas can be represented by shading or crosshatching.
A key or legend should always be supplied if shadings are used.

Organization Charts To show staff functions, lines of authority, or flow of work within an organization, an organization chart is a helpful graphic device.

Units may be represented by circles, squares, or oblongs, with names lettered within the units.

Distinctions between direct and indirect rela- tionships may be indicated by the use of solid and dotted lines.
Ordinarily, authority, supervision, or movement of materials flows from the top to 78 Part One Introduction to Educational Research the bottom of the chart, but variations can be indicated by the use of arrows.

Evaluafing a Research Report Writing a critical analysis of a research report is a valuable experience for the student of educational research.
Reports for this purpose may be taken from published collections and such periodicals as the Educational Re- searcher, the Journal of Educational Research, or one of the many other pub- lications that publish reports of research in education or in the closely related fields of psychology or sodology.

Unpublished research reports written by previous students of educational research are another source, as are the theses or dissertations found in the university library.
Through a critical analysis, the student may gain some insight into the nature of a research problem, the methods by which it may be attacked, the difficulties inherent in the research process, the ways in which data are analyzed and conclusions drawn, and the style in which the report is presented.

The following questions are suggested as a possible structure for the analysis: 1.

The Title and Abstract a.

Are they clear and condse?

b.

Do they promise no more than the study can provide?

2.
2.

The Problem and Hypotheses (Introductory Section) a.

Is the problem clearly stated?

b.

Is the problem properly delimited?

c. Is the significance of the problem recognized?

d. Are hypotheses clearly stated and testable?

e. Are assumptions, limitations, and delimitations stated?

f. Are important terms defined?

3. Review of Related Literature (Introductory Section) a.

Is it adequately covered?

b.

Are important findings noted?

c. Is it well organized?

d. Is an effective summary provided?


e. Is the literature cited directly relevant to the problem and hypotheses?

4.

Method Section a.

Is the research design described in detail?

b.

Is it adequate?

Chapter 3 The Research Report 79 c. Are the samples described in detail?

d. Are relevant variables recognized?

e. Are appropriate controls provided to establish experimental validity?

f. Are data-gathering instruments appropriate?

g. Are validity and reliability of the instruments established?
h. Can the sample and procedure be replicated based on the in- formation and references given?

5.

Results Section a.

Is the statistical treatment appropriate?

b.

Is appropriate use made of tables and figures?

c. Is the analysis of data relationships logical, perceptive, and objective?

6.

Discussion Section a.

Is the discussion clear and concise?

b.

Is the problem/hypothesis restated appropriately?

c. Is the analysis objective?


d. Are the findings and conclusions justified by the data presented and analyzed?

e. Did the author(s) generalize appropriately or too much?

7.

Overall Writing of Paper a.

Is it clear, concise, and objective?

b.

Are the parts of the paper properly related to each other?

Summary The research report is expected to follow the conventional pattern of style and form used in academic circles.
Although style manuals differ in some of the smaller details, students are expected to be consistent in following the pattern of style contained in the manual required by their institution or in the one that they are permitted to select.

The style of writing should be clear, concise, and completely objective.

Of course, the highest standards of correct usage are expected, and careful proofreading is necessary before the final report is submitted.
Tables and figures may help to make the meaning of the data clear.

They should be presented in proper mechanical form and should be carefully designed to present an accurate and undistorted picture.

The evaluation of a research project is a valuable exercise for students of educational research.

Using analytical questions such as those suggested, the cri- tiquing of another researcher's report helps students develop competency in their own research and reporting skills.
80 Part One Introduction to Educational Research References American Psychological Association.

(1983).

Publication manual (3rd ed.).

Wash- ington, DC: Author.

KHr, J. V. (1982).

Moral reasoning in Irish children and adolescents as measured by the Defining Issues Test.

Irish Journal of Psychology, 2, 96—108.

Additional Readings CAMPBELL, W. G. & BALLOU, S. V. (1974).

Form and style: Theses, reports, term papers.

Boston: Houghton Mifflin.

KcmsoD, P. E. (1964).


The writing requirements for graduate degrees.

Englewood Cliffs, NJ: Prentice-Hall.

PEIumJ, P. G. (1972).

Reference handbook of grammar and usage.

New York: William Morrow.

Roget's international thesaurus (4th ed.).

(1979).

New York: Harper & Row.

STRUNK, W. & WHITE, E. B.

(1979).

The elements of style (3rd ed.).

New York: Macmillan.

TURABIAN, K. L. (1987).

A manual for writers of term papers, theses, and dissertations (5th ed.).
Chicago: University of Chicago Press.

University of Chicago (1982).

The Chicago Manual of Style (13th ed.).

Chicago: Uni- versity of Chicago Press.

__PART TWO Research Methods THE METHODOLOGIES UTILIZED in the conduct of educational research are based, in most instances, on research methods used in the behavioral and social sciences, relying most heavily on psychology, sociology, and anthropology.
Since research in these fields of study emphasizes logical- positivism, which uses experimental and quantitative research methods, most educational research also utilizes these methodologies.

Still, some research concerns may be addressed more appropriately with a phenomenological, or qualitative, research approach derived from the humanities, particulatly history and philosophy, or with qualitative methods from the social sciences (e.g., ethnography from anthropology).
Research can be divided into two broad categories: quantitative re- search and qualitative research.

Quantitative research consists of those in which the data concerned can be analyzed in terms of numbers.

An example of quantitative research might be a study comparing two methods of teaching reading to first-grade children, because the data used to determine which method is more successful will be a test score.
The average score of the children receiving one method will be compared to the average score of children receiving the other method.

This example would be an experimental study (discussed in Chapter 6) if the experimenter randomly assigned the children to the methods, or a descriptive study (Chapter 5) if the children had already received the instruction and the experimenter was merely ex- amining.the results after the fact.

In either case the study w uld be considered quantitative.
Research can also be qualitative; that is, it can describe events, per- sons, and so forth scientifically without the use of numerical data.

A study consisting of interviews of mothers of handicapped infants to determinehow their lives and beliefs were affected by the birth of their handicapped children is an example of qualitative research.

Such a study would carefully and logically analyze the responses of the mothers and report those responses that are consistent as well as areas of disagreement.
81 82 Part Two Research Methods Each of these types of research has advantages and disadvantages.

In quantitative research, the experimenter has carefully planned the study including the tests, or other data collection instruments, to be used.

Each subject is studied in an identical manner and there is little room for human bias to create problems with the data.

Qualitative research is also planned carefully.


Yet qualitative studies leave open the possibility of change, to ask different questions, and to go in the direction that the observation may lead the experimenter.

Quantitative research is basedmore directly on its original plans and its results are more readily analyzed and interpreted.

Qualitative research is more open and responsive to its subject.
Part Two of this book is intended to provide the student witha detailed description of a variety of methodologies and data collection procedures that are used in educational research.

Chapter 4 describes the procedures used in historical research.

Some historians (we include educational historians in this broader category) use strictly qualitative procedures based on the hu- manistic research view, while others include quantitative procedures from the social science perspective.
Both of theseapproaches are described.

Chapter 5 strives to delineate the variety of descriptive studies (as- sessment, evaluation, and research) using quantitative methods thatcan be accomplished in educational settings.

All descriptive studies are attempts to describe the current state of affairs.

Two of the broad categories of descriptive studies discussed in this chapter, assessment and evaluation studies, are not truly research.
These two categories differ from descriptive research in that their goals are not the creation of new, generalizable knowledge.

Rather, these ir1portant types of studies are interested in theassessment of a given population's status on one or more measures or in the evaluation of the success of a given program or project, respectively.

Chapter 6 describes the variety of research designs that can be utilized in experimental research.
Included are true experimental designs, where randomization is used, quasi-experimental designs (no randomization), and preexperimental designs, which offer little or no control of confounding vari- ables.

This chapter also describes and defines thetype of variables used in experimental research (independent, dependent, and confounding), experi- mental validity and the various threats to it, and the ways in which the diverse research designs control for the various threats to experimental validity.
Chapter 7 provides the reader with informationregarding a specific type of experimental research, single-subject research.

The most commonly used designs and procedures are described.

Chapter 8 is new to this edition.

Although the topic ofqualitative meth- odologies was included in the previous edition, only a fewapproaches were covered briefly.

This chapter provides a more comprehensive and detailed overview of qualitative research methods, other thanhistorical methods which are covered in Chapter 4.
Chapter 9 depicts the tools and methods by which researchers collect data.

The procedures described include those used in all types of research discussed in the previous chapters.
Part Two Research Methods 83 While Chapters 5 through 8 separate and describe different approaches to the research endeavor to make it easier for the beginning researcher to understand these procedures, the separation of descriptive research from experimntal and quantitative from qualitative is somewhat arbitrary and ar- tificial.

For instance, some descriptive research (Chapter 5) can utilize the quasi-experimental designs described in Chapter 6.
More importantly, quantitative and qualitative research should be thought of as existing on a continuum rather than a mutually exclusive dichotomy.

For instance, survey research is described in both Chapters 5 and 8 because it can be used in both quantitative and qualitative research.

More to the point, the same survey instrument, in the same study, can include both quantitative and qualitative items which require different analyses.
In addition, as indicated in Chapter 8, some qualitative research methods will culminate in some numerical result (e.g., the summing up of the number of instances of interview responses In each response category).

Thus, while the reader is being in- troduced to each of these concepts separately, the good researcher will utilize a variety of these methods in combination during her or his career.
4 Historical Research HISTORICAL RESEARCH DIFFERS MARKEDLY from the sort of research con- ducted by most scientists, including behavioral and social scientists.

In fact, it is so different from other types of research that it almost does not belong as a topic in this book.
It is included because many areas of concern to education can best be studied in this way, because the quantity and quality of research on the history of education have increased greatly in the past two decades (e.g., Best, 1983; Warren, 1978), and because a review of the research literature which is done prior to other types of research is, in effect, a historical study.

History is a meaningful record of human achievement.
It is not merely a list of chronological events but a truthful integrated account of the relationships between persons, events, times, and places.

We use history tq understand the past and to try to understand the present in light of past events and developments We also use it to prevent "rein- venting the wheel" every few years.

Historical analysis may be directed toward an individual, an idea, a movement, or an institution.
However, none of these objects of historical observation can be considered in iso- lation.

People cannot be subjected to historical investigation without some consideration of their interaction with the ideas, movements, and/or in- stitutions of their times.

The focus merely determines the points of em- phasis toward which historians direct their attention.

Table 4—i illustrates several historical interrelationships that have been taken from the history of education.
For example, no matter whether the historian chooses for study the Jesuit Society, religious teaching orders, the Counter-Reformation, or Ignatius of Loyola, each of the other elements appears as a prominent influence or result and as an indispensable part of the account.

The interrelationship of this institution, movement, and man would make the study of one in isolation from the others meaning- less, if not impossible.
Those who wish to engage in historical research should read the works of historians regarding the methods and approaches to conducting 85 86 Part Two Research Methods TABLE 4—1 Some Examples of the Historical Interrelationship among Men, Movements, and Institutions Men Movements Institutions General Type .

Name Ignatius of Loyola Benjamin Franklin Daniel Colt Gilman C. Stanley Hall Wm.

Rainey Harper John Dewey W. E. B. Dubois Walter White B. R. Buckingham Counter-Reformation .
Scientific Movement Education for Life Graduate Study and Research Religious Teaching Order Academy University Gradu- ate School .

Experimentalism Progressive Educa- tipn Racial Integration in the Public Schools .
Scientific Research in Education Experimental School Persuasion Organi- zation Research Periodical, Research Organiza- tion Society of Jesus, 1534 (Jesuit Society) Philadelphia Acad- emy, 1751 Johns Hopkins Uni- versity, 1876 Clark University, 1887 University of Chi- cago, 1892 University of Chi- cago Elementary School, 18% National Assn.

for the Advancement of Colored People, 1909 Journal of Ed.
Re- search, 1920 American Educa- tional Research Assn., 1931 historical studies in education (e.g., Best, 1983; Billington, 1975; Brickman, 1982; Gottschalk, 1950; Hockett, 1948; Warren, 1978).

The History of American Education Historical studies deal with almost every aspect of Amencan education.

Such investigations have pointed out the important contributions of both educators and statesmen.
They .have examined the growth and develop- ment of colleges and universities, elementary and secondary schools, ed- ucational organizations and associations, the rise and decline of educa- tional movements, the introduction of new teaching methods, and the issues that have persistently confronted American education.

An understanding of the history of education is important to profes- sional workers in this field.
It helps them to understand the how and why of educational movements that have appeared and, in some cases, con- Chapter 4 Historical Research 87 tinue to prevail in the schools.

It helps them to evaluate not only lasting contributions but also the fads and "bandwagon" schemes that have ap- peared on the educational scene only to be discarded.

An examination of many developments of the past seems to confirm the observation that little in education is really new.
Practices hailed as innovative are often old ideas that have previously been tried and replaced by something else.

Innovators should examine the reasons why such practices were discarded and consider whether their own proposals are likely to prove more successful.

Several studies, briefly described, illustrate the historical background of some contemporary educational movements and issues.
Organized programs of indivi4ualized instruction introduced in a number of school systems in the 1960s seem to be similar in many respects to those introduced in a number of schools in the 1890s and in the first quarter of the twentieth century.

First introduced at Pueblo, Colorado, and known as the Pueblo Plan, later modified and known as the Winnetka and Dalton Plans, these programs do seem to have common elements.
Dispensing with group class activity in academic courses, students were given units of work to complete at their own rate before proceeding to more advanced units.

Individual progress based upon mastery of subject matter units was the criterion for promotion or completion of a course.

Search (1901) advocated this plan, and his influence upon Carleton Wash- burn in the elementary schools of Winnetka, Illinois, and Helen Parkhurst in the secondary schools at Dalton, Massachusetts, is generally recognized.
Whether the Pueblo, Winnetka, or Dalton plans were fads or sound pro- grams, the fact remains that they disappeared from the schools before reappearing in the 1960s.

The place of religion in public education is an issue that concerns many people.

In the period following World War II, in a series of Supreme Court decisions, religious instruction and religious exercises within public schools have been declared unconstitutional and in violation of the First Amendment of the United States Constitution.
In 1963, in the case of Abington School District v. Schempp, the Court held that a Pennsylvania law requiring daily Bible reading was in violation of the First Amendment.

Much resentment and criticism of the Supreme Court followed this de- cision, and several efforts have been made to introduce amendments to the Constitution to permit religious exercises in the public schools.

The Bible reading issue was also a bitter one more than 100 years ago.
The Philadelphia Bible Riots of 1840 (Lannie & Diethorn, 1968) re- sulted in the deaths of about 45 soldiers and civilians, serious injury to about 140, and property damage to homes and churches valued at nearly $500,000.

Nativist/foreign-born, and Catholic/Protestant conflicts produced the tense atmosphere, but the Bible reading issue precipitated the riots.
It is apparent that Bible reading is not an issue of recent origin and that an understanding of previous conflicts places the issue in clearer perspective.
88 Part Two flesearch Methods The contributions of Thomas Jefferson, Benjamin Franklin, Calvin Stowe, Catherine Beecher, Horace Mann, Maria Montessori, Henry Bar- nard, Ella Flagg Young, William Holmes McGuffey, Daniel Coit Gilman, John Dewey, and many other eminent educators have been carefully examined in many studies, and their impact on American education has been noted.

Thursfield (1945) studied Henry Barnard's American Journal of Edu- cation, published in 31 massive volumes between 1855 and 1881.
He points out the Journal's vital contribution to the development of American edu cation.

Through its comprehensive treatment of all aspects of education it provided a readily available medium for the presentation and exchange of ideas of many of the great educators of the period.

It has been stated that almost every educational reform adopted in the last half of the nine- teenth century was largely due to the influence of the Journal.
Among its contributors were Henry Barnard, Horace Mann, Bronson Alcott, Daniel Coit Gilman, William T. Harris, Calvin Stowe, and Herbert Spencer, in addition to many prominent foreign contributors.

Cremin (1961) examined the reason for the rise and decline of the Progressive Education movement, including the major changes in philos- ophy and practices that transformed American education and the forces that brought the movement to a halt in the 1950s.
Although some his- torias differ with his conclusions, Cremin's analysis is the definitive history of Progressive Education in America.

These historical studies are examples of but a few of the thousands of books, monographs, and periodical articles that depict the story of American education.

In addition to examining these works, students are urged to consult the History of Education Quarterly, in which scholarly book reviews and critical analyses of contemporary historical research are presented.
History and Science Opinions differ as to whether or not the activities of the historian can be considered scientific or whether there is such a thing as historical research.

Those who take the negative position may point out the following limitations: 1.

Although the purpose of science is prediction, the historian cannot usually generalize on the basis of past events.
Because past events were often unplanned or did not develop as planned, because there were so many uncontrolled factors, and because the influence of one or a few individuals was so crucial, the same pattern of factors is not repeated.

Chapter 4 Historical Research 89 2.

The historian must depend upon the reported observations of others, often witnesses of doubtful competence and sometimes of doubtful objectivity.

3.
3.

The historian is much like a person trying to complete a complicated jigsaw puzzle with many of the parts missing.

On the basis of what is often incomplete evidence, the historian must fill in the gaps by inferring what has happened and why it happened.

4.

History does not operate in a closed system such as may be created in the physical scietce laboratory.

The historian cannot control the conditions of observation nor manipulate the significant variables.
Those who contend that historical investigation may have the char- acteristics of scientific research activity present these arguments: 1.

The historian delimits a problem, formulates hypotheses or raises questions to be answered, gathers and analyzes primary data, tests the hypotheses as consistent or inconsistent with the evidence, and formulates generalizations or conclusions.

2.
2.

Although the historian may not have witnessed an event or gathered data directly, he or she may have the testimony of a number of witnesses who have observed the event from different vantage points.

It is possible that subsequent events have provided additional in- formation not available to contemporary observers.

The historian rigorously subjects the evidence to critical analysis in order to estab- lish its authenticity, truthfulness, and accuracy.

3.
3.

In reaching conclusions, the historian employs principles of proba- bility similar to those used by physical scientists.

4.

Although it is true that the historian cannot control the variables directly, this limitation also characterizes most behavioral research, particularly nonlaboratory investigations in sociology, social psy- chology, and economics.

5.
5.

The observations of historians may be described in qualitative or quantitative terms depending on the subject matter and the approach of the historian.

In general, the traditional approach is qualitative while the revisionists use quantitatiye analyses.

The traditional, qual- itative approach in many historical studies does not preclude the application of scientific methodology.
As Brickman (1982) points out, it simply requires "the synthesis and presentation of the facts in a logically organized form" (p. 91).

Historical Generalization There is some difference of opinion, even among historians, as to whether or not historical investigations can establish generalizations.

Most histo- 90 Part Two Research Methods rians would agree that some generalizations are possible, but they disagree on the validity of applying them to different times and places.
Gottschalk (1963) states the case of the comparative historian in this way: Sooner or later one or more investigators of a period or area begin to suspect some kind of nexus within the matter of their historical investigation.
Though such "hunches," "insights," "guesses," "hypotheses"—whatever you may call them—may be rejected out of hand by some of them, the bolder or rasher among them venture to examine the possibility of objective reality of such a nexus, and then it is likely to become a subject of debate, and perhaps of eventual refinement to the point of wide recognition in the learned world.

Tlw process is not very different from the way analytical scholars in other fields proceed—Darwin, for example, or Freud.
If this process serves no other purpose, it at least may furnish propositions upon which to focus future investigations and debates.

.

But do not these historical syntheses, no matter what their author's intention, invariably have a wider applicability than to any single set of data from which they rose?

If Weber was right, isn't it implicit in this concept of the Protestant ethic that where a certain kind of religious attitude prevails, there the spirit of capitalism Will, or at least may, flourish?

.
.

.

.

IfMahan was right,, couldn't victory in war (at least before the invention of the airplane) be regarded as dependent on maritime control?

If Turner was right, won't his frontier thesis apply to some extent to all societies that have frontiers to conquer in the future, as well as it has applied to American society in the past?

(pp.

121—122) Finley (1963) comments on generalization: Ultimately the question at issue is the nature of the historian's function.
Is it only to recapture the individual concrete events of a past age, as in a mirror, so that the progress of history is merely one of rediscovering lost data and of building bigger and better reflectors?

If so, then the chronicle is the only correct form for his work.

But if it is to understand—however one chooses to define the word—then it is to generalize, for every explanation is, or implies, one or more generalizations.
(p. 34) Aydelotte (1963) states the argument for generalization: Certainly the impossibility of final proof of any historical generalization must be at once conceded.

Our knowledge of the past is both too limited and too extensive.

Only a minute fraction of what has happened has been recorded, Chapter 4 Historical Research 91 and only too often the points on which we need most information are those on which our sources are most inadequate.
On the other hand, the fragmentary and incomplete information we do have about the past is too abundant to prevent our coming to terms with it; its sheer bulk prevents its being easily manipulated, or even easily assimilated, for historical purposes.

Further, historians deal with complex problems, and the pattern of the events they study, even supposing it to exist, seems too intricate to be easily grasped.

Doubtless, finality of knowledge is impossible in all areas of study.
We have learned through works of popularization how far this holds true even for the natural sciences, and, as Crane Brinton says, the historian no longer needs to feel that "the uncertainties and inaccuracies of his investigation leave him in a position of hopeless inferiority before The glorious certainties of physical science."

(pp.

156—157) The foregoing quotations are presented in support of the position that the activities of the historian are not different from those of the scientist.
Historical research as it is defined in this chapte* includes de- limiting a problem, formulating hypotheses or generalizations tobe tested or questions to be answered, gathering and analyzing data, and arriving at probabffity-type conclusions or at generalizations based upon deductive- inductive reasoning.

The Historical Hypothesis Nevins (1962) illustrates the use of hypotheses in the historical research of Edward Channing in answering the question, "Why did the Confed- eracy collapse in April 1865?"
Channing formulated four hypotheses and tested each one in light of evidence gathered from letters, diaries, and official records of the army and the government of the Confederacy.

He hypothesized that the Confederacy collapsed because of 1.

The military defeat of the Confederate army 2.

The dearth of military supplies 3.

The starving condition of the Confederate soldiers and the civilians 4.
The disintegration of the will to continue the war Channing produced evidence that seemed to refute the first three hypotheses.

More than 2OO,OOO well-equipped soldiers were under arms at the time of the surrender, the effective production of powder and arms provided sufficient military supplies to continue the war, and enough food was available to sustain fighting men and civilians.
Channing conduded that hypothesis 4, the disintegration ofthe will to continue the war, was substantiated by the excessive numberof desertions 92 Part Two Research Methods of enlisted men and officers.

Confederate military officials testified that they had intercepted many letters from home urging the soldiers to desert.

Although the hypothesis sustained was not specific enough to be partic- ularly helpful, the rejection of the first three did claim to dispose of some commonly held explanations.
This example illustrates a historical study in which hypotheses were explicitly stated.

Hypotheses in Educational Historical Research Hypotheses may be formulated in historical investigations of education.

Several examples are listed.

1.

The educational innovations of the 1950s and 1960s were based upon practices that previously have been tried and discarded.

2.
2.

Christian countries whose educational systems required religious in- struction have had lower church attendance rates than those coun- tries in which religious instruction was not provided in the schools.

3.

The observation of European school systems by Atherican educators during the nineteenth century had an important effect upon Amer- ican educational practices.

4.

The monitorial system had no significant effect upon American education.
Although hypotheses are not always explicitly stated in historical investigations, they are usually implied.

The historian gathers evidence and carefully evaluates its trustworthiness.

If the evidence is compatible with the consequences of the hypothesis, it is confirmed.

If the evidence is not compatible, or negative, the hypothesis is not confirmed.

It is through such synthesis that historical generalizations are established.
The activities of the historian, when education is his or her field of inquiry, are no different from those employed in any other field.

The sources of evidence may be concerned with schools, educational practices and policies, movements, or individuals, but the historical processes are the same.

DifficultIes Encountered in Historical Research The problems involved in the process of historical research make it a somewhat difficult task.
A major difficulty is delimiting the problem so that a satisfactory analysis is possible.

Too often, beginners state a problem much too broadly; the experienced historian realizes that historical re- search must involve a penetrating analysis of a limited problem rather than a superficial examination of a broad area.

The weapon of research is the target pistol, not the shotgun.
Chapter 4 Historical Research 93 Since historians may not have lived during the time they are studying and may be removed from the events they investigate, they must often depend upon inference and logical analysis, using the recorded experience of others rather than direct observation.

To ensure that their information is as trustworthy as possible, they must rely on primary, or firsthand, accounts.

Finding appropriate primary sources of data requires imagina- tion, hard work, and resourcefulness.
Historians must also keep in mind the context in which the events being studied occurred and were recorded.

It is necessary to keep the biases and beliefs of those who recorded the events in mind, as well as the social and political climate in which they wrote.

Sources of Data Historical data are usually classified into two main categories: 1.

Primary sources are eyewitness accounts.

They are reported by an actual observer or participant in an event.

2.
2.

Secondary sources are accounts of an event that were not actually witnessed by the reporter.

The reporter may have talked with an actual observer or read an account by an observer, but his or her testimony is not that of an actual participant or observer.

Secondary sources may sometimes be used, but because of the distortion in passing on information, the historian uses them only when primary data are not available.

Primary Sources of Data Documents.


Documents are the records kept and written by actual par- ticipants in, or witnesses of, an event.

These sources are produced for the purpose of transmitting information to be used in the future.
Documents classified as primary sources are constitutions, charters, laws, court deci- sions, official minutes or records, autobiographies, letters, diaries, geneal- ogies, census information, contracts, deeds, wills, permits, licenses, affi- davits, depositions, declarations, proclamations, certificates, lists, handbills, bills, receipts, newspaper and magazine accounts, advertisements, maps, diagrams, books, pamphlets, catalogs, films, pictures, paintings1 inscrip- tions, recordings, transcriptions, and research reports.
Remains or Relics.

Remains or relics are objects associated with aperson, group, or period.

Fossils, skeletons, tools, weapons, food, utensils,cloth- ing, buildings, furniture, pictures, paintings, coins, and art objects are examples of those relics and remains that were not deliberately intended 94 Part Two Research Methods for use in transmitting information or as records.

However, these sources may provide clear evidence about the past.
The contents of an ancient burial place, for instance, may reveal a great deal of information about the way of life of a people—their food, clothing, tools, weapons, art, religious beliefs, means of livelihood, and customs.

Similarly, the contents of an institution for the mentally ill or mentally retarded can reveal a good deal of information about the way the clients were treated, including the quality of food, the opportunity for work and recreational activities, and whether abuses regularly occurred.
Oral Testimony.

Oral testimony is the spoken account of a witness of, or participant in, an event.

This evidence is obtained in a personal interview and may be recorded or transcribed as the witness relates his or her experiences.

Primary Sources of Educational Data Many of the old materials mentioned in the preceding section provide primary evidence that may be useful specifically in studying the history of education.

A number are listed here.

Official Records and Other Documentary Materials.
Included in this cat- egory are records and reports of legislative bodies and state departments of public instruction, city superintendents, principals, presidents, deans, department heads, educational committees, minutes of school boards and boards of trustees, surveys, charters deeds, wills, professional and lay periodicals, school newspapers, annuals, bulletins, catalogs, courses of study, curriculum guides, athletic game records, programs (for graduation, dramatic, musical, and athletic events), licenses, certificates, textbooks, examinations, report cards, pictures, drawings, maps, letters, diaries, au- tobiographies, teacher and pupil personnel files, samples of student work, and recordings.
Oral Testimony.

Included here are interviews with administrators, teach- rs and other school employees, students and relatives, school patrons or lay citizens, and members of governing bodies.

Relics.

Included in this category are buildings, furniture, teaching ma- terials, equipment, murals, decorative pictures, textbooks, examinations, and samples of student work.
Chapter 4 Historical Research 95 Secondary Sources of Data Secondary sources are the reports of a person who relates the testimony of an actual witness of, or participant in, an event.

The writer of the secondary source was not on the scene of the event, but merely reports what the person who was there said or wrote.

Secondary sources of data are usually of limited worth for research purposes because of the errors that may result when information is passed on from one person to another.
Most history textbooks and encydopedias are examples of secondary sources, for they are often several times removed from the original, firsthand account of events.

Some types of material may be secondary sources for some purposes and primary sources for another.

For example, a high school textbook in American history is ordinarily a secondary source.
But if one were making a study of the changing emphasis on nationalism in high school American history textbooks, the book would be a primary document or source of data.

Historical Criticism It has been noted that the historian does not often use the method of direct observation.

Past events cannot be repeated at will.


Because the historian must get much of the data from the reports of those who wit- nessed or participated in these events, the data must be carefully analyzed to sift the true from the false, irrelevant, or misleading.

Trustworthy, usable data in historical research are known as historical evidence.

That body of validated information can be accepted as a trust- worthy and proper basis for the testing and interpretation of a hypothesis.
Historical evidence is derived from historical data by the process of crit- idsm, which is of two types: external and internal.

External Criticism External criticism establishes the authenticity or genuineness of data.

Is the relic or document a true one rather than a forgery, a counterfeit, or a hoax?

Various tests of genuineness may be employed.


Establishing the age or authorship of documents may require intricate tests of signature, handwriting, script, type, spelling, language usage, documentation, knowledge available at the time, and consistency with what is known.

It may involve physical and chemical tests of ink, paint, paper, parchment, cloth, stone, metals, or wood.
Are these elethents consistent with known facts about the person, the knowledge available, 96 Part Two Research Methods and the technology of the period in which the remain or the document originated?

Internal Criticism After the authenticity of historical documents or relics has been estab- lished, there is still the problem of evaluating their accuracy or worth.

Although they may be genuine, do they reveal a true picture?

What of the writers or creators?


Were they competent, honest, unbiased, and - actually acquainted with the facts, or were they too antagonistic or too sympathetic to give a true picture?

Did they have any motives for dis- torting the account?

Were they subject to pressure, fear, or vanity?

How long after the event did they make a record of their testimony, and were they able to remember accurately what happened?

Were they in agreement with other competent witnesses?
These questions are often difficult to answer, but the historian must be sure that the data are authentic and accurate.

Only then may he or she introduce them as historical evidence, worthy of serious consideration.

The following examples describe ways in which evidence is tested for authenticity.

The first is an example of historical criticism of a scholarly type, carried on by scientists and biblical scholars, in which historic doc- uments were proven to be genuine.

The Dead Sea Scrolls.


One of the most interesting and significant his- torical discoveries of the twentieth century was the finding of the Dead Sea Scrolls.

This collection of ancient manuscripts was discovered in 1947 by a group of Bedouins of the Ta'amere tribe.

Five leather scrolls were found, sealed in tall earthenware jars in the Qumran caves near Aim Feshkha, on the northwest shore of the Dead Sea (Davies, 1956).
The Bedouins took the scrolls to Metropolitan Mar Athanesius Ye- shue Samuel, of St. Mark's monastery in Jerusalem, who purchased them after discovering that they were written in ancient Hebrew.

A consultation with biblical scholars confirmed the fact that they were very old and possibly valuable.

They were later purchased by Professor Sukenik, an archaeologist of Hebrew University at Jerusalem, who began to translate them.
He also had portions of the scrolls photographed to send to other biblical scholars for evaluation.

Upon examining some of the photographs, Dr. William F. Albright of Johns Hopkins University pronounced them "the greatest manuscript discovery of modern times."
A systematic search of the Wadi Qumran area caves in 1952 yielded other leather scrolls, many manuscript fragments, and two additional scrolls of copper that were so completely oxidized that they could not be Chapter 4 Histoncal Research 97 unrolled without being destroyed.
By 1956, scientists at the University of Manchester, England, had devised a method of passing a spindle through the scrolls, spraying them with aircraft glue, baking them, and then saw- ing them across their rolled-up length to yield strips which could be photographed.

The origin, the agt', and the historic value of the scrolls have been questioned.
By careful and systematic external and internal criticism, how- ever, certain facts have been established and are quite generally accepted by biblical scholars and scientists.

The scrolls are very' old, probably dating back to the first century A.D.

They are written in ancient Hebrew and probably originated in a pre- Christian monastery of one of the Jewish sects.
The writings contain two versions (one complete and one incomplete) of the Book of Isaiah, a commentary or Midrah on the Book of Habakkuk, a set of rules of the ancient Jewish monastery, a collection of about twenty psalms similar to those of the Old Testament, and several scrolls of apocalyptic writings, similar to the Book of Revelation.

The contents of the copper scrolls and other fragments have now been translated.
It is possible that more scrolls and writings may be dis- covered in the area, and it is likely that these ancient documents may throw new light on the Bible and the origins of Christianity.

It is interesting to note how these documents were authenticated, dated, and evaluated by: 1.

Paleography, an analysis of the Hebrew alphabet forms used.

These written characters were similar to those observed in other documents known to have been written in the first century.

2.
2.

A radiocarbon test of the age of the linen scroll covering conducted by the Institute of Nuclear Research at the University of Chicago.

All organic matter contains radiocarbon 14, which is introduced by the interaction of cosmic rays from outer space with the nitrogen in the earth's atmosphere.

The radioactivity constantly introduced throughout the life of the specimen ceases at death and disintegrates at a constant known rate.
At the time of death, all organic matter yields 15.3 disintegrations per minute per gram of carbon content.

The number of disintegrations is reduced by one-half after 5568 years, plus or minus 30 years.

By measuring disintegrations by using a Geiger-type counter, it is possible to estimate the age of specimens within reasonable limits of accuracy.

Through use of this technique, the date of the scrolls was estimated at A.D. 33, plus or minus 200 years.

3.
3.

Careful examination of the pottery form in which the scrolls were sealed.

These jars, precisely shaped to fit the manuscripts, were the type commonly used during the first century.

98 Part Two Research Methods 4.

Examination of coins found in the caves with the scrolls.

These dated Roman corns provided convincing evidence of the age of the scrolls.

5.

Translation of the scrolls.

When translated, the scrolls compared to other writings, both biblical and nonbiblical, of known antiquity.
Although external criticism has now produced convincing evidence of the genuineness and age of the Dead Sea Scrolls, internal criticism of their validity and relevance will be pursued by biblical scholars for many years to come and may provide many new hypotheses concerning biblical writings and the early history of Christianity and the pre-Christian Jewish sects.
Modern approaches to historical research have applied advanced technology, emphasizing the usefulness of both qualitative and quanti- tative data.

As we have seen in this example, researchers employed the radiocarbon 14 test to verify the authenticity of the scrolls.

The next example illustrates the use of the computer in archaeological and historical research.

Stonehenge (Hahging Stones).


For centuries historians and archaeolo- gists have debated the origin and purpose of Stonehenge, a curious ar- rangement of stones and archways, each weighing more than 40 tons, located on the Salisbury Plain about 90 miles southwest of London.

From the beginning of recorded history, writers have speculated about the stones.

Their construction and arrangement have been attributed to many tribes and national groups who invaded or inhabited England.
Modern radio- carbon dating of a deer antler found in the stone fill seems to date their erection at about 1900 to 1600 B.C.

Their purpose has been explained in many legends—a city of the dead, a place of human sacrifice, a temple of the sun, a pagan cathedral, and a Druid ceremonial place.
More recently some scientists and historians have suggested that Stonehenge was a type of astronomical computer calendar used by early Britons who were apparently sophisticated enough to compute the posi- tion of the sun and the moon at their various stages.

Using an IBM 704 computer, Gerald S. Hawkins, an astronomer at the Smithsonian Astro- physical Observatory at Cambridge, Massachusetts, entered into the com- puter 240 stone alignments, translated into celestial declinations.
Accom- plishing in less than a minute a task that would have required more than 4 months of human calculator activity, Qie computer compared the align- ments with the precise sunlmoon extreme positions as of 1500 B.C.

and indicated that they matched with amazing accuracy.
Hawkins suggests that the stone arrangements may have been cre- ated for several possible reasons: They made a calendar that would be useful for planting crops; they helped to create and maintain priestly power, by enabling the priest to call out the people to see the rise and setting of the midsummer sun and moon over the heel stone and mid- Chapter 4 Historical Research 99 winter sunset through the great trilithon; or possibly they served as an intellectual exercise.
Hawkins concludes: In any case, for whatever reasons those Stonehenge buildersbuilt as they did, their final completed creation was a marvel.

As intricately aligned as an interlocking series of astronomical instruments (which indeed it was) and yet architecturally perfectly simple, in function subtle and elaborate, in appear- nnce stark, imposing, awesome, Stonehenge was a thing ofsurpassing in- genuity of design, variety of usefulness and grandeur—in concept and construction an eighth wonder of the world.
(Hawkins & White, 1966, pp.

117—118) This interesting historical-archaeological controversy illustrates the use of sophisticated computer techncilogy to test a hypothesis.

Examples of Topics for Educational Historical Study Brickman (1982) provids a number of possible topics by typesof historical research in education and an example for each.

We repeat his list here: 1.

PERIOD.

"Education during the First Half of the Fifteenth Century."

2.

GEOGRAPHICAL REGION.


"German Education under Frederick the Great."

in the Nineteenth Century."

3.

EDUCATIONAL LEVEL.

"The Secondary Schools of Ancient Rome."

4.

INSTITUTION.

"Amherst College in the Nineteenth Century."

5.

BIOGRAPHY.

"Bronson Alcott as an Educator."

Biographical detail, as such, is of less importance for term-report purposesthan an ex- position of the man's educational ideas, work, and influence.

6.

INNOVATIONS.

"Three Decades of Audio-Visual Education."

7.

PHILOSOPHY.
7.

PHILOSOPHY.

"Changing Conceptsof American Higher Education 8.

METHODOLOGY.

"Herbartianism in American Educational Practice."

9.

CURRICULUM.

"The Subject of Rhetoric in Ancient Greece."

10.

PERSONNEL.

"The Role of the Teacher duringthe Renaissance."

11.

CHILDREN.

"Changing Attitudes toward Corporal Punishment of Attendance Laws in Prussia Children in the United States."

12.

LEGISLATION.

"Compulsory School During the Eighteenth Century."

13.

MATERIALS.
13.

MATERIALS.

"The Evolution of American School Readers, 1700— 14.

NONSCHOOL AGENCIES.

"The Development of the Library in 1830."

Nineteenth-century America."

15.

ORGANIZATIONS.

"History of the Public School Society of New York."

100 Part Two Research Methods 16.

FINANCE.

"Methods of School Taxation in Pennsylvania, 1820—1880."

17.

ARCHITECTURE.

'The Evolution of the School Building in Illinois."

18.

ADMINISTRATION.

"The Rise of the State Superintendency of Schools."

19.

LITERATURE.
19.

LITERATURE.

"A Century of Educational Periodicals in the United States."

20.

INFLUENCE.

"The Influence of Rousseau upon Pestalozzi."

21.

REPUTATION.

"The Reception of Horace Mann's Educational Ideas in Latin America."

22.

COMPARISON.

"A Comparative Study of Renaissance Theories of the Education of the Prince."

23.

TEXTBOOK ANALYSIS.

"A Study of the Treatment of Primitive Education in Textbooks in Educational History" (pp.
5_6)1 Obviously, these topics are too broad for a student project, and in some cases, would probably take most of a career.

The processes of de- limitation and hypothesis formation are needed to make-these topics useful.

Writing the Historical Report No less challenging than research itself is the writing of the report, which calls for creativity in addition to the qualities of imagination and resource- fulness already illustrated.
It is an extremely difficult task to take often seemingly disparate pieces of information and synthesize them into a meaningful whole.

Research reports should be written in a dignified and objective style.

However, the historian is permitted a little more freedom in reporting.
Hockett suggests that "the historian is not condemned to a bald, plain, unattractive style" and that "for the sake of relieving the monotony of statement after statement of bare facts, it is permissible, now and then, to indulge in a bit of color."

He concludes, however, by warning that "above all, embellishments must never become a first aim, or be allowed to hide or distort the truth" (Hockett, 1948, p. 139).
An evaluation of graduate students' historical-research projects gen- erally reveals one or more of the following faults: 1.

Problem too broadly stated 2.

Tendency to use easy-to-find secondary sources of data rather than sufficient primary sources, which are harder to locate but usually more trustworthy 3.

Inadequate historical criticism of data because of.

failure to establish authenticity of sources and trustworthiness of data.
For example, there is often a tendency to accept the truth of a statement if sev- 'Used with the permission of Emeritus, Inc., publisher.

Chapter 4 HistorIcal Research 101 era!

observers agree.

It is possible that one may have influenced the other or that all were influenced by the same inaccurate source of information.

4.
Poor logical analysis resulting from: a. Oversimplification—failure to recognize the fact that causes of events are more often multiple and complex than single and simple b. Overgeneralization on the basis of insufficient evidence, and false reasoning by analogy, basing condusions upon superficial similarities of situations c. Failure to interpret words and expressions in the light of their d. Failure to distinguish between significant facts in a situation accepted meaning in an earlier period and those that are irrelev&nt or unimportant e. Failure'to consider the documents in the context of their time, that is, the existing beliefs, biases, and so forth.
5.

Expression of persona!

bias, as revealed by statements lifted out of context for purposes of persuasions assuming too generous or un- critical an attitude toward a person or idea (or being too unfriendly or critical), excessive admiration for the past (sometimes known as the "old oaken bucket" delusion), or an equally unrealistic admira- tion for the new or contemporary, assuming that all change repre- sents progress 6.
Poor reporting in a style that is dull and colorless, too flowery or flippant, too persuasive or of the "soap-box" type, or improper in usage It is apparent that historical research is difficult and demanding.The gathering of historical evidence requires longhours of careful examination of such documents as court records, records of legislative bodies, letters, diaries, official minutes of organizations or other primary sources of-data.
Historical research may involve traveling to distant places to examine the necessary documents or relics.

In fact, any significant historical study would make demands that few students have the time, financial resources, patience, or expertise to meet.

For these reasons, good historical studies are not often attempted for the purpose of meeting academic degree requirements.
Summary History, the meaningful record of human achievement, helps us to understand the present and, to some extent, to predict the future.

Historical research is the application of scientific method to the description and analysis of past events.

102 Part Two Research Methods Historians ordinarily draw their data from the observations and experience of others.
Because they are not likely to have been at the scene of the event, they must use logical inferences to supplement what is probably an incomplete account.

Primary sources may be "unconscious" testimony, not intended to be left as a record—relics or remains such as bones, fossils, clothing, food, utensils, weapons, coins, and art objects are useful.
Conscious testimony, in the form of records or documents, is another primary source of information—examples are constitutions, laws, court decisions, official minutes, autobiographies, letters, contracts, wills, cer- tificates, newspaper and magazine accounts, films, recordings, and research reports.

Historical criticism is the evaluation of primary data.
External criticism is con- cerned with the authenticity or genuineness of remains or documents, and internal criticism is concerned with the trustworthiness or veracity of materials.

The accounts of the Dead Sea Scrolls and Stonehenge illustrate the processes of historical criticism.

The historical research studies of graduate students often reveal serious limi- tations.
Frequently encountered are such faults as stating the problem too broadly, inadequate primary sources of data, unskillful historical crfticism, poor logical analysis of data, personal bias, and ineffective reporting.

Exeicises 1.

Write a proposal for a historical study in a local setting.

You may select a community, school, church, religious or ethnic group, or individual.
State an appropriate title, present your hypothesis, indicate the primary sources of data that you would search, and tell how you would evaluate the au- thenticity and validity of your data.

2.
2.

Select a thesis of the historical type from the university library and analyze it in terms of a. hypothesis proposed or questions raised b. primary and secondary sources of data used c. external and internal criticism employed d. logical analysis of data relationships e. soundness of conclusions f. documentation References Abington School District v. Schempp, 374 U.S. 203 (1963).

AYDELOTrE, W. 0.

(1963).

Notes on the problem of historical generalization.

In L. Gottschalk (Ed.


), Generalization in the writing of history.

Chicago: University of Chicago Press.

BEsT, J. H.

(Ed.).

(1983).

Historical inquiry in education: A research agenda.

Washington, D.C.: American Educational Research Association.

BILLINGTON, R. A.

(1975).

Allan Nevins on history.

New York: Charles Scribner's Sons.

Chapter 4 Histoncal Research 103 BRICKMAN, W. W. (1982).

Educational historiography: Tradition, theory, and technique.

Cherry Hill, NJ: Emeritus.

CREIVUN, L. (1961).


The transformation of the school: Progressivism in American education.

New York: Alfred A. Knopf.

DAVIES, A. P. (1956).

The meaning of the Dead Sea Scrolls.

New York: New American Library of World Literature.

FINLEY, M. I.

(1963).

Generalizations in ancient history.

In L. Gottschalk (Ed.

), Generalization in the writing of history.

Chicago: University of Chicago Press.

GOTTSCHALK, L. R. (1950).

Understanding history.

New York: Alfred A. Knopf.


GOTTSCHALK, L. R. (1963).

Categories of historical generalizations.

In L. Gottschalk (Ed.

), Generalization in the writing of history.

Chicago: University of Chicago Press.

HAWKINS, G. S. & WHITE, J.

B.

(1965).

Stonehenge decoded.

Garden City, NY: Doubleday.

HocKETr, H. C. (1948).

Introduction to research in American history.

New York: Macmillan.

LANNIE, V. L. & DIETHORN, B. C. (1968).


For the honor and glory of God: The Philadelphia Bible riots of 1840.

History of Education Quarterly, 8, 44—106.

NEVINS, A.

(1962).

The gateway to history (Rev.

ed.).

Boston: Raytheon Education Co. SEARCH, P. W. (1901).

An ideal school: Looking forward.

New York: Appleton-Century- Crofts.

THURSFIELD, R. E. (1945).

Henry Barnard's American Journal of Education.

Baltimore: Johns Hopkins University Press.

WARREN, D. R.

(Ed.).

(1978).


History, education, and public policy: Recovering the American past.

Berkeley: McCutchan.

Additional Readings BARZUN, J.

& GRAFF, H. F. (1977).

The modern researcher (2nd ed.).

New York: Harcourt Brace Jovanovich.

BRiCKMAN, W. W. (1949).

Guide to research in educational history.

New York: New York University Bookstore.

CARR, H. (1962).

What is history?

New York: Alfred A. Knopf.

EDGERTON, H. E. (1960).

Stonehenge: New light on an old riddle.


National Geo- GARRAGHAN, C. J.

(1946).

A guide to historical method.

New York: Fordham University graphic, 117, 846—866.

Press.

GorrscHAuç L. R., (Ed.).

(1963).

Generalization in the writing of history.

Report of the Committee of Historical Analysis of Social Science Research Council.

Chicago: Uni- versity of Chicago Press.

HoCKETr, H. C. (1955).

The critical method in historical research and writing.

New York: Macmillan.

KLEIN, A.

(Ed.).

(1955).


Grand deception.

Philadelphia: J.

B. Lippincott.

MOEHLMAN, A., ST AL.

(1969).

A guide to computer assisted historical research in American education.

Austin: The Center for the History of Education, University of Texas.

NORDIN, V. D. & TURNER, W. L. (1980).

More than segregation academies: The growing Protestant fundamentalist schools.

Phi Delta Kappan, 61, 391—394.

PETERSON, C. S. (1946).

America's Rune stone.

New York: Hobson Book Press.


Part Two Research Methods STEBBINS, C. L. (1960).

Here I shall finish my voyage: The death site of Father Jacques Marquette.

Orema, MI: Solle's Press.

ZIRKEL, P. A.

(1979).

A digest of Supreme Court decisions affecting education.

Bloom- ington, IN: Phi Delta Kappa.

5 Descriptive Studies: Assessment, Evaluation, and Research A DESCRIPTIVE sruo' describes and interprets what is.
It is concerned with conditions or relationships that exist, opinions that are held, processes that are going on, effects that are evident, or trends that are developing.

It is primarily concerned with the present, although it often considers past events and influences as they relate to current conditions.

The term descriptive study masks an important distinction, for not all descriptive studies fall into the category of research.
In fact, of the three kinds of descriptive studies only one is actually research.

In Chapter 1 the similarities and differences between assessment, evaluation, and re- search were briefly discussed.

We will restate those similarities and dif- ferences in this discussion of descriptive studies.

Also, this chapter will be confined to descriptive studies that use quantitative methods.

Quali- tative studies wifi be discussed in Chapter 8.
Assessment describes the status of a phenomenon at a particular time.

It describes without value judgment a situation that prevails; it attempts no explanation of underlying reasons and makes no recommendations for action.

It may deal with prevailing opinion, knowledge, practices, or con- ditions.

As it is ordinarily used in education, assessment describes the progress students have made toward educational goals at a particular time.
For example, in the National Assessment of Education Progress program, the data are gathered by a testing program and a sampling procedure in 105 Part Two Research Methods such a way that no individual is tested over the entire test battery.

It is not designed to determine the effectiveness of a particular process or program but merely to estimate the degree of achievement of a large number of individuals who have been exposed to a great variety of ed- ucational and environmental influences.
It does not generally provide recommendations, but there may be some implied judgment on the sa- tisfactoriness of the situation or the fulfillment of society's expectations.

Evaluation is a process used to determine what has happened during a given activity or in an institution.

The purpose of evaluation is to see if a given program is working, if an institution is successful according to the goals set for it, or if the original intent is being successfully carried out.
To assessment, evaluation adds the ingredient of value judgment of the social utility, desirability, or effectiveness of a process, product, or program, and it sometimes includes a recommendation for some course of action.

School surveys are usually evaluation studies; educational prod- ucts and programs are examined to determine their effectiveness in meet- ing accepted objectives, often with recommendations for constructive action.
Descriptive research, sometimes known as nonexperimental or corre- lational research, deals with the relationships between variables, the test- ing of hypotheses, and the development of generalizations, principles, or theories that have universal validity.

It is concerned with functional re- lationships.

The expectation is that if variable A is systematically associated with variable B, prediction of future phenomena may be possible and the results may suggest additional or competing hypotheses to test.
In carrying out a descriptive research project, in contrast to an ex- periment, the researcher does not manipulate the variable, decide who receives the treatment, or arrange for events to happen.

In fact, the events that are observed and 'described would have happened even though there had been no observation or analysis.

Descriptive research also involves events that have already taken place and may be related to a: present condition.
The method of descriptive research is particularly appropriate in the behavioral sciences because many of the types of behavior that interest the researcher cannot be arranged in a realistic setting.

Introducing sig- nificant variables may 1e harmful or threatening to human subjects.

Ethical considerations often preclude exposing human subjects to harmful ma- nipulation.
For exan,le, it would be unthinkable for an experimenter to randomly decide whO should smoke cigarettes and who should not smoke them for the purpose of studying the effect of smoking on cancer, heart disease, or other ifinesses thought to be caused by cigarette smoke.

Sim- ilarly, to deliberately arrange auto accidents, except when manikins are used, in order to evaluate the effectiveness of seat belts or other restraints in preventing serious injury would be absurd.
Although many experimental studies of human behavior can be ap- propriately carried out both in the laboratory and in the field, the pre- Chapter 5 Descriptive Studies 107 vailing research method of the behavioral sciences is descriptive.

Under the conditions that naturally occur in the home, the c1assroom the rec- reatidnal center, the office, or the factory, human behavior can be system- atically examined and analyzed.
The many similarities between these types of descriptive studies may have tended to cloud the distinctions between them.

They are all char- acterized by disciplined inquiry, which requires expertise, objectivity, and careful execution.

They all develop knowledge, adding to what is already known.

They use similar techniques of observation, description, and anal- ysis.
The differences between them lie in the motivation of the investi- gator, the treatment of the data, the nature of the possible conclusions, and the use of the findings.

The critical distinctions are that the three types of studies have different purposes and, therefore, approach the problem differently and that only descriptive research studies lead to generalizations beyond the given sample and situation.

It is also possible for a single study to have multiple purposes.
For instance, a study may evaluate the success/failure of an innovative pro- gram and also include sufficient controls to qualify as a descriptive research study.

Similarly, an assessment study may include elements that result in descriptive research too.

Examples of these three types of descriptive studies are presented next.
It is important to keep in mind that, while these examples are presented to illustrate each individual type of study (assessment, evalu- ation, or descriptive research), they are not mutually exclusive.

That is, for example, while we use surveys to illustrate assessment and evaluation studies, surveys also are used in descriptive research studies.
Similarly, although we use causal-comparative studies to illustrate their major use, descriptive reearch, this type of design also can be used in an assessment or evaluation study.

Assessment Studies The Survey The survey method gathers data from a relatively large number of cases at a particular time.

It is not concerned with characteristics of individuals as individuals.

It is concerned with the generalized statistics that result when data are abstracted from a number of individual cases.
It is essen- tially cross-sectional.

Ninety-four percent of American homes have at least one television set.

About three out of five students who enter the American secondary school remain to graduate.

Fifty-six percent of adult Americans voted in the 1972 presidential election.

The average American consumes about 103 pounds of refined sugar annually.

The ratio of femalebirths to male births 108 Part Two Research Methods in the United States in 1974 was 946 to 1000.
The population of Illinois, according to the 1980 census, was 11,426,518.

Data like these result from many types of surveys.

Each statement pictures a prevailing condition at a particular time.

In analyzing political, social, or economic conditions, one of the first steps is to get the facts about the situation—_or a picture of conditions that prevail or that are developing.

These data may be gathered from surveys of the entire population.
Others are inferred from a study of a sample group, carefully selected from the total population.

And at times, the survey may describe a limited population which is the only group under consideration.

The survey is an important type of study.

It must not be confused with the mere clerical routine of gathering and tabulating figures.

It in- volves a clearly defined problem and definite objectives.
Itrequires expert and imaginative planning, careful analysis and interpretation of the data gathered, and logical and skillful reporting of the findings.

Social Surveys In the late 1930s a significant social survey was directed by the Swedish sociologist Gunnar Myrdal and sponsored by the Carnegie Foundation.
Myrdal and his staff of researchers made a comprehensive analysis of the social, political, and economic life of black persons in the United States, yielding a great mass of data on race relations in America (Myrdal, 1944).

The late Alfred Kinsey (1948) of Indiana University madea compre- hensive survey of the sexual behavior of the human male, based on data gathered from more than 12,000 cases.

His second study (Kinsey, 1953) of the behavior of the human female followed later.
Although these studies have raised considerable controversy, they represent a scientific approach to the study of an important social problem and have many implications for jurists, legislators, social workers, and educators.

Witty (1967) has studied the television viewing habits of school chil- dren and has published annual reports on hisinvestigations.
These studies were conducted in the Chicago area and indicate the amount of time devoted to viewing and the program preferences of elementary and sec- ondary students, their parents, and their teachers.

Witty attempted to relate television viewing to intelligence, reading habits, academic achieve- ment, and other factors.

Shaw and McKay (1942) conducted a study of juvenile delinquency in Chicago, yielding significant data on the nature and extent of delin- quency in large urban communities.
Lang and Kahn (1986) examined special education teacher estimates of their students' criminal acts and crime victimizations.

The dataindicated Chapter 5 Descriptive Studies 109 that special education students seem to be victimized in the same way as others but to a greater degree.

This preliminary study led to Lang's (1987) dissertation, an experiment aimed at reducing the rate of victimization of mentally retarded students.
The National Safety Council conducts surveys on the nature, extent, and causes of automobile accidents in all parts of the United States.

State high school athletic associations conduct surveys on the nature and extent of athletic injuries in member schools.

Public Opinion Surveys In our culture, where so many opinions on controversial subjects are expressed by well-organized special interest groups, it is important to find out what the people think.
Without a means of polling public opinion, the views of only the highly organized minorities are effectively presented through the printed page, radio, and television.

How do people feel about legalized abortion, the foreign aid pro- gram, busing to achieve racial integration in the public schools, or the adequacy of the public schools?

What candidate dothey intend to vote for in the next election?

Such questions can be partially answered by means of the public opinion survey.
Many research agencies carry on these surveys and report their findings in magazines and in syndicatedarticles in daily newspapers.

Since it would be impracticable or even impossible to get an expres- sion of opinion from every person, sampling techniques are employed in such a way that the resulting opinions of a limited number of people can be used to infer the reactions of the entire population.
The names Gallup, Roper, and Harris are familiar to newspaper readers in connection with public opinion surveys.

These surveys of opin- ion are frequently analyzed and reported by such classifications as age groups, sex, educational level, occupation, income level, political affilia- tion, or area of residence.

Researchers are aware of the existence of many publics, or segments of the public, who may hold conflicting points of view.
This further analysis of opinion by subgroups adds meaning to the analysis of public opinion in general.

Those who conduct opinion polls have developed more sophisticated methods of determining public attitudes through more precise sampling procedures and by profiting from errors that plagued early efforts.

In prediction of voter behavior several well-known polls have proved to be poor estimators of election results.
As described earlier, in 1936, a prominent poil with a sample of over 2 million voters predicted the election of Alfred Landon over President Roosevelt by nearly 15 percentage points.

The primary reason for this 110 Part Two Research Methods failure in prediction was the poll's sampling procedure.

The sample was taken from telephone directories and automobile registration lists which did not adequately represent poor persons, who in this election voted in unprecedented numbers.
Gallup, on the other hand, correctly predicted that Roosevelt would win, using a new procedure, quota sampling, in which various components of the population are included in the sample in the same proportion that they are represented in the population.

However, there are problems with this procedure, which resulted in Gallup and others being wrong in 1948 (Babbie, 1973).

In the 1948 election campaign most polls predicted the election of Thomas E. Dewey over Président Truman.
This time the pollsters were wrong, perhaps partly because of the sampling procedure and partly because the polls were taken too far before the election despite a trend toward Truman throughout the campaign.

Had the survey been made just prior to election day, a more accurate prediction might have resulted.

In addition, most survey researchers (including pollsters) use probability sampling today instead of quota sampling.
This results in all members of a given population having the same probability of being chosen for the sample.

In the 1968 election the predictions of both Gallup and Harris polls were less than 2 percentage points away from Richard Nixon's actual percentage of the vote with samples of only about 2000 voters.

This ac- curacy was possible due to the use of probability sampling (Babbie, 1973).
In addition to the limitations suggested, there is a hazard of careless responses, given in an ofthand way, that are sometimes at variance with the more serious opinions that are expressed as actual decisions, Since 1969 the Gallup organization has conducted an annual nation- wide opinion poll of public attitudes toward education.
Using a stratified cluster sample of 1500 or more individuals over 18 years of age, the data have been gathered by personal interviews from seven geographic areas and four size-of-community categories.

The responses were analyzed by age, sex, race, occupation, income level, political affiliation, and level of education.
A wide range of problem areas has been considered: In the 1975 poll such problem areas confronting education were the use of drugs and alcohol; programs on drugs or alcohol; behavior standards in the schools; policies on suspension from school; work required of students, including amount of homework; requirements for graduation from high school; federal aid to public schools; the nongraded school program; open education; alternative schools; job training; right of teachers to strike; text- book censorship; and the role of the school principal as part of manage- ment (Elam, 1979).
The 1982 poll indicated the public's clear support for education.

Education was ranked first among twelve funding categories con- sidered in the survey—above health care, welfare, and military defense— with 55 percent selecting public education as one of their first three choices (Nation at Risk, 1983, p. 17).
Chapter 5 Descriptive Studies 111 National Assessment of Educational Progress The National Assessment of Educational Progress was the first nationwide, comprehensive survey of educational achievement to be conducted in the United States.

Originally financed by the Carnegie Foundation and the Fund for the Advancement of Education, with a supporting grant from the U.S. Office of Education, the Committee on Assessing the Progress of Education (CAPE) began its first survey in the spring Of 1969.
It gathered achievement test data by a sampling process such that no one individual was tested over the whole test battery or spent more than 40 minutes in the process.

Achievement was assessed every 3 years in four age groups (9, 13, 17, and young adults between 26 and 35), in four geographical areas (Northeast, Southeast, Central, and West), for four types of com- munities (large city, urban fringe, rutal, and small city), and for several socioeconomic levels and ethnic groups.
Achievement has been assessed in art, reading, writing, social stud- ies, science, mathematics, literature, citienship, and music.

Comparisons between individuals, schools, or school systems have never been made.

The agency now conducting the assessment is the National Assess- ment of Educational Progress (NAEP), financed by the National Center for Educational Statistics, a division of the Department of Education.
Pe- riodic reports are provided for educators, interested lay adults, and for the general public through press releases to periodicals.

International Assessment The International Association for the Evaluation of Educational Achieve- ment, with headquarters in Stockholm, Sweden, has been carrying on an assessment program in a number of countries since 1964.
The first study, The International Study of Achievement in Mathematics (Torsten, 1967), com- pared achievement in twelve countries: Austria, Belgium, England, Fin- land, France, West Germany, Israel, Japan, the Netherlands, Scotland, Sweden, and the United States.

Short answer and multiple choice tests were administered to 13-year-olds and to students in their last year of the upper secondary schools, prior to university entrance.

More than 132,000 pupils and 5000 schools were involved in the survey.
Japanese students excelled above all others, regardless of their socioeconomic status, and United States students ranked near the bottom.

Although the purpose of assessment is not to compare school sys- tems, the data lead observers to make such comparisons.
Critics of the first assessment pointed out the inapproprtateness of comparing17-year- olds in the United States, where more than 75 percent are enrolled in secondary schools, with 17-year-olds in other countries in which those 112 Part Two Research Methods enrolled in upper secondary schools comprise a small, highly selected population.
More recent assessments reveal that, although 10 percent of the top United States students surpassed similar groups in all other countries in reading, in science they occupied seventh place (Hechinger & Hechinger, 1974).

Other assessments have been carried out, and the number of par- ticipating countries has been increased to twenty-two.
Activity Analysis The analysis of the activities or processes that an individual is called upon to perform is important, both in industry and in various types of social agencies.

This process of analysis is appropriate in any field of work and at all levels of responsibility.

It is useful in the industrial plant; where needed skills and competencies of thousands of jobs are carefully studied, jobs ranging in complexity from uhskilled laborer to plant manager.
In school systems the roles of the superintendent, the principal, the teacher, and the custodian have been carefully analyzed to discover what these individuals do and need to be able to do.

The Commonwealth Teacher Training Study (Charters & Waples, 1929) described and analyzed the ac- tivities Of seetal thousand teachers and searched previous studies for opinions of writers on additional activities in which classroom teachers should engage.
A more recent study (Morris, Crowson, Porter-Gehrie, & Hurwitz, 1984) described and analyzed the activities of school principals.

This study is described in some detail later in Chapter 8 as an example of ethnographic research.

This type of analysis may yield valuable information that would prove useful in establishing 1.

The requirements for a particular job or position 2.

A program for the preparation or training of individuals for various jobs or positions 3.
An in-service program for improvement in job competence or for upgrading of individuals already employed 4.

Equitable wage or salary schedules for various jobs or positions.

Trend Studies The trend study is an interesting application of the descriptive method.
In essence it is based upon a longitudinal consideration of recorded data, indicating what has been happening in the past, what the present situation reveals, and on the basis of these data, what is, likely to happen in the Chapter 5 Descriptive StudIes 113 future.

For example, if the population in an area shows consistent growth over a period of time, one might predict that by a certain date in the future the population will reach a given level.
These assumptions are based upon the likelihood that the factors producing the change or growth will continue to exert their influence in the future.

The trend study points to conclusions reached by the combined methods of historical and de- scriptive analysis and is illustrated by Problems and Outlook of Small Private Liberal Arts Colleges: Report to the Congress of the United States by the Comptroller Genral (1978).
In response to a questionnaire sent to 332 institutions, 283 furnished data on facility construction, loan repayments, enrollment, the effectiveness of methods used to attract more students, financial aid pro- vided, and the general financial health of their institutions.
Based upon past and present experience, such influences as the growth of the community college, the effect of inflation on operating costs, tuition, living expenses and fees, and the decline in the number of college- age students were projected for the years 1978 to 1985 and their impact upon the financial stability of the small liberal arts college assessed.

The following trend study topics would also be appropriate: 1.

The Growing Participation of Women in Intercollegiate Sports Pro- grams 2.
Trends in the Methods of Financial Support of Public Education 3.

The Growth of Black Student Enrollment in Graduate Study Pro- 4.

The Minimum Competency Requirement Movement in American grams Secondary Education Evaluation Studies School Surveys What has traditionally been called a school survey is usually an assessment and evaluation study.
Its purpose is to gather detailed information to be used as a basis for judging the effectiveness of the instructional facilities, curriculum, teaching and supervisory personnel1 and financial resources in terms of best practices and standards in education.

For example, profes- sional and regional accrediting agencies send visitation teams to gather data on the characteristics of the institution seekingaccreditation.
Usually, following a self-evaluation by the school staff, the visiting educators eval- uate the institution's characteristics on the basis of agency guidelines.

Many city, township, and county school systems have been studied by this method for the purpose of determining status and adequacy.

These survey-evaluations are sometimes carried on by an agencyof a university Part Two Research Methods in the area.
Frequently a large part of the data is gathered by local edu- cators, with the unwersity staff providing direction and advisory services.

Proram Evaluation The most common use of evaluation is to determine the effectiveness of a program and sometimes the organization.

The school surveys described above are evaluations only of the organization.

Program evaluations, while often including the organization, focus primarily on program effectiveness results.
As Kaufman and Thomas (1980) put it: Evaluation deals with results, intended and unintended.

The questions asked during evaluation are usually the same.

Regardless of the context, evaluation seeks to answer the following questions: 1.

What are the goals and objectives of the organizgtion?

2.

What should be the goals and objectives of the organization?

3.

What results were intended by the program, project, activity, or organization?

4.
4.

What results were obtained liy the program, project, activity, or organization?

5.

What were the value and usefulness of the methods and means used to achieve the results?

6.

How well was the program, project, activity, or organization admin- istered and managed?

7.

What, if anything, about the program, project, activity, or organization should be changed?

8.

What, if anything, about the program, project, activity, or organization should be continued?

9.
9.

Should the organization, project, program, or activity exist at all?

These questions are basic.

They probe the issue of activities and the worth of these activities in terms of what they accomplished.

Evaluation is more than testing or measuring; it includes asking and answering basic questions about efforts and results.

(pp.
(pp.

1_2)1 Since in many cases of educational program evaluation what is being evaluated is a school-based program, we often implicitly know the goals and objectives of the organization, student learning.

In these cases the focus of the evaluation is on what is being taught, how it is being taught, and most importantly, whether the subject matter was learned.

Evalua- tions of this type can be conducted by the school district or outsiders hired to evaluate a specific program.
In addition, good teachers continuously are evaluating their teaching and whether and how their students are learning.

Thus, evaluations of school-based programs are concerned with 1Used with the permission of the authors.

Chapter 5 Descriptive Studies 115 two aspects of the evaluation process, formative and summafive evaluations.

Summative evaluation is what most people think of when they consider evaluation.
Its primary purposes are to determine grades, judgeteaching competence, and compare curricula (Bloom, Hastings1 & Madaus, 1971).

Teachers use this type of evaluation at regular intervals to give students grades, usually at the end of a set time period (eg., every few weeks or at the end of the semester) or after a certain portion of content has been covered (e.g., at the end of a chapter or set of information such as statistics).

Formative evaluation, on the other hand, is an ongoing continuous process.
"The main purpose of formative observations is to determine the degree of mastery of a given learning task and to pinpoint the part of the task not mastered.

Perhaps a negative description will be even clearer.

The purpose is not to grade or certify the learner; it is to help both the learner and teacher focus on the particular learningnecessary for move- ment toward mastery"(Bloom, Hastings, & Madaus, 1971, p. 61).
Thus the distinction between formative and summative evalution has to do with the purpose (e.g., grading or looking forwhat aspect of a task is not yet mastered) and the portion of the content involved (e.g., an entire course or asingle specific skill).

These two aspects of evaluation are not mutually exclusive and are often used in combination.

There are a number of evaluation models that evaluators use.

Some models are actually research approaches to evaluation.
Ruttman (1977) used the term evaluation research to describe evaluation procedures that use rigorous research methodology.

To accomplish evaluation with the rigor of research usually requires that the evaluator be involved in de- signing the evaluation before the program to be evaluated is implemented.

Usually, it is only in this way that the necessary controls can be put in place.

More often, evaluators are involved after the program is imple- mented.
While these evaluations are important and useful, they do not have the same degree of rigor required for research.

Three factorsshould be considered when one is selecting the evaluation model and data col- lection tools to be used to evaluate a particular program: the purpose of the evaluation, the needs of those for whom the evaluation is being per- formed (e.g., replication to other sites, modification of the existing pro- gram, fiscal decisions), and the objectives of the program being evaluated.
Quantitative methods, qualitative methods, or a combination may be used to collect the pertinent data needed for a complete evaluation.

Kaufman and Thomas (1980) describe, in detail, eight different models that can be used.

This is not meant to be a complete list, merely a diverse set of exemplars.

The reader interested in conducting an evaluation should read Kaufman and Thomas, or another book on the subject, before de- signing the evaluation.
An evaluation study of nontraditional training programs for math- ematics and science teachers (Kirby, Darling-Hammond, & Hudson, 1989), 116 Part Two Research Methods which used rigorous research methodology, will be used here as an il- lustration of evaluation research.
Three types of programs were compared: nontraditional recruitment techniques to recruit potential teachers from pools not usually tapped but which required the full certification program normally used in that state; alternative certification programs (usually consisting of fewer courses and/or practicum hours); and retraining pro- grams, which focused on helping teachers trained in other fields to obtain certification in science and/or mathematics.
Nine programs from a list of 64 were selected for in-depth study.

The nontraditional recruitment type of programs were then further divided into midcareer change and recent BA programs.

Questionnaires were mailed to participants and graduates of these nine programs and 481 completed responses (77%) were received.

The evaluation, and therefore the questionnaire, focused on.

six questions: What are the recruits' backgrounds?

Why did the recruits decide to go into mathematics and science teaching?
What are the graduates of the programs currently doing?

Are the recruits' actual teaching experiences similar to what they expected?

Do the recruits plan to continue teaching as a career?

Why did some decide not to teach after graduation from a program?

The results of the questionnaires and background information about the nine programs wer then analyzed to determine how successful the programs were.
Without going into great detail, the findings of Kirby, Darling- Hammond, and Hudson (1989) indicated that: not all of the programs were equally effective; the recruits "contain a higher than average rep- resentation of minority, female and older candidates" (p. 319); the "quality and intensity of preparation makes a difference in how well prepared recruits feel to teach," and "for all their promise, nontraditional teacher preparation programs cannot fully overcome other attributes of teaching that make recruitment and retention of teachers difficult" (p. 301).
With impending teacher shortages, which already exist in some fields such as mathematics and science, innovative programs to recruit people into teaching may be needed.

This study may be useful in directing teacher certification boards, colleges of education, and policy makers (e.g., leg- islators) as to how to most effectively recruit a new cohort of teachers.

Assessment and Evaluation in Problem Solving • In solving a problem or charting a course of action, several sorts of in- formation may be needed.
These data may be gathered through assess- ment and evaluation methods.

The first type of information is based upon present conditions.

Where are we now?

From what point do we start?

These data may be gathered Chapter 5 Descnptive Studies 117 by a systematic description and analysis of all the important aspects of the present situation.

The second type of information involves what we may want.

In what direction may we go?

What conditions are desirable or are considered to represent best practice?
This clarification of objectives or goals may come from a study of what we think we want, possibly resulting from a study of conditions existing elsewhere, or of what experts consider to be ade- quate or desirable.

The third type of information is concerned with how to get there.

This analysis may involve finding out about the experience of others whohave been involved in similar situations.

It may involve the opinions of expert, who presumably know best how to reach the goal.
Some studies emphasize only one of these aspects of problem solv- ing.

Others may deal with two, or even three, of the elements.

Although a study does not necessarily embrace all the steps necessary for the so- lution of a problem, it may make a valuable contribution by clarifying only one of the necessary steps—from description of present status to the charting of the path to the goal.

Assessment and evaluation methods may supply some or all of the needed information.
An example will illustrate how they can be used to help solve an educational problem.

Washington Township has a school building problem.

Its present educational facilities seem inadequate, and if present developments con- tinue, conditions may be much worse in the future.

The patrons and educational leaders in the community know that a problem exists, but they realize that this vague awareness does not provide a sound basis for action.

Three steps are necessary to provide such a basis.


The first step involves a systematic analysis of present conditions.

How many school-age children are there in the township?

How many children are of preschool age?

Where do they live?

How many classrooms now exist?

How adequate are they?

What is the average class size?

How are these present buildings located in relation to residential housing?

How adequate are the facilities for food, library, health, and recreational serv- ices?
What is the present annual budget?

How is it related to the tax rate and the ability of the community to provide adequate educational facilities?

The second step projects goals for the future.

What will the school population be in 5, 10, or 20 years?

Where will the children live?

How many buildings and classrooms will be needed?

What provisions should be made for special school services, for libraries, cafeterias, gymnasiums, and play areas to take care of expected educational demands?
Step three considers how to reach those goals which have been established by the analysis of step two.

Among the questions to be an- swered are the following: Should existing facilities be expanded or new buildings constructed?

If new buildings are needed, what kind should be provided?

Should schools be designed for grades 1 through 8, or should Part Two Research Methods 4 6-year elementary schools and separate 2- or 3-year junior high schools be provided?

How will the money be raised?


When and how much should the tax rate be increased?

When should the construction program get underway?

Many of the answers to the questions raised in step three will be arrived at by analysis of practices of other townships, the expressed opin- ions of school patrons and local educational leaders, and the opinions of experts in the areas of school buildings, school organization, community planning, and public finance.
Of course, this analysis of school building needs is but one phase of the larger educational problem of providing an adequate educational program for tomorrow's children.

There remain problems of curriculum, pupil transportation, and school personnel.

These problems can also be attacked by using similar methods of assessment and evaluation.

The Follow-up Study The follow-up study investigates individuals who have left an institution after having completed a program, a treatment, or a course of study.
The study is concerned with what has happened to them, and what has been the impact upon them of the institution and its program.

By examining their status or seeking their opinions, one may get some idea of the adequacy or inadequacy of the institution's program.

Which courses, ex- periences, or treatments proved to be of value?

Which proved to be in- effective or of limited value?

Studies of this type enable an institution to evaluate various aspects of its program in light of actual results.
Dillon's (1949) study of early school leavers yielded information that led to the improvement of the curriculum, guidance services, administra- tive procedures, and thus the holding power of the American secondary school.

Project Talent (1J.S.


Office of Education, 1965) was an educational survey conducted bi the University of Pittsburgh with support from the Cooperative Research Program of the U.S. Office of Education, the Na- tional Institutes of Health, the National Science Foundation, and the De- partment of Defense.
The survey consisted of the administration of a 2-day battery of aptitude, ability, and achievement tests, and inventories of the background characteristics of 440,000 students enrolled in 1353 secondary schools in all parts of the United States.

Five basic purposes of the survey were stated: 1.

To obtain an.

inventory of the capacities and potentialities of Amer- ican youth Chapter 5 Descriptive Studies 119 2.

To establish a set of standards for educational and psychological measurement 3.
To provide a comprehensive counseling guide indicating patterns of career success 4.

To provide information on how youth choose their life work 5.

To provide better understanding of the educational experiences that prepare students for their, life work.

In addition to the testing program, questionnaire follow-up studies have been conducted, and are planned at regular intervals, to relate the information gathered to patterns of aptitude and ability required by var- ious types of occupations.
The vast amount of data stored in the data bank, now available in the computer files, will make significant educational research possible and may provide a basis for possible changes in the educational patterns of American secondary schools.

Project Talent, described as an example of an educational survey, also provides an ifiustration of a follow-up study.
One phase of the longitudinal study reported by Combs and Cooley (1968) involved the follow-up of the ninth-grade group who failed to complete the high school program.

This group, which represented a random sample of the ninth-grade secondary school population, provided an estimate of the characteristics of the drop- out population, compared with those of a random sample of students who graduated but did not enter a junior college or 4-year institution of higher learning.
These two samples were compared on a number of char- acteristics, such as academic achievement, participation in extracurricular activities, work experiences, hobbies, contacts with school counselors, and self-reported personal qualities.

The students who graduated scored significantly higher on most of the characteristics, except self-reported qualities of leadership and impul- siveness.
One unusual finding indicated that the dropouts earned as much as those who had finished high school and had been earning it longer.

It was pointed out, however, that the economic advantages of finishing high school could not be adequately evaluated until later in life.

Project Talent, funded by the National Institute of Education, main- tained contact with the original students and completed the eleventh-year follow-up survey.
Many of the students expressed dissatisfaction with their schooling and regretted that they had not gone on to college or vocational school and that they had married too early.

More than half still live within 30 miles of their high schools, a surprising observation in a society that is believed to be extremely mobile.

The more mobile half were the high academic achievers.
Eighty perceht of the men, but only 65 percent of the women, expressed satisfaction with their jobs in meeting their long-range goals.

120 Part Two Research Methods Descriptive Research The examples discussed up to this point in the chapter have been des- ignated as assessment studies and evaluation studies.

Descriptive research studies have all of the following characteristics which distinguish them from the type previously described.

1.

They involve hypothesis formulation and testing.

2.
2.

They use the logical methods of inductive-deductive reasoning to.

arrive at generalizations.

3.

They often employ methods of randomization so that error may be estimated when population characteristics are inferred from obser- vations of samples.

4.

The variables and procedures are described as accurately and com- pletely as possible so that the study can be replicated by other researchers.
These characteristics are what make descriptive research similar to other kinds of research, including those described in the next chapter.

However, descriptive research methods are nonexperimental, for they deal with the relationships among nonmanipulated variables.
Since the events or conditions have already occurred or exist, the researcher merely selects the relevant variables for an analysis of their relationships.. Descriptive research seeks to find answers to questions through the analysis of variable relationships.

What factors seem to be associated with certain occurrences, outcomes, conditions, or types of behaviors?
Because it is often impracticable or unethical to arrange occurrences, an analysis of past events or of already existing conditions may be the only feasible way to study causation.

This type of research is usually referred to as ex post facto or causal-comparative research or, when correlational analyses are used, it may be referred to as correlational research.

For example, one would not arrange automobile accidents in order to study their causes.
The automobile industry, police departments, safety commissions, and insurance companies study the conditions associated with the accidents that have occurred.

Such factors as mechanical faults or failures, excessive speed, driving under the influence of alcohol, and others have been identified as causal.

However, while studies of past events may be the only practicable way to investigate certain problems, the researcher needs to be aware of the problems inherent in this type of research.
The researcher must be cognizant of the fact that the information used in ex post facto studies may be incomplete.

That is, the researcher may not have sufficient information about all of the events and variables that were occurring at the time being studied.

This lack of control or even of knowledge regarding what vari- Chapter 5 DescrIptive Studies 121 ables were controlled makes causal statements based upon this type of research very difficult to make.
Research on cigarette smoking has had a tremendous effect on society.

Laws banning television advertising and cigarette smoking in certain areas resulted from the U.S.

Surgeon General's reports (1964, 1979).

These re- ports compiled the research of epidemiologists on the effects of smoking on a person's health.

Epidemiological research methods are used to study trends and incidences of disease and are descriptive in nature.
The epi- demiological research on smoking included two types of descriptive meth- odology: retrospective studies relate personal histories with medical and mortality records; prospective studies follow a gioup of individuals for an indefinite period or until they die.

The early studies, from 1939 to the early 1960s, were primarily retrospective.

Thesestudies found that persons who had died of lung cancer were more likely to have been cigarette.

smokers than nonsmokers.


A number of prospective studies, begun in the 1950s, found a greater likeithood of a variety of health problems among smokers than nonsmok- ers.

Table 5—1 (Table 2 in Chapter 4 of theU.S.

Surgeon General's [1964] report) shows the expected number of deaths, based on the overall death rates for persons, the ages of the subjects, and theactual number of deaths for seven prospective studies combined.

Mortality ratio is simplyobserved deaths divided by expected deaths.
As the Surgeon General's reportstates: The mortality ratio for male cigarette smokers compared with non-smokers, for all causes of death taken together, is 1.68, representing a total death rate nearly 70 percent higher than for non-smokers.

(This ratio includes death rates for diseases not listed in the table as well as the 24 disease categories shown.)
In the combined results from the seven studies, the mortality ratio of cigarette smokers over non-smokers was particularly high for a number of diseases: cancer of the lung (10.8), bronchitis and emphysema (6.1), cancer of the.

larynx (5.4), oral cancer (4.1), cancer of the esophagus (3.4), peptic ulcer (2.8), and the group of other circulatory diseases (2.6).

For coronary artery disease the mortality ratio was 1.7.
Expressed in percentage-form, this is equivalent to a statement that for coronary artery disease, the leading cause ofdeath in this country, the death rate is 70 percent higher for cigarette smokers.

For chronic bronchitis and emphysema, which are among the leading causes of severedisability, the death rate for cigarette smokers is 500 percent higher than for non-smokers.

For lung cancer, the most frequent site of cancer in men, the death rate is nearly 1,000 percent higher.

(pp.
(pp.

28—29) While this evidence appears overwhelming, it is not totally convinc- ing by itself.
Since the researchers could not randomly assign persons to 122 Part Two Research Methods TABLE 5—1 Expected Death Rates for Smokers Underlying Cause of Death Cancer of lung (162—3) Bronchitis and emphysema (502, 521.1) Cancer of larynx (161) Oral cancer (140—8) Cancer of esophagus (150) Stomach and duodenal ulcers (540, 541) Other circulatory diseases (451—68) Cirrhosis of liver (581) Cancer of bladder (181) Coronary artery disease (420) Other heart diseases (421—2, 430—4) Hypertensive heart (440—3) General arteriosclerosis (450) Cancer of kidney (180) All causes (Surgeon General's Report, 1964).
Expected Deaths Observed Deaths Mortality Rqtio 170.3 89.5 14.0 37.0 33.7 105.1 254.0 169.2 111.6 6,430.7 526.0 4A) the smoking and nonsmoking groups, it is possible that persons who decide to smoke are particularly nervous individuals and that it is their nervousness, not their smoking, that causes their greater incidence of illness and early death.
This argument is used by the tobacco industry.

Of course this research, along with chemical analyses indicating carcin- ogens in cigarette smoke and animal studies, is convincing to the vast majority of scientists and the public.

Sesame Street Studies.

Minton (1975) studied the effect of viewing the children's television program, "Sesame Street," on the reading readiness of kindergarten children.

Of three sample groups, a 1968, a 1969, and a 1970 group, only the 1970 group had viewed the program.
Reading Readiness and "Sesame Street" Sample Group 1968 1969 1970 N 482 495 524 White Black Spanish-Speaking 431 434 436 51 61 88 18 9 25 From "Impact of Sesame Street on Reading Readiness" by j. M. Minton, Sociology of Education, 1975, 48, 141—51.

Reprinted by permission.


Scores on the Metropolitan Reading Readiness Test battery, consist- ing of six subtests (word meaning, listening, matching, alphabet letter Chapter 5 Descriptive Studies 123 recognition, numbers, and copying text) were used to measure readiness.

Using pretest-posttest design, the mean gain scores of the 1970 group were compared with those of the 1968 and 1969 groups.

No significant differences at the 0.05 level were observed in total scores.
On only one of the subtests, letter recognition, was a significant difference observed, favoring the 1970 group.

In a classification by socio- economic status, advantaged children watched more and scored higher than disadvantaged children.

The hypothesis that viewing "Sesame Street" would help to close the gap between advantaged and disadvantagedchil- dren was not supported;.

rather, the gap was widened.

Anderson and Levin (1976) studied the effect of age on the viewing.
attention of small children to a 57-minute taped "Sesame Street" program, consisting of 41 bits, each ranging in length from 10 to 453 seconds.

Six groups of five boys and five girls, ages 12, 18, 24, 30, 36, 42, and 48 months were observed by video tape recordings.

In a viewing room, in the presence of parents, toys were provided as alternatives to viewing.

The following observations were reported: 1.

Length of attention increased with age.


The younger children ap- peared to be more interested in the toys and interacting with their mothers.

2.

Length of attention decreased as bit length increased.

3.

Attention to animals increased to 24 months but dropped therafter.

4.

Children showed more interest in the presence of women, lively music, puppets, peculiar voices, rhyming, repetition, and motion.

5.
5.

Children showed less interest in the presence of adult men, animals, inactivity, and still drawings.

Other Descriptive Research.

The literature suggests that there are "ad- verse effects of early father unavailability on the cognitive development of children" (Fry & Grover, 1982, p. 105).

Since it is impossible to randomly assign children to father present and absent conditions, descriptive re- search is most common in this area.
Fry and Grover (1982) studied the effect of father absence on social problem solving, using a combination of descriptive and experimental procedures.

They first used descriptive re- search to verify the relationship and then followed this with an experi- mental intervention designed to improve the children's social problem solving.

Only the descriptive study will be described here.


Sixty children from father-absent homes were matched with 60 chil- dren from father-present homes on socioeconomic, sex, and IQ (within 5 points).

Children with chronic behavioral and emotional problems were excluded from the study.

The children were then assessed individually on two of problem-solving measures and a self-rating scale designed to measure self-confidence, self-efficacy and self-worth.
The statistical anal- ysis of the data indicated that the two groups of children, father present 124 Part Two Research Methods and father absent, differed, with the father-present children scoring higher in problem solving and on the measures of self.

This study could only have been done by using descriptive research methodology.

Since these findings supported their hypothesis, Fry and Grover then carried out the intervention phase of their research.
Other potential descriptive research includes studies of juvenile de- linquency which might compare the social and educational backgrounds of delinquents and nondelinquents.

What factors, if any, were common to the nondelinquent group?

Any factors common to one group, but riot to the other, might serve as a possible explanation of the underlying causes of delinquency.

Some efforts have been made to associate good or poor teaching with the type of educational institution in which the teachers prepared.
Those studies have proved inconclusive, possibly for a number of reasons.

In addition to the difficulty of finding valid and satisfactory criteria of good and poør teaching, many factors other than type of college attended seem to be significant.

Such variables as quality of scholarship, socio- economic status, personality qualities, types of nonschool experiences, attitudes toward the teaching profession, and a host of others have pos- sible relevancy.
Replication and Secondary Analysis Replication, a combination of the terms repetition and duplication, is an important method of challenging or verifying the conclusions of a previous study.

Using different subjects at a different time and in a different setting and arriving at conclusions that are consistent with those of the previous study would strengthen its conclusions and justify more confidence in its validity.
Replication is essential to the development and verification of new generalizations and theories.

Another useful procedure, known as secondary analysis, consists of reanalyzing the data gathered by a previous investigator and may involve different hypotheses, different experimental designs, or different methods of statistical analysis.

The subjects are the same and the data are the same.

The difference is that of alternative methods of analysis.
Secondary analysis has a number of advantages that commend its use: 1.

The new investigator may bring an objectivity, a fresh point of view, to the investigation and may think of better questions to be raised or hypotheses to be tested.

For example, the viewpoint of a psy- chologist rather than that of a sociologist (or vice versa) may find greater meaning in the data already available.

Chapter 5 Descriptive StudIes 125 2.


Secondary analysis may bring greater expertise to the area of in- vestigation and greater skill in experimental design and statistical analysis.

3.

The reanalysis would involve less expense in both time and money.

Because the data are already available, a more modest appropriation of funds would be possible.

It would not be necessary to intrude upon the time of subjects (teachers and students) whose primary activities had been diverted in the original investigation.
4.

Secondary analysis may provide useful experience for students of research methodology by.enabling them to use real data, rather than simulated or inferior data, for the purposes of the exercise.

Secondary analysis has played an important part in educational re- search.

Probably no investigation has been subjected to as great a degree of secondary analysis as the Equality of Educational Opportunity study, de- scribed next.

Equality of Educational Opportunity Study.


In 1964, the Congress of the United States passed the Civil Rights Act, which directed the United States Commissioner of Education to carry out a study of "the lack of educational opportunity by reason of race, color, religion, or national origin in public educational institutions at all levels in the United States, its territories and possessions, and the District of Columbia."
This authorization assumed that educational opportunity for mem- bers of minority groups was unequal to that available for white students.

This study was one of the largest of its type ever conducted.

The report of its findings, commonly known as the Coleman Report, was titled Equal- ity of Educational Opportunity (Coleman, et al., 1966).
The nationwide investigation selected, by a two-stage probability sample, 640,000 public school pupils in grades 1, 3, 6, 9, and 12, and 60,000 teachers in more than 4000 schools.

Data were also gathered from parents, school principals, school district superintendents, and prominent community members.

In addition, case studies of individual cities were conducted by educators, lawyers, and sociologists.
For comparative pur- poses the data were organized by geographic location as northern, north- ern metropolitan, southern and southwestern, southern metropolitan, and midwestern and western.

Individuals were classified as white, black, Asian, Indian, Mexican-American, and Puerto Rican.

As much as possible, data-gathering instruments were checked for validity and reliability.

Methods of data analysis included multiple cor- relation and factorial analysis of variance and covariance.
Although it would not be feasible to present a detailed account of the findings of the study, a few of the major conclusions are induded: 126 Part Two Research Methods 1.

The report rejected the assumption that the educational opportunities provided for minority children were unequal.

There seemed to be little difference in almost all school facilities that would relate to equality of opportunity.

In some areas, minority schools seemed to be more adequate than predominantly white schools.

2.
2.

Family background, rather than the characteristics of the school, appeared to be the major influence on school achievement.

It was apparent that, over the years, the school experience did little to narrow the initial achievement gap.

3.

The socioeconomic composition of the student body was more highly related to achievement than any school factor.

4.

The achievement level in rank order was white, Asian-American, American Indian, Mexican-American, Puerto Rican, and black.
While white students scored sigiiificant1y higher than any other group, Asian- Americans excelled in nonverbal and mathematics achievement.

)5 Inequalities of educational opportunity were more closely related to regional differences, rather than to differences between predomi- nantly black and white schools.

Schools in the North, Midwest, and West seemed to have better facilities than those in the South and Southwest.

6.
6.

Social class differences within all groups appeared to be more sig- nificant than the differences between ethnic groups.

The Coleman Report has been subjected to criticism both by expe- rienced researchers and by members of special interest groups.

The find- ings were unacceptable to some, who pointed out flaws in the gathering of data and their interpretation.

Others found procedural defects in sam- pling and statistical analysis of the data.
Of 900,000 pupils solicited, only about 640,000, or about two-thirds of the invited sample, were tested.

Twenty-one metropolitan school dis- tricts refused to participate in the study, including such large cities as Boston, Chicago, Indianapolis, and Los Angeles.

In addition, twenty-three other school districts, who participated to a limited degree, refused to.

test their pupils.
test their pupils.

The provision for an equal number of white and nonwhite participants in the sample introduced a possible element of invalidity in the statistical analysis of the data.

The questionnaires were criticized for their lack of what has been termed a "qualitative bite," the effort to get beneath the surface for more meaningful responses.

There was also a high degree of nonresponse to the questionnaires, particularly on some items of an emotional or contro- versial nature.
For example, one-third of the principals failed to answer questions on the racial composition of their faculties.

Some critics believed that the report did not m'ke a highly significant contribution to education, but most agreed that it did stimulate interest Chapter 5 Descriptive Studies 127 in further research concerning the relationship of the family, the school, and the community.
The fact that no previous study has generated so much controversy is not surprising considering the complexity of the problems involved and the sensitive nature of the issues.

For example, both advocates and op- ponents of school busing viewed the data in the light of their own estab- lished positions.

A number of studies using secondary analysis were authorized by various government agencies, special commissions, and philanthropic foundations.
Using the Coleman Report data, various aspects of the prob- lem were examined more closely, using different statistical procedures and raising.

different questions.

Some confined their investigations to data relating to a single geographic area while others considered a wider range of data analysis.

Helpful resumes of several of these studies are induded in the publication, On Equality of Educational Opportunity, edited by Mos- teller and Moynthan (1972).

Meta-analysis.
Meta-analysis.

A relatively recent innovation that allows a researcher to systematically and statistically combine the findings of several previous studies is known as meta-analysis, research synthesis, or research integration.

There are a number of quantitative techniques, ranging from fairly simple to quite complex, by which the data from previously published studies can be combined.

Glass (1978) and his colleagues (Glass, Smith, & Barton, 1979) have developed and described some of these techniques.
Walberg (1986) discusses the relative advantages of the traditional review of the literature and the statistical research synthesis.

He suggests that a com- bination of these approaches can be useful in estimating the effects of a number of studies.

Walberg and his colleagues have conducted a number of studies using these techniques.

See the special issue of Evaluation in Education, 1980, Vol.

4, pp.

1—142, edited by Walberg and Haertel, for a selection of these and other research integration efforts.
The Post Hoc Fallacy One of the most serious dangers of ex post facto and causal-comparative research is the post hoc fallacy, the conclusion that because two factors go together one must be the cause and the other the effect.

Because there seems to be a high relationship between the number of yearsof education completed and earned income, many educators have argued that staying in schoOl will add x number of dollars of income over a period of time for each additional year of education completed.
Although there may be such a relationship, it is also likely that some of the factors that influence young people to seek additional education are more important than the 128 Part Two Research Methods educational level completed.

Such factors as socioeconomic status, per- sistence, desire, willingness to postpone immediate gratification, and in- telligence level are undoubtedly significant factors in vocational success.

Staying in school may be a symptom rather than the cause.
Some critics of cigarette—cancer research have advanced a similar argument.

The case that they propose follows this line of reason: Let us suppose that certain individuals with a type of glandular imbalance have a tendency toward cancer.

The imbalance induces a certain amount of nervous tension.

Because excessive cigarette smoking is a type of nervous tension release, these individuals tend to be heavy smokers.
The cancer could result from the glandular imbalance rather than from the smoking, which is only a symptom.

This error of confusing symptoms or merely associated factors with cause could lead researchers to deduce a false cause-and-effect relationship.

This illustration is not presented to discredit this type of cancer research.

Substantial evidence does suggest a significant relationship.
Lab- oratory experiments have supported a causal relationship between the coal-tar products that are distilled from cigarette combustion and malig- nant growth in animals.

The association explanation, however, is one that should always be examined carefully.

Ex post facto and causal-comparative research is widely and appro- priately used, particularly in the behavioral sciences.
In education, because it is impossible, impracticable, or unthinkable to manipulate such variables as aptitude, intelligence, personality traits, cultural deprivation, teacher competence, and some variables that might present an unacceptable threat to human beings, this method will continue to be used.

However, its limitations should be recognized: 1.

The independent variables cannot be manipulated.

2.

Subjects cannot be randomly, or otherwise, assigned to treatment groups.

3.
3.

Causes are often multiple rather than single.

For these reasons scientists are reluctant to use the expression cause and effect in nonexperimental studies in which the variables have not been carefully manipulated.

They prefer to Observe that when variable A ap- pears, variable B is consistently associated, possibly for reasons not com- pletely understood or explained.
Since there is a danger of confusing symptoms with causes, ex post facto research should test not just one hypothesis but other logical alternate or competing hypotheses as well.

Properly employed and cautiously in- terpreted, it will continue to provide a useful methodology for the de- velopment of knowledge.

Students who have completed a course in research methods should be sensitive to the operation of extraneous variables that threaten the Chapter 5 Descriptive Studies 129 validity of conclusions.
Glass (1968) cautions educators of the need for critical analysis of reported research.

He cites a number of interesting examples of-carelessly conducted studies that resulted in completely false conclusions.

Unfortunately, these conclusions were accepted by gullible readers and widely reported in popular periodicals and some educational psychology textbooks.
The authors trust that the experience of the introductory course in educational research will help students and educators to read research reports more carefully and to apply more rigorousstandards of judgment.

Summary The term descriptive studies has been used to classify a number of different types of activity.

This chapter points out the distinctions between three major categories: assessment, evaluation, and descriptive research.
Assessment describes the status of a phenomenon at a particular time without value judgment, explanation of reasons or underlying causes, or recommendations for action.

Evaluation adds to the description of status the element of value judgment, in terms of effectiveness, desirability, or social utility, and may suggest a courseof action.

No generalizations are extended beyond the situation evaluated.
Descriptive research is concerned with the analysis of the relationships between nonmanipulated variables and the development of generalizations, extending its con- clusions beyond the sample observed.

Assessment types of studies described are surveys, public opinion polls, the National Assessment of Educational Progress, the International Assessment of Ed- ucational Achievement, activity analysis, and trend studies.

Evaluation studies included are school surveys and follow-up studies.
The application of evaluation findings to social problem solving is discussed.

Descriptive research étudies include ex post facto or explanatory observational studies.

These methods have been described and examples provided.

The hazards of the post hoc fallacy have been emphasized.

Exercises 1.

Why is it sometimes difficult to distinguish between an assessment study, an evaluation study, and a descriptive research project?

Illustratewith an example.

2.


Public opinion polls base their conclusions on a sample of approximately 1500 respondents.

Is this an adequate sample for a nationwide survey?

3.
3.

In a 1974 study, the West Virginia State Department of Education reported that counties with the highest per-pupil expenditure were the counties with the highest level of academic achievement, and that this "shows for the Part Two Research Methods first time the clearest possible relationship between student achievement and the amount of money invested in the public schools."

Can you suggest several competing hypotheses that might account for high academic achievement?

4.
4.

What is the difference between a study and a research project?

5.

In what ways does conducting longitudinal studies run the risk of the violation of confidentiality of personal information?

6.

How can a study of money and investment trends help you provide for your future financial security?

7.

Draw up a proposal for a follow-up study of your high school graduating class of 5 years ago.

Indicate what information you believe would be helpful in improving the curriculum of the school.

8.
8.

Of what value are the findings of the annual Gallup poll of public attitudes toward education?

9.

How could the survey type of study be helpful in arriving at solutions to the crime problem in large cities?

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COOLEY, W. W. (1978).

Explanatory Observational Studies.

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COOLEY, W. & LOHNES, P. R. (1976).

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Theory and practice.


6 Experimental and Quasi-Experimental Research EXPERIMENTAL RESEARCH PROVIDES a systematic and logical method for an- swering the question, "If this is done under carefully controlled condi- tions, what will happen?"

Experimenters manipulate certain stimuli, treat- ments, or environmental conditions and observe how the condition or behavior of the subject is affected or changed.

Their manipulation is de- liberate and systematic.


They must be aware of other factors that could influence the outcome and remove or control them so that they can es- tablish a logical assodation between manipulated factors and observed effects.

Experimentation provides a method of hypothesis testing.

After ex- perimenters define a problem, they propose a tentative answer, or hy- pothesis.
They test the hypothesis and confirm or disconfirm it in the light of the controlled variable relationship that they have observed.

it is important to note that the confirmation or rejection of the hypothesis is stated in terms of probability rather than certainty.

Experimentation is the dassic method of the science laboratory, where elements manipulated and effects observed can be controlled.
It is the most sophisticated, exacting, and powerful method for discovering and developing an organized body of knowledge.

Although the experimental method finds its greatest utility in the laboratory, i has been effectively applied within nonlaboratory settings such as theclassroom, where significant factors or variables can be con- trolled to some degree.

The immediate purpose of xperimentation is to predict events in the experimental setting.
The ultimate purpose is to 133 134 Part Two Research Methods generalize the variable relationships so that they may be applied outside the laboratory to a wider population of interest.

Early Experimentation The earliest assumptions of experimental research were based upon what was known as the law of the single variable.

In 1873, John Stuart Mill provided a definition for this principle.
He stated five rules or canons that he believed would include all types of logical procedure required to establish order among controlled events.
One of his canons, known as the method of difference, states: If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur have every circumstance in common save one, that one occurring only in the former, the circumstance in which alone the two instances differ is the effect, or the cause, or an indispensable part of the cause of the phenomenon.
(Mill, 1873, p. 222) In simpler language, if two situations are alike in every respect, and one element is added to one but not the other, any difference that develops is the effect of the added element; or, if two situations are alike in every respect, and one element is removed from one but not from the other, any difference that develops may be attributed to the subtracted element.

The law of the single variable provided the basis for much early laboratory experimentation.
In 1662, Robert Boyle, an Irish physicist, used this method in arriving at a principle upon which he formulated his law of gases: When temperature is held constant, the volume of an idealgas is inversely proportional to the pressure exerted upon it.

In other words, when pressure is raised, volume decreases; when pressure is lowered, volume increases.

In Boyle's law, pressure is the single variable.


V1 — P2 — V2 P1 A little more than a century later, Jacques A. C. Charles, a French physicist, discovered a companion principle, now known as Charles' law.

He observed that when the pressure was held constant, the volume of an ideal gas was directly proportional to the temperature.

When temper- ature is raised, volume increases; when temperature is lowered, volume decreases.

In Charles' law, temperature is the single variable.
V1 — T1 — V2 T2 Chapter 6 Experimental and Quasi-Experimental Research Although the concept of the single variable proved useful in some areas of the physical sdences, it failed to provide a sound approach to experimentation in the behavioral sdences.

Despite its appealing implicity and apparent logic, it did not provide an adequate method for studying complex problems.

It assumed a highly artificial and restricted relationship between single variables.
Rarely, if ever, are human events the result of single causes.

They are usually the result of the interaction of many variables, and an attempt to limit variables so that one can be isolated and observed proves impossible.

The contributions of R. A. Fisher, first applied in agricultural ex- perimentation, have provided a much more effective way of conducting realistic experimentation in the behavioral sdences.
His concept of achiev- ing preexperimental equating of conditions through random selection of subjects and random assignment of treatments, and his concepts of anal- ysis of variance and analysis of covariance made possible the study of complex interactions through factorial designs, in which the influence of more than one independent variable upon more than one dependent variable could be observed.

Current uses of this type of design will be discussed more fully later in this chapter.
Experimental and Control Groups An experiment involves the comparison of the effects of a particular treat- ment with that of a different treatment or of no treatment.

In a simple conventional experiment, reference is usually made to an experimental group and to a control group.

These groups are equated as nearly as possible.

The experimental group is exposed to the influence of the factor under consideration; the control group is not.
Observations are then made to determine what difference appears or what change or modification occurs in the experi- me)ta1 as contrasted with the control group.

Sometimes it is also necessary to control for the effect of actually participating in an experiment.

Medical researchers have long recognized that patients who receive any medication, regardless of its real efficacy, tend to feel better or perform more effectively.
In medical experiments, a harmless or inert substitute is administered to the control group to offset the psychological effect of medication.

These substitutes, or placebos, are indistinguishable from the real medication under investigation, and nei- ther experimental nor control subjects know whether they are receiving the medication or the placebo.

The effectiveness of the true medication is the difference between the effect of the medication and that of the placebo.
What seems to be a similar psychological effect was recognized in a series of experiments at the Hawthorne Plant of the Western Electric Company and originally published in 1933 (Mayo, 1960).

The studies 136 Part Two Research Methods concerned the relationships between certain working conditions and worker output efficiency.

illumination was one of these manipulated experimental variables.

The researchers found that as light intensity was increased, worker output increased.
After a certain peak was apparently reached, it was decided to see what effect the reduction of intensity of illumination would have.

To the surprise of the researchers, as intensity was decreased by stages, output continued to increase.

The researchers concluded that the attention given the workers and their awareness of participation in our experiment apparently were important motivating factors.

From these studies the term Hawthorne Effect was introduced into the psychological literature.
It has been commonly believed that this reactive effect of knowledge of participation in an experiment, the Hawthorne Effect, is.

similar to the medical placebo effect.

Researchers have frequently devised nonmedical placebos to counteract this potential effect.
One such device, used in connection with experiments involving the comparison of traditional teaching materials with new experimental materials, is to reprint the traditional, or control materials and label both these and the new, experimental ma- terials: "Experimental Method."

A group receiving a placebo is usually known as a placebo control group to distinguish it from the more common control group that receives nothing additional as a result of the study.
Even when the subjects of a study are unlikely to know or care that they are participants in an experiment, it may be necessary to utilize a placebo control group.

Research with severely and profoundly retarded children may result in increased time spent with the experimental group children over the control group children unless a placebo is introduced.

An example is a study in which Kahn (1978) investigated the effect of a cognitive training program.

Rather than have the usual control group.


this study made sure that the nonexperimental group children received as much individual instruction as the experimental group children, albeit in areas other than the experimental treatment.

Thus, this study used a placebo control group in order to assure that group differences were a result of the training procedure rather than additional attention.

Experiments are not always characterized by a treatment—nontreat- ment comparison.
Varying types, amounts, or degrees of the experimental factor may be applied to a number of groups.

For example, in medical research an experiment to test the effectiveness of a particular medication in reducing body temperature might involve administering a massive dos- age to one group, a normal dosage to a second, and a minimal dosage to a third.
Because all the groups receive medication, there is no control group in the limited sense of the term, but control of the experimental factors and observation of their effects are essential elements.

In educational research, varying types or degrees of an experimental factor might also be used with different groups.

For instance, a researcher Chapter 6 Experimental and Quasi-Experimental Research 137 might compare three different methods of teaching a subject such as spelling.
Or a. researcher might wish to study the effect of dass size on learning in a high school history course.

Such a study might.

compare three classes of varying size, say 35, 30, and 25, to see which class did better.

Of course, the researcher would have to be certain that all other factors (e.g., intelligence, prior knowledge, time of day, and length of instruction, etc.)

were equated.
were equated.

Variables Independent and Dependent Variables Variables are the conditions or characteristics that the experimenter ma- nipulates, controls, or observes.

The independent variables are the condi- tions or characteristics that the experimenter manipulates or controls in his or her attempt to ascertain their relationship to observed phenomena.
The dependent variables are the conditions or characteristics that appear, disappear, or change as the experimenter introduces, removes, or changes independent variables.

In educational research an independent variable may be a particular teaching method, a type of teaching material, a reward, or a period of exposure to a particular condition, or an attribute such as sex or level of intelligence.

The dependent variable may be a test score, the number of errors, or measured speed in performing a task.
Thus, the dependent variables are the measured changes in pupil performance attributable to the influence of the independent variables.

There are two types of independent variables: treatment and orga- nismic or attribute variables.

Treatment variables are those factors that the experimenter manipulates and to which he or she assigns subjects.

At- tribute variables are those characteristics that cannot be altered by the experimenter.
Such independent variables as age, sex, race, and intelli- gence level have already been determined, but the experimenter can de- cide to include them or remove them as variables to be studied.

The question of whether 8-year-old girls show greater reading achievement than 8-year-old boys is an example of the use of an organismic variable, sex.

The teaching procedure is the same for both groups so there is no treatment independent variable.
Confounding Variables Confounding variables are aspects of a study cr sample that might influence the dependent vaable joutcome measure) and whose effect 138 Part Two Research Methods may be confused with the effects of the independent variable.

Confound- ing variables are of two types: intervening and extraneous variables.

Intervening Variables.

In many types of behavioral research the relation- ship between the independent and dependent variables is not a simple one of stimulus to response.
Certain variables which cannot be controlled or measured directly may have an important effect upon the outcome.

These modifying variables intervene between the cause and the effect.

In a classroom language experiment a researcher is interested in determining the effect of immediate reinforcement upon learning the parts of speech.
He or she suspects that certain factors or variables, other than the one being studied, immediate reinforcement, may be influencing the results, even though they cannot be observed directly.

These factors, anxiety, fatigue, motivation, for example, may be intervening variables.

They are difficult to define in operational, observable, terms, but they cannot be ignored.

Rather, they must be controlled as much as is feasible through the use of appropriate designs.

Extraneous Variables.


Extraneous variables are those uncontrolled vari- ables (i.e., variables not manipulated by the experimenter) that may have a significant influence upon the results of a study.

Many research con- clusions are questionable because of the influence of these extraneous variables.

In a widely publicized study, the effectiveness of three methods of social studies teaching was compared.
Intact classes were used, and the researchers were unable to randomize or control such variables as teacher competence or enthusiasm, or the age, socioeconomic level, or academic ability of the student subjects.

The criterion of effectiveness was achieve- ment, measured by scores on standardized tests.

It would seem clear that the many extraneous variables precluded valid conclusions about the rel- ative effectiveness of the independent variables, which were teaching methods.
It should be noted that in order for an extraneous variable to confound the results of a study, it must be correlated strongly enough with both the independent and dependent variables that its influence can be mistaken for that of the independent variable.

Although it is impossible to eliminate all extraneous variables, par- ticularly in classroom research, sound experimental design enables the researcher to largely neutralize their influence.
Controlling Extraneous Variables Variables that are of interest to the researcher can be controlled by building them 'into the study as independent variables.

For instance, a researcher comparing two different reading programs may wish to control for the Chapter 6 Experimental and Quasi-Experimental Research potentially confounding extraneous variable sex by making it an inde- pendent attribute variable and, thereby, investigating the effect of sex on the two different reading programs.
Variables that are not of direct interest to the researcher may be removed or their influence minimized by several methods, which are discussed in the following sections.

Removing the Variable.

Variables may be controlled by eliminating them completely.

Observer distraction may be removed by separating the ob- server from both experimental and control groups by a one-way glass partition.

Some variables between subjects may be eliminated by selecting cases with uniform characteristics.
Using only female subjects removes sex as a variable but thereby reduces the generalization from the study to only females.

Randomization.

Randomization involves pure chance selection and, as- signment of subjects to experimental and control groups for a limited supply of available subjects.

Random selection was discussed in Chapter 1.


Here we are referring to random assignment, the method by which everyone already selected for the sample has an equal chance of being assigned to the various treatment conditions (e.g., experimental and control).

If two groups are involved, randomization could be achieved by tossing a coin, assigning a subject to one group if heads appeared, to the other if the toss were tails.
When more than two groups are involved, dice or a table of random numbers could be used.

Randomization provides the most effective method of eliminating systematic bas and of minimizing the effect of extraneous variables.

The principle is based upon the assumption that through random assignment, differences between groups result only from the operation of probability or chance.

These differences are known as sampling error orerror variance, and their magnitude can be established by the researcher.
In an experiment, differences in the dependent variables that may be attributed to the effect of the independent variables are known as experimental variance.

The significance of an experiment may be tested by comparing experimental variance with errorvariance.

Ifat the conclusion of the experiment the differences between the experimental and control groups are too great to attribute to errorvariance, it may be assumed that these differences are attributable to experimental variance.
This process is described in detail in Chapter 11.

Matching Cases.

When randomization is not feasible (e.g., there are too few subjects), selecting pairs or sets of individuals with identical or nearly identical characteristics and assigning one of them to the experimental group and the other to the control group provides another method of Part Two Research Methods control.

This method is limited by the difficulty of.matching on more than one variable.
It is also likely that some individua's wifi be excluded from the experiment if a matching subject is not available.

Matching is not considered satisfactory unless the members of the pairs or sets are then randomly assigned to the treatment groups, a method known as matched randomization.

Balancing Cases, or Group Matching.

Balancing cases consists of assign- ing subjects to experimental and control groups in such a way that the means and the variances of the groups are as nearly equal as possible.
Because identical balancing of groups is impossible, the researcher must decide how much departure from equality can be tolerated without loss of satisfactory control.

This method also presents a similar difficulty noted in the matching method; namely, the difficulty of equating groups on the basis of more than one characteristic or variable.

Analysis of Covariance.


This method permits the experimenter to elim- inate initial differences on several variables between the experimental and control groups by statistical methods.

The use of pretest mean scores as covariants is considered preferable to the conventional matching of groups.

Analysis of covariance is a rather complicated statistical procedure, beyond the scope of this elementary treatment.
For a complete discussion, readers may wish to consult Glass and Hopkins (1984), Hays (1981), Kerlinger (1986), Kirk (1982), or Winer (1971).

Experimental Validity To make a significant contribution to the development of knowledge, an experiment must be valid.

Campbell and Stanley (1966) described two types of experimental validity, internal validity and external validity.

Cook and Campbell (1979) further divided experimental validity, adding two other types, statistical validity and construct validity.
For purposes of this introductory treatment of the issue, we will confine our discussion to the two types of experimental validity described by Campbell and Stanley.

Internal Validity.

An experiment has internal yalidity to the extent that the factors that have been manipulated (independent variables) actually have a genuine effect on the observed consequences (dependent variables) n the experimental setting.

External Validity.
External Validity.

The researcher would achieve little of practical value if these observed variable relationships were valid only in the experimental setting and only for those participating.

External validity is the extent to Chapter 6 Experimental and Quasi-Experimental Research 141 which the variable relationships can be generalized to other settings, other treatment variables, other measurement variables, and other populations.
Experimental validity is an ideal to aspire to, for it is unlikely that it can ever be completely achieved.

Internal validity is very difficult to achieve in the nonlaboratory setting of the behavioral experiment, where there are so many extraneous variables to attempt to control.

When ex perimental controls are tightened to achieve internal validity, the more artificial, less realistic situation may prevail, reducing the external validity or generalizabiity of the experiment.
Some compromise is inevitable so that a reasonable balance may be established between control and gen- eralizability—between internal and external validity.

Threats to internal Experimental Validity In educational experiments, or in any behavioral experiments, a number of extraneous variables are present in the situation or are generated by the experimental design and.

procedures.

These variables influence the results of the experiment in ways that are difficult to evaluate.
In a sense, they introduce rival hypotheses that could account for experimental change not attributable to the experimental variables under consideration.

Al- though these extraneous variables usually cannot be completely elimi- nated, many of them can be identified.

It is important that behavioral researchers anticipate them and take all possible precautions to minimize their influence through sound experiment design and execution.
A number of factors jeopardize the power of the experimenter to evaluate the effects of independent variables unambiguously.

Campbell and Stanley (1966) have discussed these factors in their excellent definitive treatment.

They include the following: Maturation.

Subjects change (biologically and psychologically) in many ways over a period of time, and these changes may be confused with the effect of the independent variables under consideration.
During the course of a study, the subjects might become more tired, wiser, hungrier, older, and so on.

They may be influenced by the incidental learnings or expe- riences that they encounter through normal maturation.

This threat is best controlled by randomly assigning subjects to experimental and control groups.

Differences between the groups would then be considered to be due to the treatment rather than to maturation.

Histoiy.
Histoiy.

Specific external events occurring between the first and second measurements and beyond the control of the researcher may have a stim- ulating or disturbing effect upon the performance of subjects.

The effect of a fire drill, the emotional tirade of a teacher, a pep session, the anxiety produced by a pending examination, or a catastrophic event in the com- 142 Part Two Research Methods munity may significantly affect the test performance of a group of students.
In many experiments, these external events will have a similar effect upon both experimental and control subjects, in which case this threat is controlled.

However, because they are specific events, they may affect one group but not the other.

The effect of these uncontrolled external events is one of the hazards inherent in experiments carried on outside the laboratory.

In laboratory experiments these extraneous variables can be controlled more effectively.

Testing.
Testing.

The process of pretesting at the beginning of an experiment can produce a change in subjects.

Pretesting may produce a practice effect that can make subjects more proficient in subsequent test performance.

Testing presents a threat to internal validity that is common to pretest- posttest experiments.

Of course, an equivalent control group would be affected by the test in a similar way as the experimental group.
Thus, having experimental and control groups controls for this threat in the same way that it does for the threat of maturation.

Unstable Instrumentation.

Unreliable instruments or techniques used to describe and measure aspects of behavior are threats to the validity of an experiment.

If tests used as instruments of observation are not accurate or consistent, a serious element of error is introduced.
If human observers are used to describe behavior changes in subjects, changes in observers or in their standards due to fatigue, increased insight or skill, or changes in criteria of judgment over a period of time are likely to introduce error.

Statistical Regression.


Statistical regression, also known as regression to the mean, is a phenomenon that sometimes operates when subjects are selected on the basis of extremely high or extremely low pretest scores and when the measurement device is not totally reliable, which is com- mon.

Subjects who score very high, near the ceiling, on a pretest, will most likely score lower (nearer the mean) on a subsequent testing.
Subjects who score very low, near the floor, on a pretest will most likely score higher (nearer the mean) on a subsequent testing.

The reader should be aware that this phenomenon occurs only when subjects are selected as a group because of their extreme scores and that the regression we are referring to is for the group as a whole, not all individuals.

Posttest scores for individuals may go in the opposite direction expected by this phe- nomenon for the group.
The purpose of a study may require the researcher to select subjects based on their extreme scores.

A study of the effects of different remedial reading programs assumes that the subjects must need remedial reading instruction and, therefore, have very low reading scores on the pretest.

To control for regression to the mean, the researcher would randomly Chapter 6 Experimental and Quasi-Experimental Research 143 assign his or her sample of poor readers to the experimental and control groups.
Since both groups would be expected to improve equally because of regression tO the mean, if the experimental group improved significantly more than the control group, the researcher could conclude that this was due to the experimental treatment rather than statistical regression.

Selection Bias.

Selection bias is represented by the nonequivalence of experimental and control groups, and its most effective deterrent is the random assignment of subjects to treatments.
Selection bias is likely when, upon invitation, volunteefs are used as members of an experimental group.

Although they may appear to be equated to the nonvolunteers, their characteristics of higher motivation may introduce a bias that would in- validate reasonable comparison.
Selection bias may be introduced when intact classes are used as experimental and control groups: Because of scheduling arrangements, an English class meeting during the fourth pe- riod may consist of particularly able students who are scheduled at that period because they are also enrolled in an advanced mathematics dass.

Interaction of Selection and Maturation.

This type of threat to the internal validity of a study is not the same as selection bias.
The interaction of selection and maturation may occur wherever the subjects can select which treatment (e.g., Which instructional method) they will receive.

Even though the groups may be equivalent on the pretest and other cognitive measures, the reasons why some people choose one treatment over another may be related to the outcome measure (dependent variable).
Thus, if more mo- tivated students chose method A for learning calculus over method B because A is harder and requires greater academic motivation, that dif- ferential motivation might be confused for the effects of the experimental variable.

Experimental Mortality.

Mortality, or loss of subjects, particularly likely in a long-term experiment, introduces a potentially confounding element.
Even though experimental and control groups are randomly assigned, the survivors might represent groups that are quite different from the un- biased groups that began the experiment.

Those who survive a period of experimentation are likely to be healthier, more able, or more highly motivated than those who are absent frequently or who drop out of school and do not remain for the duration of the experiment.
The major concern here is whether the groups experienced different loss rates or reasons for dropouts that might confound the results.

Experimenter Bias This is a type of bias introduced when the researcher has some previous knowledge about the subjects involved in an experi- ment.

This knowledge'of subject status may cause the researcher to convey 144 Part Two Research Methods some clue that affects the subject's reaction or may affect the objectivity of his or her judgment.
In medical research it is common practice to conceal from the sub- ject the knowledge of who is receiving the placebo and who the experi- mental medication.

This is known as a blind.

Having someone other than the experimenter administer the treatments and record which subjects are receiving the medication and which the placebo provides an additional safeguard.

This practice, known as a double blind, helps to minimize contamination.
Beginners in educational research have been known to contaminate a study by classifying student performance when they know the nature of the variable to be correlated with that performance.

In a simple ex post facto study a student proposed to determine the relationship between academic achievement and citizenship grades in her class.

Since she pro- posed to assign the citizenship grades herself, it would seem apparent that an element of contamination would result.
Her knowledge of the student's previous academic achievement would tend to precondition her judgment in assigning citizenship grades.

In educational studies of this type, researchers would minimize con- tamination if outside observers rated the subjects without any knowledge of their academic status.

Threats to External Experimental Validity Laboratory research has the virtue of permitting the experimenter to care- fully avoid threats to internal validity.
However, the artificial nature of such a setting greatly reduces the generalizabiity of the findings from such research.

Since educational researchers are primarily concerned with the practical uses of their findings, they frequently conduct their studies in real classroom situations.

While these real-life settings present oppor- tunities for greater generalization, they dO not automatically result in externally valid research.
Campbell and Stanley (1966) also discussed the factors that may lead to reduced generalizabiity of research to other set- tings, persons, variables, and measurement instruments.

The factors they discussed include the following: Interference of Prior Treatment.

In some types of experiments the effect of one treatment may carry over to subsequent treatments.
In an educa- tional experiment, learning produced by the first treatment is not com- pletely erased and its influence may accrue to the advantage of the second treatment.

This is one of the major limitations of the single-group, equated- materials experimental design in which the same subjects serve as mem- bers of both control and experimental groups.

If an equated-materials design is necessary, a counterbalanced design will generally control for this threat.
Chapter 6 Experimental and Quasi-Experimental Research 145 The Artificiality of the Experimental Setting.

In an effort to control extra- neous variables the researcher imposes careful controls which may intro- duce a sterile or artificial atmosphere that is not at all like the real-life situation about which generalizations are desired.

The reactive effect of the experimental process is a constant threat.

Interaction Effect of Testing.


The use of a pretest at the beginning of a study may sensitize individuals by making them more aware of concealed purposes of the researcher and may serve as a stimulus to change.

This is a different potential jroblem than that of testing, discussed earlier as a threat to internal validity.

With testing, the threat was that the pretest would affect the subjects' performance on the posttest in a direct fashion.

That was easily controlled by having a control group.
In the case of the interaction effect of testing, we have a more difficult problem.

Here the pretest may alert the experi- mental group to some aspect of the interventions that is not present for the control group.

That is, the pretest may interact differently with the experimental intervention than it does with the control or placebo con- ditions.

To avoid this threat requires random assignment and either no pretest or the Solomon four-group design discussed in the next section.
Interaction of Selection and Treatment.

Researchers are rarely, if ever, able to randomly select samples from the wide population of interest or randomly assign to groups; consequently, generalization from samples to populations is hazardous.

Samples used in most classroom experiments are usually composed of intact groups, not randomly selected individuals.

They are based upon an accepted invitation to participate.

Some school officials agree to participate; others refuse.
One cannot assume that sam- ples taken from cooperating schools are necessarily representative of the target population.

Such schools are usually characterized by faculties that have high morale, less insecurity, greater willingness to try a new ap- proach, and a greater desire to improve their performance.

The Extent of Treatment Verification.


Due to the potential threat of ex- perimenter bias, most researchers have research assistants, or others who are not directly involved in the formulation of the research hypotheses, deliver the treatment.

This leads to a potential threat to external validity.

Was the treatment administered as intended and described by the re- searcher?
The researcher must have a verification procedure (e.g., direct observation, videotape) to make sure that the treatment was properly administered.

After reading about these threats to experimental validity, the be- ginner is probably ready to conclude that behavioral research is too haz- ardous to attempt.

Particularly outside of the laboratory, ideal experi- mental conditions and controls are never likely to prevail.
However, an understanding of these threats is important so that the researcher can 146 Part Two Research Methods make every effort to remove or minimize their influence.

If one were to wait for a research setting free from all threats, no research would ever be carried on.

Knowing the limitations and doing the best that he or she can under the circumstances, the researcher may conduct experiments, reach valid conclusions, provide answers to important questions, and solve significant problems.
Experimental Design Experimental design is the blueprint of the procedures that enable the researcher to test hypotheses by reaching valid oncIusions about rela- tionships between independent and dependent variables.

ctiQfl.

prticulardesgn is based upon the purposes of the experiment, yp of variables to be manipu, and the conditions or limiting factorsj4 which it is conducted.
The design deals with such practical problems as I WS jects are to be assigned to experimental and control groups, the way variables are to be manipulated and controlled, the way extraneous variables are to be controlled, how observations are to be made, and the type of statistical analysis to be employed in interpreting data relationships.

The adequacy of experimental designs is judged by the degree to which they eliminate or minimize threats to experimental validity.
Three categories are presented here: 1.

Pre-experimental design—the least effective, for it provides either no control group or no way of equating the groups that are used.

2.

True experimental design—employs randomization to provide for con- trol of the equivalence of groups and exposure to treatment.

3.

Quasi-experimental design—provides a less satisfactory degree of con- trol, used only when randomization is not feasible.
A complete discussion of experimental design would be too lengthy and complex for this introductory treatment.

Therefore, only a relatively few designs will be described.

Readers may wish to refer to Campbell and Stanley's (1966) and Cook and Campbell's (1979) excellent treatments of the subject, in which many more designs are described.

Indiscussing experimental designs, we have followed Campbell and Stanley's symbol system.
R random assignment of subjects to groups or treatments X exposure of a group to an experimental (treatment) variable C exposure of a group to the control or placebo condition 0 observation or test administered Chapter 6 Experimental and Quasi-Experimental Research 147 Preexperimental Designs The least adequate of designs is characterized by: (1) the lack of a control group, or (2) a failure to provide for the equivalence of a control group.
The One-Shot Case Study x0 Carefully studied results of a treatment are compared with a general expectation of what would have happened if the treatment.

had not been applied.

This design provides the weakest basis for generalization.

Mr. Jones used a 25-minute film on racial integration in his junior high school history class.

In a test administered after the showing of the film, the mean score was 86 (a high score indicated a favorable attitude toward acceptance of all racial groups).
Mr. Jones believes that the mean score was higher than it would have been had the film not been viewed and, as he recalls, higher than the mean score of a test that he had administered to a similar class several years before.

He concludes that the film has been effective in reducing racial prejudice.

However, Mr. Jones has come to this conclusion on the basis of inadequate data.
The reader has no way of knowing if a change has occurred due to the lack of a pretest, or if a similar group who had not seen the film (a control group) would have scored differently than the group viewing the film.

This design is the poorest available and should not be used.
The One-Group, Pretest-Posttest Design 01 X02 01 = pretest 02 = posttest This design provides some improvement over the first, for the effects of the treatment are judged by the difference between the pretest and the posttest scores.

No comparison with a control group is provided.

In the same setting, Mr. Jones administered a pretest before showing the film and a posttest after the viewing.
He computed the mean difference between the pretest and the posttest scores and found that the mean had increased from 52 to 80, a mean gain of 28 score points.

He also apparently detected some temporary improvement in attitude toward racial integra- tion.

He concludes that there has been a significant improvement in at- titude as a result of viewing the film.
But what about the sensitizing effect of the pretest items that may have made the students aware of issues that they had not even thought of before?

What would the gain have been if the pretest and the posttest had been administered to another class that 14$ Part Two Research Methods had not viewed the film?

Threats to the internal validity that are not controlled include history, maturation, testing, and so forth.
External va- lidity is also poor: The Static-Group Comparison Design xo Co This design compares the status of a group that has received an experimental treatment with one that has not.

There is no provision for establishing the equivalence of the experimental and control groups, a very serious limitation.

A beginning researcher administered the 25-minute racial integration film to a group of elementary teachers in one school.

He then administered the attitude scale and computed the mean score.
At another elementary school he administered the attitude scale to teachers who had not viewed the film.

A comparison of mean scores shows that the teachers who had viewed the film had a higher mean score than those who had not.

He cOncluded that the film was an effective device in reducing racial prejudice.

What evidence did he have that the initial attitudes of the groups were equivalent?
Without some evidence of equivalence of the control and experimental groups, ttributing the difference to the experimental variable is unwarranted.

True Experimental Designs In a true experiment the equivalence of the experimental and control groups is provided by random assignment of subjects to experimental and control treatments.
Although it is difficult to arrange 'a true experimental design, particularly in school classroom research, it is the strongest type of design and should be used whenever possible.

Three experimental designs are discussed in the following sections.

The Posttest-Only, Equivalent-Groups 'Design RXO1 RCO2 This design is one of the most effective in minimizing the threats to experimental validity.
It differs from the static group comparison desIgn in that experimental and control groups are equated by random assign- ment.

At the conclusion of the experimental period the difference between Chapter 6 Experimental and Quasi-Experimental Research 149 the mean test scores of the experimental and control groups are subjected to a test of statistical significance, a t test, or an analysis of variance.
The assumption is that the means of randomly assigned experimental and control groups from the same population will differ only to the extent that random sample means from the same population will differ as a result of sampling error.

If the difference between the means is too great to attribute to sampling error, the difference may be attributed to the treat- ment variable effect.

Using a table of random numbers, the researcher selects 80 students from a school population of 450 sophomores.
The 80 students are ran- domly assigned to experimental and control treatments, using 40 as the experimental group and 40 as the control group.

The experimental group is taught the concepts of congruence of triangles by an experimental procedure method X, and the control group is taught the same set of concepts by the usual method, method C. All factors of time of day, treatment length in time, and other factors are equated.
At the end of a 3-week period the experimental and control groups are administered a test, and the difference between mean scores is subjected to a test of statistical significance.

The difference between mean scores is found to favor the experimental group, but not by an amount that is statistically significant.

The researcher rightly concludes that the superiority of the X group could well have been the result of sampling error and that there was no evidence of the superiority of the X method.
The Pretest-Posttest Equivalent-Groups Design R O X 02 R 03 C 04 X gain = 02 — 01 C gain = 04 — 03 0 03 = pretests 02 04 = posttests This design is similar to the previously described design, except that pretests are administered before the application of the experimental and control treatments and posttests at the end of the treatment period.

Gain scores may be compared and subjected to a test of the significance of the difference between means.
Pretest scores can also be used in analysis of covariance to statistically control for any differences between the groups at the beginning of the study.

This is a strong design, but there may be a possibility of the influence of the effect of testing and the interaction with the experimental variable.

Watanabe, Hare, and Lomax (1984) have reported on a study that included a pretest-posttest equivalent-groups design.
This study compared a procedure for teaching eighth-grade students to be better able to predict the content of newspaper stories from their headlines than a control group of eighth-grade students.

A pilot study, reported in their article, indicated that even good middle-school readers have difficulty predicting the con- 150 Part Two Research Methods tent of news stories from the headlines, but that college students have no trouble with this task.
Because the eighth-graders they surveyed re- ported reading primarily comics, movie, and sport sections (which might explain their poor prediction of content from headlines) and because most teachers would prefer that their students read more of the newspaper, the authors felt that it would be useful to determine if a training program could teach eighth-graders how to better understand headlines.

Watanabe et al.
Watanabe et al.

randomly assigned 46 eighth-graders to either head- line reading instruction (experimental group) or regular reading instruction (control group).

All 46 students were asked to read 20 headlines and predict story content prior to, and after, a 3-week period of instruction.

The authors scored each attempt to predict story content on a scale of 0 to 4, with 0 indicating that the student's response explained nothing and 4 indicating an "on-target potential prediction" (pp.

439—440).
439—440).

Thus each student could receive a score from 0 to 80 on each of the testings.

At the end of the 3 weeks of instruction, the authors compared the two groups using analysis of covariance (ANCOVA) and found that the experimental group was better able to predict story content from headlines after training than the control group.

ANCOVA was used because even with random assignment the groups were not exactly equal.
ANCOVA permitted the authors to statistically control for differences on the pretest so that posttest differences would not be due to initial differences.

prior to training.

The Solomon Four-Group Design R01X02 R 03 C 04 X05 R CO6 R In this design: 1.

Subjects are randomly assigned to four groups.

2.

Two groups receive the experimental treatment (X).

3.

One experimental group receives a pretest (Or).

4.

Two groups '(control) do not receive treatment (C).

5.
5.

One control group receives a pretest (03).

6.

All four groups receive posttests (02 04 05 06).

The design is really a combination of the two group designs previ- ously described, the posttest only and the pretest-posttest.

It is possible to evaluate the effects of testing, history, and maturation.

Analysis of Chapter 6 Experimental and Quasi-Experimental Research 151 variance is used to compare the four posttest scores, analysis of covariance to compare gains in 02 and 04.
Because this design provides for two simultaneous experiments, the advantages of a replication are incorporated.

A major difficulty is finding enough subjects to assign randomly to four equivalent groups.

Quasi-Experimental Designs These designs provide control of when and to whom the measurement is applied, but because random assignment to experimental and control treatments has not been applied, the equivalence of the groups is not assured.
Of the many quasi-experimental designs, only five are described.

See Cook and Campbell (1979) for a comprehensive review of quasi-experimental designs.

The Pretest-Posttest Nonequivalent-Groups Design 01 X 02 03 C 04 01 03 = pretests 02 04 = posttests This design is often used in classroom experiments when experi- mental and control groups are such naturally assembled groups as intact classes, which may be similar.
The difference between the mean of the 01 and 02 scores and the difference between the mean of the 03 and 04 scores (mean gain scores) are tested for statistical significance.

Analysis of covariance may also be used.

Because this design may be the only feasible one, the comparison is justifiable, but the results should be in- terpreted cautiously.

Two first-grade classes in a school were selected for an experiment.
One group was taught by the initial teaching alphabet (1TA) approach to reading, and the other was taught by the traditional alphabet approach.

Prior to the introduction of the two reading methods and again at the end of the school year, both groups were administered a standardized reading test, and the mean scores of the two groups were compared.

The ITA group showed a significant superiority in test scores over the con- ventional alphabet group.
However, without some evidence of the equiv- alence of the groups in intelligence, maturity, readiness, and other factors at the beginning of the experimental period, conclusions should be cau- tiously interpreted.

The Follow Through Planned Variation Study.

An interesting example of the pretest-posttest nonequivalent groups design was the Follow Through Planned Variation Study (Abt Associates, 1977), conceived in the late 1960s and initiated and funded by the United States Office of Education.
The 152 Part Two Research Methods purpose of the program was to implement and evaluate a variety of compensatory programs, extending the services of Project Head Start for disadvantaged children into the primary grades.

Head Start was a large- scale enterprise, including many innovative instructional models and in- volving the expenditure of more than a half billion dollars.
The program extended over a period of more than 9 years, with more than 79,000 first-, second-, and third-grade children participating.

Of the twenty dif- ferent instructional models and 170 projects, 17 models and 70 projects were selected for evaluation.

Approximately 2 percent of the total number of children were included in the evaluation.
Participation by school districts was voluntary, with each district selecting the particular model that it wished to implement and helping to choose the groups that were to be used as controls.

Treatments were not randomly assigned nor control groups randomly selected.

The unit of analysis was pupil mean gain for groups K—3 and 1—3 growth scores, statistically compared by instructional model and by proj- ect, using variants of linear regression and analysis of covariance.
Outcome measures were derived from gain scores on the following measuring instruments: 1.

The Metropolitan Achievement Test Battery covering such basic skills as reading comprehension, spelling, word usage and analysis, and mathematical computation, concepts, and problem solving.

2.

The Raven's Coloured Matrices Test, a nonverbal test of problem-solving ability, requiring the manipulation of geometric patterns, essentially a measure of intelligence rather than a measure of learning outcomes.

3.
3.

The Coopersmith Self-Image Inventory, a measure of self-esteem, but questioned on the grounds that it required a maturity of judgment beyond the competence of primary-age children.

4.

The Intellectual Achievement Responsibility Scale, which attempts to as- sess the child's experience of success or failure, indicating the degree to which the child attributes success to internal or external causes.

This instrument was also judged to require insights beyond the ma- turity level of small children.
There have been many analyses and evaluations of the program by official and independent agencies funded by the United States Office of Education and by private philanthropic foundations.

Among the evalu- ating agencies were the Office of Education; Abt Associates, Inc.; The Stanford Research Institute; The Huron Institute; and The Center for Re- search and Curriculum Evaluation of the University of Illinois.
It is unlikely that any large-scale study has been scrutinized so ex- tensively concerning research design, procedures employed, and inter- pretation of the data.

There have been critiques of the evaluations and critiques of the critiques, with sharp disagreement on most aspects of the Chapter 6 Experimental and Quasi-Experimental Research 153 study (Anderson, St. Pierre, Proper, & Stebbins, 1978; House, Glass, McLean, & Walker, 1978; Wisler, Burns, & Iwamoto, 1978).
However, the consensus is that the findings were disappointing, because most of the experimental effects were negligible.

Only a few of the treatment effects produced as much as a one-quarter standard devia- lion change.

(This concept is discussed in Chapter 10.)

Of those that met this criterion, two instructional models with at least one positive effect were structured approaches.

Three models with at least one negative effect were unstructured approaches.
Few of either cognitive, structured ap- proaches or child-centered, nonstructured approaches yielded significant effects.

Much of the disagreement centered around the reasons why the study was ineffective.

Several explanations have been suggested.

1.

The research was deficient in design, implementation, statistical anal- ysis, and interpretation.
Because experimental treatments were not randomly selected and control groups were not randomly assigned, mismatching resulted and comparisons were really made between different populations.

2.

There was great intersite difference in effectiveness within a given instructional model.

Most of the within-model differences were greater than the between-models difference.
There may have been serious deficiencies in the competence of those who implemented the in- novative procedures or in the actual method implemented1 even though the teachers and their teacher aides were specially trained and their activities monitored by the project sponsors.

3.

The measuring instruments may have been incompatible with the goals of the project because of inadequate identification and defini- lion of appropriate outcome variables.
The more effective instru- ments seemed to focus on basic skills or traditional educational goals rather than on goals ordinarily associated with nonstructured ap- proaches to education.

Some measured intellectual status rather than achievable learning goals.

Others appeared to require a maturity of response too complex for primary-age children.

Not all reactions to the study were negative.


Hodges, a member of the Follow Through Task Force, lists a number of reasons for viewing the program as significant and worthwhile.

"Justbecause Follow Through has not proved to be an easy, workable, inexpensive solution for all the ed- ucational problems of poor children does not mean it should be dismissed as just another failure in compensatory education" (Hodges, 1978, p. 191).
In behavioral research, the random selection and assignment of sub- jects to experimental and control groups may be impracticable.

Because of administrative difficulties in arranging school experiments, it may be 154 Part Two Research Methods necessary to use the same group as both the experimental and control group.

These designs have two apparently attractive features.

They can be carried on with one intact group without a noticeable reorganization of the classroom schedule.
The changes in procedures and testing can be concealed within ordinary classroom routines.

Artificiality can be mini- mized, for the procedures can be introduced without the subjects' aware- ness of participation in an experiment.

The Time-Series Design.

At periodic intervals, observations (measure- ments) are applied to individuals or a group.
An experimental variable (X) is introduced, and its effect may be judged by the change or gain from the measurement immediately before to the one immediately after its introduction.

The purpose of the series of measurements before and after the intervention or treatment is to demonstrate little or no change except immediately after the intervention.

In the time-series experimental design, a measured change or gain from observation 4 to observation 5 would indicate that the treatment had an effect.
This design is particularly sensitive to the failure to control the extraneous variable, history, for it is possible that some distracting, si- multaneous event at the time of the intervention would provide a rival hypothesis for the change.

01 0 0 0 X 05 06 07 08 The diagram showing one X and several Os does not necessarily represent the relative number of sessions for each.

It may be that each 0 represents one measurement while the single X represents an intervention, of several weeks.
While it is better to have several observations, as shown, it is not always possible to have this many.

Fo!

instance, a recent time- series experiment by a student used only two preintervention and two postintervention measures.

Since this study was measuring the effect of a program to reduce the number of criminal victimizations of disabled students, it was necessary to have a 2-month period between measure- ments in order to have a sufficient number of victimizations for each period measured.
That is, 01, in November measured September and October crimes, 02 in January measured crimes in November and December, and so on.

The Equivalent Time-Samples Design.

Instead ofhaving equivalent sam- ples of persons, it may be necessary to use one group as the experimental and control group.

In this design, the experimental condition (X1) is pres- ent between some observations and not (X0) between others.
This may be diagrammed as shown below, although the number of observations and interventions vary and the alternation of the experimental condition Chapter 6 Experimental and Quasi-Experimental Research 155 with the control condition would normally be random rather than system- atic as shown here.

01 X1 02 X0 03 X1 04 X0 05 A study by Hall et al.

(1973) illustrates a version of the equivalent time-samples design.
Five subjects, identified as the most violently ag- gressive, were selected from a group of 46 mentally retarded boys living in an institution dormitory.

Their ages ranged from 12 to 16 (mean, 13.8); their IQs from 40 to 62 (mean = 50).

Each subject was observed for 10 weeks in 10 randomly selected 3-minute periods, during which time acts of aggressive behavior were recorded.
Acts were classified as motor ag- gressive (throwing objects, kicking, fighting, scratching) and nonmotor aggressive (verbal abuse, screaming or shouting, insubordination).

The observations were scheduled in four periods: 1.

Observation (baseline) 2.

On-reinforcement 3.

Off-reinforcement 4.

On-reinforcement session 1 sessions, 2,, 3, 4, 5 sessions 6, 7 sessipns 8, 9, 10 Positive reinforcement as a reward for nonaggressive behavior con- sisted of candy, praise, or trips to the canteen.
Negative reinforcement following aggressive acts consisted of ostracizin.g from group activities, taking away a favorite toy, or reprimanding verbally.

Two observers were employed, one observing motor aggressive acts, .the other, nonmotor ag- gressive acts.

The researchers concluded that reinforcement affected the amount of aggressive output.

Motor aggressive behavior was reduced more effec- tively than nonmotor aggressive behavior (see Figure 6—1).
To assess the permanence of behavior change after the conclusion of the experiment, a phase-out period of 89 days of observation was scheduled.

The only reinforcement used was the posting of stars for nonaggressive behavior.

Observations during the phase-out period indicated much more acceptable dormitory behavior.

Designs of this type have a number of limitations.


Although they may minimize the effect of history, it is possible that they may increase the influence of maturation, unstable instrumentation, testing, and ex- perimental mortality.
The Equivalent Materials, Pretest, Posttest Design 01 XMA 02 03 XMB 04 X = teaching method A XMB = teaching method B 0 and 0 are pretests 02 and 04 are posttests 156 Part Two Research Methods FIGURE 6—1 Number of Motor Aggressive, Nonmotor Aggressive, and Total Aggressive Acts during On-Reinforcement and Off-Reinforcement Expenmental Conditions.

Another experimental design, using the same group or class for both experimental and control groups, involves two or more cycles.
The class may be used as a control group in the first cycle and as an experimental group in the second.

The order of exposure to experimental and control can be reversed—experimental first and control following.

Essential to this design is the selection of learning materials that are different but as nearly equal as possible in interest to the students and in difficulty of comprehension.

An example may help to clarify the procedure.


Ms. Smith hypothesized that the students in her class who were used to background music while doing their homework would learn to spell more efficiently in the classroom if music were provided.

Because Chapter 6 Experimental and Quasi-Experimental Research 157 she was unable to arrange a parallel group experiment, she decided to use her class as both an experimental and a control group.
To equate the words to be learned, she randomly selected two sets of 100 words from an appropriate graded word list.

For cycle I, the control cycle, she pretested the class on word list A.

Then for 20 minutes each day the students studied the words, using drill and the usual spelling rules.

At the end of 2 weeks she retested the class and computed the mean gain score in correct spelling.

For cycle II, the experimental cycle, she pretested the class on word list B.
Then for 20 minutes each day, with soft, continuous music in the background (the experimental condition), the students studied their word list, using the same drill and spelling rules.

At the end of the second 2-week period she retested the class and computed the mean gain score in correct spelling.

The mean gain score for the experimental cycle was significantly greater than the mean gain score for the control cycle.
Sheconcluded that the introduction of the experimental variable had indeed improved the effectiveness of the learning experience.

The apparent simplicity and logic of this design is somewhat mis- leading, and when examined in light of the threats of experimental valid- ity; the design's weaknesses become apparent.

1.

It is often difficult to select equated materials to be learned.
For types of learning other than spelling, finding learning materials that are equally interesting, difficult, and unfamiliar would be a serious problem.

2.

As the students enter the second cycle, they are older and more mature.

They also have more experience.

3.

Outside events (history) would be more likely to affect the experience in one cycle than in the other.

4.

There would be an influence of prior treatment carrying over from the first cycle to the second.

5.
5.

The effects of testing would be more likely to have a greater impact on the measurement of gain in the second cycle.

6.

Mortality, or loss of subjects from the experiment, would be more likely in an experimental design spread over a longer period of time.

7.

When the experimenter's judgment was a factor in evaluation, con- tamination, the experimenter's knowledge of subject performance in the first cycle, could possibly influence evaluation of performance in the second.
Some of the limitations of the equivalent-materials, single-group, pretest-posttest design can be partially minimized by a series of replica- tions in which the order of exposure to experimental and control treat- 1 58 Part Two Research Methods ments is reversed.

This process, known as rotation, is illustrated by this pattern in a four-cycle experiment.
I 01 X 02 01 03 05 II 03 C 04 07 = pretests Iv III 05 C 06 02 04 06 0 = posttests 07 X 0 If the experimental treatment yielded significantly greater gains re- gardless of the order of exposure, its effectiveness could be accepted with greater confidence.

However, it is apparent that this design is not likely to equate materials, subjects, or experimental conditions.
All single-group experimental designs are sensitive to the influences of many of the threats to validity previously mentioned in this chapter: history, maturation, unstable instrumentation, testing, and experimental mortality.

Replication of the studies, using different units as subjects, is an effective way to improve their validity.

However, single-group experi- ments may be performed when randomly equated group designs cannot be arranged.

CounterbalancedDesigns.


These are designs in which experimental con- trol derives from having all the subjects receive all the treatment condi- tions.

The subjects are placed into, in the case of this example, four groups.

Each of the groups then receives all four treatments, but in different orders.
This may be diagrammed as follows: Replication 1 2 3 4 01X1 Group A Group B Group C Group 0 02X2 0,X3 04 X4 05 B 0 A C C A D B D C B A In the first sequence following a pretest (0), group A receives treat- ment 1, group B receives treatment 2, group C receives treatment 3, and group D receives treatment 4.

After a second test (02), each group then receives a second treatment, and so on.
Thus each group receives all treatments, and each treatment is first, second, third, or fourth in the order received by one of the groups.

This design has excellent internal validity because history, matura- tion, regression, selection, and mortality are all generally well controlled.

The major limitation is that an order effect could wipe out any potential differences among the treatments.

Four randomly assigned groups would therefore be preferable.
Thus, this design should be used when random assignment is not possible and when it is expected that the different treatments will not interfere too much with each other.

Chapter 6 Experimental and Quasi-Experimental Research 159 Factorial Designs When more than oe idepndent yriable is inciude4 in a study whether a true experiment or a quasi-experiment, a factorial design is necessary.
Because most real-world outcomes are the result of a number of factors factorial designs, researchers can determine, for example, if the treatment ttects significantly with, sex oi a That is, the experimenter can de- termine if one treatment is more effective with boys and another with girls, or if older girls do better on the treatment than younger girls, whereas older and younger boys do equally well on the treatment.
The simplest case of a factorial design would be to have two inde- pendent variables with two conditions of each, known as a 2 x 2 factorial design.

This design would be used if a researcher decided to compare a new (experimental) method of teaching reading to reading-disabledchil- dren with a commonly used (control) method, and also wanted to deter- mine if boys and girls would do differently on the two methods.

Such a design would look like Figure 6—2.


With this design we have four cells, each of which represents a subgroup (for example, experimental females, control males, and so forth).

This design will permit the researcher to determine if there is a significant overall effect, known as main effect, for treatment and/or sex.
It also permits the determination whether these two variables interact significantly, such Treatment Experimental Control Females CELL I CELL 2 Males CELL 3 CELL 4 FIGURE 6—2 Factorial Design.

Part Two Research Methods that boys do best in the experimental condition and girls do best in the control condition.

If this were the case, the subjects in Cell 2 would have a higher average score than those in Cell 1, and the subjects in Cell 3 would outperform those in Cell 4.
Nucci and Nucci (1982) examined the responses of children to the social transgressions (such as spitting on the ground) of their peers.

They observed boys and girls between 7 and 10 and between 11 and 14 years of age and coded their observations of the responses into one of eight categories.

They found an interaction effect of sex by age for just one of the categories.

This interaction effect could be graphically represented as in Figure 6—3.
We see that the two lines actually cross, thus clearly in- dicating that "With increased age the girls provided greater frequencies of ridicule responses to [social transgressions] while the boys responded with approximately the same frequencies as at the younger age" (Nucci & Nucci, 1982, p. 1341).

Figure 6—4 shows an example of another type of response, sthting the rule being violated, for which Nucci and Nucci found no interaction effect.

Here we see two relatively parallel lines.


Of course, factorial designs can have more than two independent variables and more than two conditions of each variable.

A study might have three treatment, conditions (e.g., three methods of reading instruc- C a 0 0 0.50 — 0.45 — 0.40 — 0.35 0.30 0.2e — 0.25 U. C0 E 0.15 a 2 0.

0.or 0.10 — 7-10 Years 11-14 Years Age FIGURE 6—3 Interaction Effect (based on data from Nucci & Nucci, 1982).
Chapter 6 Experimental and Quasi-Experimental Research 161 0.40- & 0.3(— I 7-10 Ysars 11.14 Ysars Ago FIGURE 6—4 No Interaction Effect (based on data from Nucci & Nucci, 1982).

tion), the two sexes, three age groups, and three intelligence levels (gifted, average, and mildly retarded) as the independent variables.

This would be a 3 x 2 X 3 x 3 design and would have a total of 54 subgroups or cells.

Such designs are too complex for this elementary treatment.
We mention such a complex design only to make the reader aware that these designs exist and that they are frequently appropriate and necessary.

Advanced students may wish to refer to such sources as Glass and Hop- kins (1984), Kirk (1982), and Winer (1971) for more detailed information.

This discussion, which has examined the many limitations of the experimental method in behavioral research, may convey a sense of fu- tility.
As is true in many other areas of significant human endeavor, researchers do not work under ideal conditions.

They must do the best they can under existing circumstances.

They will find, however, that in spite of its limitations, the well-designed and well-executed experiment provides a legitimate method for testing hypotheses and making proba- bility decisions about the relationships between variables.

Some variables cannot be manipulated.


The ethical problems that would be raised if some others were manipulated indicates a place for such nonexperimental methods as ex post facto research.

The researcher starts with the observation of dependent variables and goes back to the observation of independent variables that have previously occurred under 162 Part Two Research Methods uncontrolled conditions.
Such studies are not experiments, for the re- searcher has had no control over the events; they occurred before he or she began the investigation.

The description of cigarette-smoking cancer research in Chapter 5 is an example of ex post facto research.

Summary The experimental method provides a logical, systematic way to answer the question, 'If this is done under carefully controlled conditions, what will hap- pen?"
To provide a precise answer, experimenters manipulate certain influ- ences, or variables, and observe how the condition or behavior of the subject is affected or changed.

Experimenters control or isolate the variables in such a way that they can be reasonably sure that the effects they observe can be attributed to the variables they have manipulated, rather than to some other uncontrolled influences.
In testing hypotheses or evaluatrng tentative answers to questions, experimenters make decisions based upon probability rather than certainty.

Experimentation, the classic method of the laboratory, is the most powerful method for discovering and developing a body of knowledge about the prediction and control of events.

The experimental method has been used with some success in the school classroom, where, to some degree, variables can be controlled.
The early applications of experimental method, based upon John Stuart Mill's law of the single variable, have been replaced by the more effective applications of factorial designs made possible by the contributions of R. A. Fisher.
His concept of equating groups by random selection of subjects and random assignment of treatments, and his development of the analysis of variance and the analysis of covariance have made possible the study of complex multivariate relationships that are basic to the understanding of human behavior.

Experimenters must understand and deal with threats to the internal validity of the experiment so that the variable relationships they observe can be interpreted without ambiguity.
They must also understand and deal with threats to the external validity of the experiment so that their findings can be extended beyond their experimental subjects and generalized to a wider population of interest.

Experimental design provides a plan or blueprint for experimentation.

Three preexparimental, three true experimental, and five quasi-experimental designs have been presented, and their appropriate use, advantages, and disadvantages have been briefly discussed.
Experimentation is a sophisticated technique for problem solving and may not be an appropriate activity for the beginning researcher.

It has been suggested that teachers may make their most effective contribution to ed- ucational research by identifying important problems that they encounter in Chapter 6 Experimental and Quasi-Experimental Research 163 their classrooms and working cooperatively with research specialists in the conduct and interpretation of classroom experiments.

Exercises 1.
Exercises 1.

Why is it more difficult to control extraneous variables in a classroom experiment than in a pharmaceutical laboratory experiment?

2.

What significant element distinguishes a quasi-experiment from a true experiment?

3.

Why is an ex post facto study not an experiment?

4.

A researcher, in proposing a research project, defines the dependent variable as achievement in mathematics.

What difficulty does this defi- nition present?

How would you improve it?

5.


How could a double blind be applied in an educational experiment?

6.

Under what circumstances could an independent variable in one study be a dependent variable in another study?

7.

Why is randomization the best method for dealing with extraneousvari- ables?

8 How could a high degree of experimental mortality seriously affect the validity of an experiment?

9.

Read the report of an experiment in an educational research journal.

a.

Was the problem clearly stated?

b.


Were the variables defined in operational terms?

c. Was the hypothesis clearly stated?

d. Were the dehmitations stated?

e. Was the design clearly described?

f. Were extraneous variables recognized?

What provisions weremade to control them?

g. Were the population and the sampling methods described?

h. Were appropriate methods used to analyze the data?

i.

Were the conclusions clearly presented?

j.

Were the conclusions substantiated by the evidence presented?
References Abt Associates (1977).

Education as experimentation: A planned variationmodel.

Cam- bridge, MA: Abt Associates.

Also issued as The follow through planned variation experiment series.

Washington, D. C.: U.S. Office of Education, 1978.

164 Part Two Research Methods ANDERSON, R. B., ST. PIERRE, R. C., PROPER, E. C. & STEBBINS, L. B.

(1978).

Pardon us, but what was the question again?

: A response to the critique of the follow through evaluation.

Harvard Educational Review, 48, 161—170.


CAMPBELL, D. T. & STANLEY, J. C. (1966).

Experimental and quasi-experimental designs for research.

Chicago: Rand McNally.

CooK, T. D. & CAMPBELL, D. T. (1979).

The design and analysis of quasi-experiments for field settings.

Chicago: Rand McNally.

GLASS, C. V. & Hopiur.js, K. D. (1984).

Statistical methods in education and psychology (2nd ed.).

Englewood Cliffs, NJ: Prentice-Hall.

HALL, H. V., PRICE, A.


B., SHINEDLING, M., PEIZER, S. B., & MASSEY, R. H. (1973).

Control of aggressive behavior in a group of retardates using positive and negative reinforcement.

Training School Bulletin, 70, 179—186.

HAYS, W. L. (1981).

Statistics (3rd ed.).

New York: Holt, Rinehart, and Winston.

HoDGES, W. L. (1978).

The worth of the follow through experience.

Harvard Educational Review; 48, 186—192.

HOUSE, E. L., GLASS, G. V., MCLEAN, L. D., & WALKER, D. F. (1978).
No simple answer: Critique of the follow through evaluation.

Harvard Educational Review, 48, 128—160.

KAHN, J. V. (1978).

Acceleration of object permanence with severely and pro- foundly retarded children.

The Association for Severely-Handicapped Review, 3, 15—22.

KERLINGER, F. N. (1986).

Foundations of behavioral research.

(3rd ed.).

New York: Holt, Rinehart, and Winston.

KIRK, R. (1982).

Experimental design: Procedures for the behavioral sciences (2nd ed.).

Belmont, CA: Brooks/Cole.


MAYo, E. (1960).

The human problems of an industrial civilization.

New York: Viking Press.

MILL, J. S. (1873).

A system of logic.

New York: Harper and Row.

NUCCI, L. P. & NUcCI, M. 5.

(1982).

Children's responses to moral and social conventional transgressions in free-play settings.

Child Development, 53, 1337— 1342.

WATANABE, P., HARE, V. C., & LOMAX, R. G. (1984).

Predicting news story content from headlines: An instructional study.

Journal of Reading, 27, 436—442.


WINER, B. J.

(1971).

Statistical principles in experimental design.

New York: McGraw- Hill.

WISLER, C. E., BuRrs, C. P., & IwAM0TO, D. (1978).

Follow through redux: A response to the critique by House, Glass, McLean, and Walker.

Harvard Educational Review, 48, 177—185.

7 Single-Subject Experimental Research THE RESEARCH DESIGNS just described in Chapter 6 all have one common characteristic.

They all are used to study group behavior and change.
Single-subject research, also sometimes referred to as single-case or N of one research, is a particular type of experimental research.

Its distinguish- ing feature is the rigorous study of the effect of interventions on an individual.

While the focus of this type of study is the individual subject, most of these studies include more than one subject.
When there are multiple subjects, the data still are analyzed separately for each subject rather than as a group as would be done in the designs described in Chapter 6.

While there are many fine books on the topic of single-subject re- search, two which the authors find particularly useful are Barlow and Hersen (1984) and Kazdin (1982).

The structure of this chapter and, where indicated, the content, were influenced by these two superb texts.
We recommend these textS to anyone wishing an in-depth coverage of single- subject research.

As with experimental research in general, single-subject research is a method of testing hypotheses.

It also is prone to many of the same threats to internal and external validity to which other research designs are subject.

In particular, many critics of single-subject research question its external validity—in particular, its ability to generalize to other sub- jects.
Proponents point out that this is a problem of most research.

They question whether group analyses are useful fqr determining an individ- 165 166 Part Two Research Methods ual's treatment.

They argue that just because the experimental group outgained the control group on the average does not mean that every person receiving the experimental treatment outgained every person in the control group —or even that everyone in the experimental group improved.
The decision to use a single-subject research design depends, as does the selection of any research desigri, on the purpose of the study, the population of interest, and the situation in which the study is to be conducted.

Single-subject research designs are particularly useful in the study of behavior modification.

Most, if not all behavior modification research uses single-subject designs.

In fact, this type of research and the methodology are so often used together that many people confuse the two.
Behavior modification research studies the effect of a• certain type of intervention, operant conditioning, on individuals.

Single-subject research is a methodology that can be applied to a variety of research topics.

The case study method described in Chapter 8 is the clinical, de- scriptive foundation from which the experimental study of single-subjects developed.

"The development of single-case research, as currently prac- ticed, can be traced to the work of B.F. Skinner (b.
1904), who developed programmatic animal laboratory research to elaborate operant condition- ing" (Kazdin, 1982, p. 10).1 Skinner's (1938, 1953) research methodology, known as the experimental analysis of behavior, included certain features that are characteristic of single-subject research today.

He included only one or a few subjects in each of his studies.

He used the subject as its own control by changing the intervention presented to the subject and studied the impact of the changes on the subject.
Skinner was also very interested in the frequency with which a behavior occurred under various conditions (Kazdin, 1982).

Beginning in the 1950s, a number of investigators adapted Skinner's operant approach and methodology of the experimental analysis of be- havior to humans.

The early laboratory research produced findings that indicated the clinical utility of operant conditioning with a variety of populations (e.g., autistic children, mentally retarded persons, psychiatric patients).
Thus was born the field of applied behavior analysis with its own journal, the Journal of Applied Behavior Analysis, first published in 1968.

Most of the research published in this journal uses single-subject research methods.

In the last two decades, an increasing number of studies using this methodology for operant conditioning and other research topics has appeared in a variety of journals.
Single-subject designs are similar to three of the quasi-experimental designs described in Chapter 6, the time- series design, the equivalent samples design, and the equivalent materials pretest-posttest design.

Each of these designs includes some change in the conditions applied to the subjects with repeated observations or mea- surerzients.

The major difference between these designs and single-subject 1A11 quotes from Kazdin (1982) used with permission of Oxford University Press.
Chapter 7 Single-SubjeCt Experimental Research 167 research designs is that these quasi-experimental designs are used with a group of subjects and the data are analyzed accordingly, while single- subject research is concerned with individuals.

Single-subject research requires careful assessment, repeated obser- vations or measurements, and careful control and applications of the experimental treatment.

This chapter will address these issues and de- scribe the most common designs.
General Procedures Repeated Measurement One aspect of single-subject research is the repeated measurement or observation of the individual.

The purpose is obvious: to determine if changes in the experimental conditions effect changes in the subject.

The careful, systematic use of these repeated observations is critical in order to assure reliable and valid data.

The measurement to be used must be clearly defined.
If, asis com- mon, the procedure is observation, the behaviors to be observed must be carefully defined and observable.

The researcher must also be careful in selecting the behaviors to be observed.

In particular, the behaviors must be ones that the subject would normally be expected to exhibit with a reasonable degree of frequency.
If the measurement procedure includes tests, surveys, or attitude scales, the researcher must select instruments that canbe used repeatedly without the contamination of test or test-interaction effects.

Since elimi- nation of the test and test-interaction effects is often impossible, obser- vation is the primary measurement tool in single-subjectresearch studies.

The measurements also must be used under completely standardized conditions.
The researcher needs to use the same measurements, or ob- servation procedures for each replication of the measurement.

Where possible, the same observers or test givers should be used for all mea- surements.

When this is not possible, the researcher should demonstrate reliability of the measurements across the various personnel used.

The measurements should take place under the same conditions each time they are conducted.
Conditions that should be standardized across mea- surements include the time of day, the circumstances (e.g., during a certain lesson such as spelling), and the general surroundings (e.g., lo- cation, others present) in which the measurements take place.

Baselines The baseline in single-subject research is analogous to a pretest in group research designs.
Baseline data generally are collected by observing the 168 Part Two Research Methods aspect of the individual's behavior that is under study at several times prior to intervention.

Since a baseline is used to determine the status of the subject's behavior prior to the intervention and to predict what the immediate future behavior would be if no intervention was implemented, the baseline must be long enough to determine the trend in the data.
That is, the baseline should demonstrate a stable rate, an increasing rate, or a decreasing rate of the behavior to be modified.

Figure 7—1 provides hypothetical data showing a stable, an improving (increasing), and a wors- ening (decreasing) rate of appropriate behavior.

Since the purpose of the intervention would be to increase the rate of appropriate behavior, only the baseline showing an increasing trend (the middle panel) would present a serious problem in evaluating the effectiveness of the.
intervention.

This Trend -.-- — — 100 50 0 Foo.

50 100 50 C. Sessions FIGURE 7—1 Hypothetical Baseline Data for Attending Behavior.

Top Panel Shows Stable, Middle Panel Increasing, and Bottom Panel Decreasing Trend in the Behavior.

Chapter 7 Single-Subject Experimental Research 169 trend would be a problem because the baseline is already showing a trend in the desired direction.
A baseline must include a minimum of three separate observations but will often include from five to eight, or even more, observations.

The length of the baseline is determined by a number of factors.

Ideally for research purposes, the baseline will continue until a stable trend, with a minimum of variability, is established.

However, ethical considerations may shorten the baseline from the optimal to the minimum acceptable.
For instance, the researcher working on correcting the self-abusive be- havior of an autistic child cannot withhold treatment indefinitely until a satisfactory baseline is established.

Manipulating Variables A fundamental principle of any type of research, particularly true of single- subject research, is that only one variable should be manipulated, or changed, at any given time.
When two or more variables are manipulated during the same phase of a single-subject study, the effect of each cannot be separated.

For instance, in dealing with a hyperactive child, a researcher might want to study the effects of medication and of operant conditioning.

To do such a study properly, the research should follow the baseline with one of the interventions or treatments, let's say the medication.
After a period of time with the medication, the treatment should be removed and the baseline repeated.

Following the second baseline, the researcher would introduce the second intervention, operant conditioning, followed by a third and final baseline period.

The second and third baseline periods are commonly known as withdrawal.

This design will permit a comparison of the effectiveness of the two interventions.
(Ideally, two subjects should be used, with the order of treatments reversed, so as to control for any possible order effect.)

This design would be an A-B-A-B-A design ("A" represents baseline or no intervention and "B" represents an intervention).

If the researcher in the above study had introduced both treatments, medication and operant conditioning, at the same time, with a baseline before and after, the relative effect of each treatment would not be dis- cernible.
While the design looks appropriate on the surface, an A-B-A design (baseline, intervention, baseline), the manipulation of two variables in the same phase, makes it uninterpretable.
Length of Phases When considering the individual length of phases independently of other factors (e.g., time limitations, ethical considerations, relative length of phases), 170 Part Two Research Methods most experimenters would agree that baseline and experimental conditions should be continued until some semblance of stability in the data is apparent.
(Barlow & Hersen, 1984, P. 96)2 That is, the data collection in each phase should continue until there is no upward or downward trend and a fairly constant level of variability between data collection points.

This would obviously result in the phases of a typical study-.--baseline, intervention, baseline, intervention (A-B-A- B)—being radically different in length (Barlow & Hersen, 1984).
On the other hand, Barlow and Hersen (1973) have pointed out problems with having unequal phase and "cited the advantages of ob- taining a relatively equal number of data points for each phase" (Barlow & Hersen, 1984, p. %).

Obviously, some compromises must be made between these two often competing ideals, stability of each phase and equal phase length.
For instance, in some cases it may be necessary for the first intervention to be longer than the initial baseline in order to demonstrate a behavioral change.

In such a case, the subsequent phases, second baseline and intervention, should be the same length as the first intervention in order to replicate the changes in behavior.

"Where pos- sible, the relative equivalence of phase lengths is desirable" (Barlow & Hersen, 1984, p. 97).
A potential problem, which is sometimes related to the length of the intervention phase, is a carryover effect.

A carryover effect is found when the effect of the intervention continues into the next phase, withdrawal.

The purpose of the withdrawal phase is to support the effectiveness of the intervention by demonstrating that the effect disappears (or is at least reduced) when the treatment is removed.
In the typical A-B-A-B design, the treatment is then reintroduced and the effect reappears, thus clearly demonstrating the effectiveness of the treatment.

If the intervention effect carries over to the withdrawal phase (second baseline), there are plausible alternative hypotheses for the behavioral improvement that occurred dur- ing the intervention phase (e.g., maturation, history, etc..).
Bijou, Peterson, Harris, Allen, and Johnston (1969) recommend short interventions to prevent carryover effects "since long ones might allow enough time for the establishment of new conditioned reinforcers" (p. 202).

Thus, once an effect has been demonstrated, the withdrawal phase should be introduced right away.
Barlow and Hersen (1984) suggest al- ternating treatment designs (discussed later in this chapter) and counter- balancing procedures as ways to prevent carryover effects from obscuring the results.

2A11 quotes from Barlow and Hersen used with the permission of Pergamon Books Ltd. Chapter 7 Single-Subject Experimental Research 171 Transfer of Training and Response Maintenance Transfer of training to other situations, settings, or behaviors is of obvious importance in applied behavior analysis.
If a teacher eliminates an un- desirable behavior in his or her classroom but the behavior continues elsewhere, the instructional program has limited success.

Barlow and Hersen (1984) and Kazdin (1982) suggest a number of design options that are useful in providing for, and studying, the transfer of intervention effects.

Similarly, keeping the undesirable behavior from recurring as soon as the reinforcement schedule is eliminated or changed is also relevant.
A child must learn to behave acceptably without receiving tangible rein- forcements for the rest of his or her life.

Thus, maintenance of positive behavioral responses or of the elimination of undesirable responses is a prime purpose of the practitioner.

Various reinforcement schedules result in more or less maintenance of the intervention effect.
The reader should consult one of several fine texts (e.g., Alberto & Troutman, 1986; Cooper, Heron, & Heward, 1987; Suizer-Azaroff & Mayer, 1977) for a detailed discussion of reinforcement schedules and response maintenance.

Barlow and Hersen (1984) describe design strategies that also are useful in study- ing and effecting response maintenance.

Assessment Assessment of the effect of the intervention(s) in single-subject research is usually accomplished by observing the behaviors under study.
Chapter 9 includes a section on the use of observation as a method of data col- lection.

However, the assessment of behavioral change is so central to the issue of single-subject research that certain aspects, primarily relevant to this topic, will be briefly described here.

The texts by Barlow and Hersen (1984) and Kazdin (1982) contain a great deal more detail than can be covered in this introductory treatment.
Target Behavior The target behavior or focus of the research is usually determined by the research or real problem.

If the problem involves the elimination of in- appropriate (e.g., violent, disruptive) behaviors in the classroom, then the target behaviors will obviously be the inappropriate behaviors displayed.
The researcher may need to observe the situation for a period of time prior to implementing the study, in order to determine the precise nature 172 Part Two Research Methods of the behaviors (e.g., hitting other children, calling out, throwing spit- balls, etc.).

Once the researcher fully understands the behavior(s) to be changed, the target behavior needs to be operationally defined.

The definition should refer only to observable aspects of the behavior.
Avoid references to intent or other unobservable components.

The definition should be dearly worded for easy, unambiguous, nonsubjective understanding.

The definition also needs to completely define the outer boundaries of the behavior under study (Barlow & Hersen, 1984).

The, purpose of assessing the target behavior is [first to determine] the extent to which the target behavior is performed before the program [intervention] begins.
The rate of preprogram behavior is referred to as the baseline or operant rate.

Second, assessment is required to reflect behavior change after the intervention is begun.

Since the major purpose of the program is to alter behavior, behavior during the-program must be com- pared with behavior during baseline.

(Kazdin, 1982, pp.

23—24) Data Collection Strategies As stated earlier, the major data collection procedure used in single-subject research is observation of overt behaviors.
There are a number of ways to measure such behaviors."

A frequency measure is simply a count of the number of occurrences of the behavior that are observed during a given period of time.

If a teacher wants to know how frequently a particular student talks without permission, he or she may simply count the number of occurrences during a given class period.

This type of measure is relatively easy and is most useful when the occurrences of the behavior are all of about the same length of time.
More than one behavior are sometimes counted in this procedure (e.g., talking to other children and on-task behavior).

A time-based measure of overt behavior is duration.

In this method, the actual amount of time, during which the individual performs the behavior, is determined.

If an instructional program is designed to teach a mentally retarded student to perform an already mastered task more rapidly, the teacher would want a measure of the duration of the task performance.
Another time-based measure is time sampling or interval recording.

In this method, the observation period, such as a class period, is divided into brief observation/nonobservation intervals.

In a study designed to decrease inappropriate behavior, the observer might observe the child every 30 seconds for a 15-second interval followed by a 15-second non- observation period for recording the observed behaviors.
This method is Chapter 7 Single-Subject Experimental Research 173 frequently used but is considered to have serious flaws (Barlow & Hersen, 1984).

The final method to be described here is real-time observation.

In this procedure, behaviors are recorded'in their actual frequency, duration,and order.

This is an excellent method, but it is rarely used because of the need for expensive recording equipment.

The strategies mentioned thus far are useful for overt behavior.
For research on behaviors that are not overt, other measures are needed.

For a study on weight reduction, the data might indude a count of calories consumed and of distance walked in a day.

These data could be the totals for each day derived from a calorie counter and a pedometer.

These types of measures are called response-specific measures by Kazdin (1982).

Other types of measures used in single-subject research include psychophysio- logical (e.g., pulse, skin temperature) and self-reports.
In single-subject research, the researcher must be able to demonstrate the reliability and validity of the measures used.

For instance, do two observers count the same overt behavior in the same way?

What of the effect of the observer's presence on the person(s) being observed?

These issues are addressed in Chapter 9.

Basic Designs There are two fundmental types of designs that are used in single-subject research, A-B-A and multiple baseline.
Each of these design types will be described and an example of each will be presented.

Other designs that are too complex for this elementary discussion will be mentioned and texts suggested for those interested in more detail.

A-B-A Designs As with all single-subject designs, "A" represents a series of baseline measurements and "B" represents a series of measurements occurring • during the treatment.
Thus, A-B-A includes three phases, baseline, in- tervention, and withdrawal (baseline), each of which represents a series of measurements.

Most research studies of this type are more complex than the most basic A-B-A design.

More often than not, the intervention is reintroduced after the withdrawalphase, resulting in an A-B-A-B design.

While additional baselines and/or treatment phases may be added, further complicating the design, the most common of these designs is the A-B- A-B.
The A-B-A-B design is analogous to the equivalent time-samples design described in Chapter 6.

The primary difference is that the A-B-A- 174 Part Two Research Methods B design assumes continuous measurement of the behavior(s) being stud- ied and the analysis of individual subjects' data.

The equivalent time- samples design may include continuous measurement or finite times for the measurement, and the data are analyzed for the group of subjects.
The A-B-A-B design permits a careful examination of the effects of intervention.

Kazdin (1982) puts it quite well: The ABAB design examines the effects of an intervention by alternating the baseline condition (A phase), when no intervention is in effect, with the intervention condition (B phase).

The A and B phases are repeated again to complete the four phases.
The effects of the intervention are clear if performance improves during the first intervention phase, reverts to or approaches original baseline levels of performance when treatment is withdrawn, and improves when treatment is reinstated in the second intervention phase.

(p. 110) A ciitica1 aspect of the expected events described above is that the direction of the behavior changes each time the intervention is introduced or with- drawn.
Thus, the actual behavior differs from what would have been expected if the conditions were not changed.

Figure 7—2 shows what the A B B Al I I Sessions FIGURE 7—2 Hypothetical Data Showing an Effective Intervention for Increasing Appropriate Behavior in an A-B-A-B Design.

Chapter 7 Single-Subject Experimental Research 175 graph of such data might look like.

Clearly the intervention was effective in this hypothetical example.
Fantuzzo and Clement (1982) used an A-B-A-B design to study the effect of the reinforcement given to one student upon other students.

While the study included a number of conditions and subjects, for the purposes of this discussion we will concentrate on just one aspect of the study.

In this situation, "Al" was to reinforce himself every 60 seconds if he was attending to his assigned task.

"Ed" was able to observe Al and to behave similarly.
At the end of each session Al was able to selectedible rewards based on the number of points he had awarded himself.

Ed was not offered the edible reward regardless of his behavior or the number of points he awarded himself.

The actual percentages of attentive behavior for Al and Ed are given in Figure 7—3.

As can be seen, the treatment was effective with both students even thotigh only Al received the edibles.
The withdrawal and second intervention were also successful in effecting behavior in the directions expected.

Each time the conditions changed, A to B, B to A, and A to B again, the direction of the behaviors changed.

Thus, the study demonstrated successful generalization of reinforcement from one student to another.

Multiple Baseline Designs The designs described in this section are quite different from the A-B-A designs just considered.
In A-B-A designs, the intervention effect is dem- onstrated by withdrawal and, usually, reintroduction of the intervention.

In multiple baseline designs, the intervention effect is demonstrated by having more than one baseline.

Here each baseline represents a different person, setting, or behavior which are the three principal variations of this type of design.
The subsequent baselines (e.g., for the second and third behaviors) are longer than the previous baselines and extend into the previous ones' interventions.

Figure 7—4 provides an example, using FIGURE 7—3 Percentage of Attentive Behavior Across Ex- perimental Phases for Al and Ed.

(Adapted from Fantuzzo & Ciements [1981] with permis- sion of the authors.
Copyright C by the Society for the Ex- perimental Analysis of Behav- ior, Inc.) 176 Part Two Research Methods hypothetical data, of a typical multiple baseline design with three subjects.

As can be seen, each subject shows improvement only after the interven- tion is introduced to that subject.

Multiple baseline designs actually are replication designs.
If each subject or behavior shows the same pattern of response to the treatment, only when the treatment is applied to that subject or behavior, there is strong evidence of a true treatment effect.

By extending the second sub ject's baseline until after an intervention effect is demonstrated for the first subject, the researcher controls for maturation, history, and other threats to the internal validity of the study.
In addition, by demonstrating the treatment effect with more than one subject, the researcher demon- Cl) cg C', 0) I.

FIGURE 7—4 Hypothetical Data Showing an Effective Intervention to Increase Appropriate Behavior Using a Multiple Baseline Design.

Sessions Chapter 7 Single-Suect Experimental Research 177 strates generalizability to other subjects.
Likewise, multiple baseline de- signs that use multiple behaviors or multiple settings also control for various threats to internal validity and demonstrate generalizabiity of the treatment to other behaviors or settings.

McGee, Krantz, and McClannahan (1986) studied the effect of a particular teaching approach, incidental teaching, on the learningof sight words by an autistic child.
They used a multiple baseline design across three sets of words and added a follow-up phase to check for longer-term effects.

As can be seen in Figure 7—5, for each set of words, the percentage of correct responses began to improve only when the treatment was im- plemented on that set of words.

The follow-up at 15 and 25 days also indicated retention of the learned material.

Clearly the treatment was effective and threats to the internal validity of the study were well controlled.
Other Designs In addition to A-B-A and multiple baseline designs, a number of additional options are available to the researcher.

Alternating treatment or multiple treatment designs permit the researcher to compare two or more treatments while controlling for possible order effects.

In these designs, the researcher alternates treatments for each session or randomly assigns the sessions to each treatment.
In the first case, with two treatments, the researcher would simply use treatment 1 in the first postbaseline session, treatment 2 in the second, and so on.

With random assignment, the researcher decides on the number of intervention sessions and randomly assigns each session to a treatment.

Thus, with two treatments and ten sessions, the order of treatment might be 1-1-2-1-2-2-1-2-2-1.

These procedures per- mit a clear comparison of two, or more, treatments.
Those wishing more details regarding this type of design should refer to Barlow and Hersen (1984) or Kazdin (1982).

Researchers also combine the various designs discussed into even more intricate designs.

Kazdin (1982) describes a number of options for doing this.

Barlow and Hersen (1984).

suggest the use of replication in applied research and describe a number of strategies for carrying out replications appropriately.
Evaluating Data In studies comparing the performances of two or more groups of subjects, a statistical test of the differences betweenthe groups is the usual method 178 Part Two Research Methods Incidental Teaching A 15—day £ 25—day 0 4- C C, Acquisition Probes FIGURE 7—5 Percentage of Correct Responses on Acquisition Probes during Baseline Incidental Teaching and at 15- and 25-day Follow-ups.

(Adapted from McGee, Krantz, & McClannahan [1986] with permission of the authors.
Copyright © the Society for the Experimental Analysis of Behavior, Inc.) used for analyzing the effects of the experimental condition.

In single- subject research, however, statistical analyses are rarely used.

Visual in- spection of the data is the method most commonly used to evaluate the effect of the treatment in single-subject studies.

In single-subject designs, the approach is to see if the effect is rep- licated at the appropriate point.
In an A-B-A-B design, the effect should replicate at the beginning of each new phase, the change from A to B, from B to A, and from A to B again.

In a multiple baseline design, the effect should replicate across subjects, behaviors, or settings by occurring at each point that the treatment is applied (Kazdin, 1982).

Visual inspection is relatively easy in cases where there are major changes in the behavior.
For instance, if the behavior never occurs during Chapter 7 Single-Subject Experimental Research 179 baseline and occurs frequently during the intervention, an effect is ob- vious.

However, this is not the usual case, so we must have predetermined characteristics of the data to use in evaluating whether an effect occurred.

Kazdin (1982) suggests two types of change, magnitude and rate, that can be judged.
He further suggests using changes in the average rate of per- formance and in the level at the change point to assess the magnitude of the change.

The average rate of performance is simply the number of occurrences divided by the number of sessions.

A line can be superim- posed on the graph of the data to show any changes.

A change in the level refers "to the shift or discontinuity of performance from the end of one phase to the beginning of the next phase" (Kazdin, 1981, p. 234).
Kazdin (1982) proposes to use changes in trend and latency to assess changes in the rate of the behavior under study.

The trend of the data can be measured by the slope and is the "tendency for the data to show systematic increasesor decreases over time" (p. 235) or to show no change at all (preferable for baseline data).

The latency of the changes refers to how quickly the change occurs after beginning the intervention or with- drawal phase.
Obviously, the more rapidly a change occurs, the better evidence for the treatment having caused the change.

Thus, in evaluating a subject's data, the researcher looks to se if the average performance changes between phases, if a shift in the rate of the behavior occurs between the phases, if the slopes of the data lines are in different directions for the different phases, and how quickly a change occurs after the intervention or withdrawal is introduced.
These and other characteristics discussed by Kazdin (1982) are used to determine if the treatment was effective in changing behavior.

Summary Single-subject experimenta!

research differs from other experimental research in that the focus of the research isón the indiv1ual rather than The procedures used in single-subject research are just as rigorous as in other types of experimental research.

Single-subject research is used to test hypotheses.
In general, this.type of research is used to test the hypothesis that a partIcuIr treaxment will have an overt effect on one or more jehaviors Because most research on behavior modification has used single-subject research methods, the two are often confused and thought to be the same.

While single-subject methodology is appropriate and useful in re- search on behavior modification, it is also appropriate and used for other research topics.
This chapter has emphasized the need to collect data repeatedly and carefully.

The most commonly used method to collect data in this type of research is observation.

Thus, the method of observation, also described in Chapter 9, was considered in some detail here.

180 Part Two Research Methods The need for baseline data and the careful manipulation of variables were described.

.A$sessment of the effects of a sIngle-subject study depend upon having carefully collected baseline and treatment data.
The length of these phases should be kept as similar as possible.

Two fundamental designs, A-B-A and multiple baseline, were described in detail and an actual study.of each was presented.

A-B-A designs usually Include a second intervention, A-B-A-B, and are sOmetimes referred to as withdrawal designs.

Multiple baseline designs include two or more replications across persons, behaviors, or settings.
The baseline for later replications are long*r than the earlier ones, thereby controlling for threats to the internal validity of such studies.

The data in single-subject research is usually evaluated through visual inspec- tion.

Statistical analysis is rare.

Visual inspection considers such factors as changes in the magnitude and rate of the behaviors being studied.

Exercises 1.

What distinguishes single-subject research from other forms of experi- mental research?

2.
2.

Single-subject research is similar.

to certain quasi-experimental designs.

Discuss these similarities and how they are dissimilar.

3.

Why is single-subject research confused with behavior modification?

In what ways are they different?

4.

What is a baseline?

How does the initial baseline differ from a subsequent one in an A-B-A design?

5.

A researcher's baseline stabilizes after four sessions and she begins the intervention.
If there is no effect after four sessions, should she continue the treatment or reintroduce the baseline?

Why?

6.

Most single-subject studies are of overt behavior.

What other types of research might use single-subject methods?

7.

How does an A-B-A-B design control for threats to the internal validity of the study?

8.

How does a multiple baseline design control for threats to the external validity of the study?

9.

Read the report of a single-subject experiment in a journal.

a.
a.

What design was used?

b.

Were the variables clearly defined?

c. Was the hypothesis clearly stated?

d. Would a group design (Chapter 6) have been better?

Why or why not?

e. Were the phase lengths appropriate?

f. What method was used to collect the data?

If observation, how were the data recorded?

Chapter 7 Single-Subject Experimental Research 181 g. How were the data evaluated?

Was the evaluation appropriate?

h. Were the conclusions clearly stated?

i.


Were the conclusions substantiated by the data presented?

Ret erences ALBERTO, P. A.

& TROUTMAN, A. C. (1986).

Applied behavior analysis for teachers (2nd ed.).

Columbus, OH: Merrill.

BARLOW, D. H. & HERSEN, M. (1984).

Single case experimental designs (2nd ed.).

New York: Pergamon.

BIjou, S. W., PETERSON, R. F., HARRIs, F. R., ALLEN, K. E., & JOHNSTON, M. S. (1969).

Methodologies for experimental studies of young children in natural settings.
Psychological Record, 19, 177—210.

COOPER, J.

0., HERON, T. E., & HEWARD, W. L. (1987).

Applied behavior analysis.

Columbus, OH: Merrill.

FANTUZZO, J. W. & CLEMENT, P. W. (1981).

Generalization of the effects of teacher- and self-administered .token reinforcers to nontreated students.

Journal of Applied Behavior Analysis, 14, 435—447.

KAZDIN, A. E. (1982).

Single-case research designs.

New York: Oxford.

McGEE, G. G., KRANTZ, P. J., & McCLANNAHAN, L. E. (1986).
An extension of incidental teaching procedures to reading instruction for autistic children.

Journal of Applied Behavior Analysis, 19, 147—157.

SKINNER, B. F. (1938).

The behavior of organisms.

New York: Appleton-Century- Crofts.

SKINNER, B. F. (1953).

Science and human behavior.

New York: Macmillan.

SuLzER--AZAROFF, B.

& MAYER, G. R. (1977).

Applying behavior analysis with children and youth.

New York: Holt, Rinehart & Winston.


Qualitative Research AS ALLUDED TO IN THE INTRODUCTION to Part Two, research methodologies can be divided into two major paradigms, logical-positivism and phenom- enological inquiry.

Logical-positivism dominates educational research and is based on the assumptions of the natural sciences.

This is the paradigm elaborated upon in the description of the scientific method, hypothesis testing, etc.

in Chapter 1.
in Chapter 1.

Chapters 5, 6, and 7 also describe research methodologies based upon this paradigm.

This chapter elaborates on the other paradigm, phenomenological inquiry as broadly defined.

There has been much confusion over just what qualitative research is.

One of the problems is that qualitative research is often described by a negative.

That is, it is research that is nonquantitative.

This is a problem for two reasons.


First, some qualitative research results in some quanti- fication (e.g., counting the number of occurrences of a particular behavior).

Second, it represents a negative connotatiOn; the issue might be more accurately put that quantitative research is based on the logical-positive paradigm, which utilizes experimental research methodologies, while qualitative research is based on the phenomenological paradigm, which uses a variety of interpretive research methodologies.
Another problem in understanding qualitative research methods lies with the misconception that qualitative research is a unitary approach, when in reality qualitative research is a variety of alternative approaches to the traditional, positivistic research most often found in the literature.

A variety of terms have been used for the various forms of qualitative methods, including: ethnographic, case study, phenomenological, con- structivist, participant observational, and so on.
Erickson (.1985) uses "the term interpretive to refer to the whole family of approaches to participant observational research" (p. 119), one of the most common types of qualitative research methods.

He uses this term, which could be used for all qualitative methods, because it avoids the 183 184 Part Two Research Methods suggestion that the.
approaches are simply nonquantitative, it is more inclusive than some other terms (e.g., ethnography), and a key feature of this "family" of approaches is that the researcher plays a more central role in the elucidation and interpretation of the behaviors observed.
Lin- coln and Guba (1985) refer to this type of research as naturalistic inquiry, which implies that participant observational techniques result in a more natural approach than the tests, surveys, and the like used in the more traditional quantitative approaches.

Qualitative research uses different forms of data than those used in traditional research methods.
As Patton (1990) sets forth, Qualitative methods consist of three kinds of data collection: (1) in-depth, open-ended interviews; (2) direct observation; and (3) written documents.

The data from interviews consist of direct quotations from people about their experiences, opinions, feelings, and knowledge.
The data from observations consist of detailed descriptions of people's activities, actions, and the full range of interpersonal interactions and organizational processes that are part of observable human experience.
Document analysis in qualitative inquiry yields excerpts, quotations, or entire passages from organizational clinical or pro- gram records; memoranda and correspondence; official publications and re- ports; personal diaries; and open-ended written responses to questionnaires and surveys.1 Qualitative research studies use these techniques in isolation or in com- bination.

Themes of Qualitative Research Patton proposes ten themes which inculcate qualitative research.
These themes are summarized in Table 8—1.

The themes described are what make the various qualitative research methods both distinct from quan- titative methods and similar to each other.

Although not all qualitative thethods are consistent with all ten themes, they all have most of these themes in common.

At the same time, quantitative methods, based on the logical-positivistic paradigm, do not adhere to any of these themes.
All qualitative research methods have in common the use of qualitative data, context sensitivity, emphatic neutrality, and inductive analysis, as well as other themes.

At the same time, while most qualitative studies make use of naturalistic inquiry, it is possible for resarch to be qualitative and not use this theme.

For instance, philosophical research may not include ob- 'From M. Q. Patton, Qualitative Evaluation and Research Methods (ewbwy Park, CaRL: Sage Publications, © 1990), p. 12.
Used with permission of Sage Publications.

TABLE 8—1 Themes of Qualitative Inquiry 1.

Naturalistic inquiry 2.

Inductive analysis 3.

Holistic perspective 4.

Qualitative data 5.

Personal contact and insight 6.
Dynamk systems Studying real-world situations as they unfold naturally; nonmanipulative, unobtrusive, and noncontrolling openness to whatever emerges—lack of predetermined constraints on outcomes Immersion m the details and specifics of the data to discover important categories, dimensions, and interrelationships; begin by exploring genuinely open questions rather than testin theoretically derived (deductive) hypotheses The whole phenomenon under study is understood as a complex system that is more than the sum of its parts; focus on complex interdependencies not meaningfully reduced to a few discrete variables and linear, cause-effect relationships Detailed, thick description; inquiry in depth; direct quotations capturing people's personal perspectives and experiences The researcher has direct contact with and gets close to the peopleq situation, and phenomenon under study; researcher's personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon Attention to process; assumes change is constant and ongoing whether the focus is on an individual or an entire culture 7.
Unique case orientation Assumes each case is special and unique; the first level of 8.

Context sensitivity 9.

Empathic neutrality 10.
Design flexibility inquiry is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends on the quality of individual case studies Places findings in a social, historical, and temporal context; dubious of the possibility or meaningfulness of generalizations across time and space Complete objectivity is impossible; pure subjectivity undermines credibility; the researcher's passion is understanding the world in all its complexity—not proving something, not advocating, not advancing personal agendas, but understanding; the researcher includes personal experience and empathic insight as part of the relevant data, while taking a neutral nonjudgmental stance toward whatever content may emerge Open to adapting inquiry as understanding deepens and/or situations change; avoids getting locked into rigid designs that eliminate responsiveness; ptirsues new paths of discovery as they emerge Source: M. Q. Patton.
Qualitative Evaluation and Research Methods (Newbury Park, Calif.: Sage Publications, © 1990), Table 2.1, pp.

40—41.

Used with permission of Sage Publications.

185 186 Part Two Research Methods servations of real-world situations.

On the other hand, these themes are the antithesis of the logical-positivistic, quantitative paradigm.

For in- stance, the use of hypotheses in quantitative research precludes inductive analysis.
Quantitative research is designed to test hypotheses that are theoretically derived.

Whether the hypotheses are supported or refuted, the researcher reports the results objectively.

Qualitative researchers, on the other hand, do not bring such hypotheses to their research.

Perhaps the most relevant of Patton's themes for this discussion are three of the ones specifically mentioned in the previous paragraph, qual- itative data, context sensitivity, and inductive analysis.
That qualitative re- search methods rely on qualitative data probably seems obvious.

Yet this point cannot be emphasized too much.

The very nature of the in-depth, detailed descriptions of events, interviews, and the like is what makes qualitative research so powerful.

The richness of the data permits a fuller understanding of what is being studied than could be derived from the experimental research methods described in the previous chapters.
While those methods are important for their careful control of confounding var- iables, they do not permit the type of in-depth study of individuals that is the essence of qualitative research.

Context sensitivity cannot be completely separated from the theme of qualitative data.

The reason that qualitative data are so powerful is that they are sensitive to the social, historical, and temporal context in which the dta were collected.
The particular importance of context sensitivity is th, the data are not generalized to other contexts, socially, spatially, or temporally.

Just because something occurs in one classroom at a par- ticular school and at a particular time does not mean that similar occur- rences should be expected elsewhere or even in the same classroom at another time.
The logical-positivistic paradigm, on the other hand, pur- posefully pursues research findings that can be generalized to other set- tings, persons, and times.

Inductive analysis enables the researcher to explore the data without prior hypotheses.

This openness to find whatever there is to find is unique to qualitative research.

It permits the researcher to discover reality without having to fit it into a preconceived theoretical perspective.
This is obviously the antithesis of the logical-positivistic approach, which insists that re- search be based on hypotheses generated from theory, prior research, or experience.

Two other of Patton's themes deserve special mention.

The holistic perspective taken by qualitative researchers is important for understanding the complex nature of many aspects of human and organizational behav- ior.
Two examples of research issues that cduld benefit from the holistic approach are the study of student characteristics that are relevant to learn- ing, and the study of qualities that make for successful schools.

Design flexibility is critical for qualitative research.
Whereas experimental research is carefully planned prior to commencing data collection with no possibility Chapter 8 QualitatIve Research 187 of change once started, qualitative research is open to change throughout the data collection process.

This permits the researcher to adjust the di- rection of the inquiry based on the ongoing experience of collecting and thinking about the data.
Research Questions "In qualitative research, questions and problems for research most often come from real-world observations, dilemmas, and questions" (Marshall & Rossman, 1989, p. 28).

Marshall and Rossman go on to suggest a number of queStions with wide-ranging implications such as: "Why is the program working well in this school but not in others?

What is special about the people?

The plan?

The support?

The con- text?".


(p. 28) "What are the various techniques used by lobbying groups as they try to influence education policy?

Which are perceived tobe the most effective?"

(p. 28).

"How does play affect reading readiness?

Through what cognitive and affective processes?"

"How do school—parent cmmunity relations of private and paro- chial schools differ from those of public schools?

How are the dif- ferences connected with differences in educational philosophies and outcomes?"
(p. 29).

"How do school superintendents gain control over school board members?

What influence processes do they use?"

(p. 29)2 28).

Other research questions that best could be answered with qualitative research methods indude: How do school principals spend their time?

What do teachers think the principal's role is and/or should be?

What actually takes place at school board meetings in a particular school district based on observations and school board minutes?
How do young children react to standardized testing situations?

How do state legislators view the state affiliates of the National Education Association and the American Federation of Teachers?

2From C. Marshall and G. Rossman, Designing Qualitative Research (Newbury Park, Calif.: Sage Publications, © 1989), pp.

28, 29.

Used with permission of Sage Publications.

188 Part Two Research Methods These are but a few possible questions that would use qualitative methods.
Others can be found in any school, school district, or govern- mental agency.

All one needs to do is to look for areas undergoing change.

These "new" curricula, administrative arrangements, reform movements, and the like are ripe for qualitative inquiry.

Theoretical Traditions There are a number of different ways to view the theoretical perspectives of the various methodologies subsumed under the term qualitative research.
Jacobs (1987) describes six perspectives from ecological psychology (be- havioral settings and specimen records), anthropology (holistic ethnog- raphy, ethnography of communication, and cognitive anthropology), and social psychology (symbolic interactionism).

Although this is a useful sys- tem, it is not comprehensive and it complicates the issue by fragmenting some fields of study (e.g., two types of ethnography and two areas of ecological psychology).
Thus, rather than using Jacob's categories, we will refer again to Patton (1990), who provides a wider, yet simpler, system of categorizing the theoretical traditions upon which most qualitative re- search is based.

Patton (1990) provides a description of ten theoretical perspectives of qualitative research.

These perspectives, their disciplinary roots, and the questions they pose are summarized in Table 8—2.
We will provide a brief description of those perspectives which have the most relevance to educational research.

The reader interested in a more comprehensive analysis of any of these methods should refer to the references and suggested additional readings at the end of this chapter.

Which method is "best" will depend upon the research question to be answered and the perspective of the researcher.
Ethnography, in addition to being a theoretical perspective, also is a method for carrying out qualitative observational research.

The basicpur- pose of ethnographic research is to determine the physical and social environment in which the individuals under study live, go to school, work, and so forth.

The roots for ethnography are in anthropology, which includes the study of cultural phenomena.
In educational research, eth- nography has been used to study the culture of schools and the people who inhabit them.

A study will be described later in this chapter which used ethnography to study school principals.

Symbolic interactionism stems from social psychology and has been used to a great extent in linguistics as well.

A premise of symbolic inter- actionism is the belief that people act according to how they understand the meanings of words, things, and acts in their environment.
It is im- portant for teachers in our multicultural society to understand that stu- dents coming from different cultural backgrounds will understand the TABLE 8—2 Variety in Qualitative Inquiry: Theoretical Traditions Perspective Disciplinary Roots Central Questions 1.

Ethnography Anthropology 2.

Phenomenology Philosophy 3.

Heuristics Humanistic psychology 4.

Ethnomethodology Sociology 5.

Symbolic interactionism Social psychology 6.

Ecological psychology Ecology, psychology 7.


Systems theory Interdisciplinary 8.

Chaos theory: nonlinear dynamics Theoretical physics, natural sciences 9.

Hermeneutics Theology, philosophy, literary criticism What is the culture of this group of people?

What is the structure and essence of experience of this phenomenon for these people?

What is my experience of this phenomenon and the essential experience of others who also expe- rience this phenomenon intensely?
How do people make sense of their everyday activities so as to behave in socially acceptable ways?

What common set of symbols and understand- ings have emerged to give meaning to people's interactions?

How do individuals at- tempt to accomplish their goals through specific be- haviors in specific envi- ronments?

How and why does this system function as a whole?

What is the underlying order, if any, of disor- derly phenomenon?
What are the conditions under which a human act took place or a prod- uct was produced that makes it possible to in- terpret its meanings?

How is x ideological per- pective manifest in this phenomenon?

10.

Orientational, qualitative Ideologies, political economy _______ Source: Patton, Qualitative Evaluation and Research Methods (0 1990), cable 3.2, P. 88.

Used with permission of Sage Publicatibns.

189 190 Pail Two Research Methods world differently.
Test performance, motivation to please the teacher or do well in school, and the like will differ depending upon the students' cultural history.

Research using a symbolic interactionism perspective might investigate how different children, from different backgrounds, perceive and comprehend the school environment.

The ecological psychology perspective attempts to understand the con- nection between a person's behavior and her or his environment.
Ecolog- ical "psychologists believe that behavior is goal directed.

Thus, when a child misbehaves in class, the ecological psychologist looks for the reason.

The behavior's goal may be to receive attention, or it may be something not as obvious.

For instance, a child, who is asked to perform a task (e.g., read a passage from a book) that he or she cannot do may misbehave rather than demonstrate the inability (to read).
This perspective assumes that the behavior is related to the individual's needs as well as the context or environment in which the behavior occurs.

Ecological psychologists collect data through observing the individual and the environment.

They keep detailed descriptions of both and then code these descriptions for numerical analysis.

Thus, the methodology is qualitative while the data analysis is quantitative.
The reader should note that it is possible for "one to go from the thick description of qualitative data to quantitative analysis, but not vice versa.

One cannot generate thick description and qualitative narrative from original quantitative data" (Patton, 1990, p. 78).

Research studies using a systems theory perspective are often large, interdisciplinary studies.

They are attempts to find out how a system or organization functions, what works and what does not work.
For instance, studies that attempt to determine the characteristics of successful schools will need to examine a variety of issues about the school itself (e.g., administration, teacher characteristics, student characteristics), the envi- ronment in which the school exists including the family structure of the students, and the interaction of these various factors.
Such a large-scale study could involve a range of disciplines, including a variety of'depart- ments found in colleges of education as well as nursing, psychology, sociology, public health, social work, and so on.

Reseach Strategies Marshall and Rossman (1989) suggest three primary strategies that can be used to assist the researcher in designing appropriate data collection pro- cedures.

The choice of strategy "depends o the focus of the research and the desired time frame for the study.
Examples of strategies include life histories, case studies, and field studies" (pp.

75—75).

Ethnography falls within the more general category of field studies.

Other strategies include Chapter 8 Qualitative Research 191 experimental methods from the logical-positivistic paradigm, surveys, and document analyses.

In selecting the most appropriate strategy, the researcher must first determine what is to be studied.
If one is interested in studying the ways in which teachers organize their days at work, some form of field study is probably most suitable.

On the other hand, if one wished to study a person with mental retardation who has a particular ability in music (i.e., a savant), the life history approach would be more relevant Another factor in determining the most appropriate strategy is the time period under study; is the study about something contemporary or from the past?
Obviously, field studies would not be possible if the event to be studied was historical in nature.

In such a case, some other strategy would be needed such as a life history or document analysis.

Three strat- egies—document and content analysis, case studies, and ethnography— will be described in greater detail.

Document or Content Analysis Documents are an important source of data in many areas of investigation, and the methods of analysis are similar to those used by historians.
The major difference between this type of research and historical research is that, while historical research often uses document analysis, it deals solely with past events.

When document analysis is used as descriptive research, current documents and issues are the foci.

The analysis is concerned with the explanation of the status of some phenomenon at a particular time or its development over a period of time.
It serves a useful purpose in adding knowledge to fields of inquiry and in explaining certain social events.

Its application to educational research is suggested in some of the studies listed as examples.

In documentary analysis, the following may be used as sources of data: records, reports, printed forms, letters, autobiographies, diaries, compositions, themes or other academic work, books, periodicals, bulle- tins or catalogues, syllabi, court decisions, pictures, ifims, and cartoons.
When using documentary sources, one must bear in mind that data appearing in print are not necessarily trustworthy.

Documents used in descriptive research must be subjected to the same careful types of criti- cism employed by the historian.

Not only is the authenticity of the doc- ument important, but the validity of its contents is crucial.

It is the re- searcher's obligation to establish the trustworthiness of all data that he or she draws from documentary sources.
The following purposes may be served through documentary anal- ysis (examples of actual studies are given as illustrations).

The first five 192 Part Two Research Methods purposes are of a descriptive research nature, while the subsequent three are historical in nature: 1.

To describe prevaffing practices or conditions.

Entrance Requirements of Ohio Colleges as Revealed by an Analysis of College Bulletins Criteria for Primary Pupil Evaluation Used on Marion County Repoit Cards 2.
To discover the relative importance of, or interest in, certain topics or problems.

Public Information on Education as Measured by Newspaper Coverage in Three Indianapolis Daily Newspapers during the Month of Deember, 1958 Statistical Concepts Presented in College Textbooks in Educational Research Published since 1940 3.

To discover the level of difficulty of presentation in textbooks or in other publications.
The Vocabulary Level of Intermediate Science Textbooks Abstract Concepts Found in First-grade Readers 4.

To evaluate bias, prejudice, or propaganda in textbook presentation.

The Soviet Union as Presented in High School History Textbooks The Free Enterprise System as Pictured in High School Social Problems Textbooks Racial and Religious Stereotypes in JuniOr High School Literature Textbooks 5.

To analyze types of errors in students' work.


Typing Errors of First Semester Typing Students at Shortridge High School Errors in English Usage Found in Letters of Application for Admission to the University of Wisconsin 6.

To analyze the use of symbols representing persons, political parties or institutions, countries, or points of view.
Great Britain as a Symbol, as Represented in New York City Newspaper Cartoons in the Decade, 1930—1940 The New Dealer as Depicted in the American Press from 1932 to 1942 7.

To identify the literary style, concepts, or beliefs of a writer.

Shakespeare's Use of the Metaphor Alexander Campbell's Concept of the Trinity, as Revealed in His Sermons John Dewey's Interpretation of Education as Growth 8.

To explain the possible causal factors related to some outcome, ac- tion, or event.
The Effect of Media Coverage upon the Cutcome of the 1976 Presidential Election The Influence of Newspaper Editorials upon the Action of the State Assembly on Sales Tax Legislation Content or document analysis should serve a useful purpose in yield- ing information that is helpful in evaluating or explaining social o edu- Chapter 8 QualitatIve Research 193 cational practices.
Since there are so many significant areas to be inves- tigated, setting up studies for the pure joy of counting and tabulating has little justification.

"The Uses of Shall and Will in the Spectator Papers" or "The Use of Too, Meaning Also, in the Works of Keats" would seem to add little useful knowledge to the field of literature.

The Case Study The case study is a way of organizing social data for the purpose of viewing social reality.

It examines a social unit as a whole.


The unit may be a person, a family, a social group, a social institution, or a community.

The purpose is to understand the life cycle or an important part of the life cycle of the unit.

The case study probes deeply and analyzes interactions between the factors that explain present status or that influence change or growth.

It is a longitudinal approach, showing development over a period of time.
The element of typicalness, rather than uniqueness, is the focus of attention, for an emphasis upon uniqueness would preclude scientific abstraction.

As Bromley (1986) notes, "A 'case' is not only about a 'person' but also about that 'kind of person'.

A case is an exemplar of, perhaps even a prototype for, a category of individuals" (p. 295).
Thus, the selec- tion of the subject of the case study needs to be done carefully in order to assure that he or she is typical of those to whom we wish to generalize.

Data may be gathered by a wide variety of methods, including 1.

Observation by the researcher or his or her informants of physical characteristics, social qualities, or behavior 2.

Interviews with the subject(s), relatives, friends, teachers, counse- lors, and others 3.

Questionnaires, opinionnaires, psychological tests and inventories 4.
Recorded data from newspapers, schools, courts, clinics, government agencies, or other sources.

A single case study emphasizes analysis in depth.

Though it may be fruitful in developing hypotheses to be tested, it is not directed toward broad generalizations.

One cannot generalize from a number (N) of 1.

To the extent that a single case may represent an atypical situation, the observation is sound.
But if the objective analysis of an adequate sample of cases leads researchers to consistent observations of significant variable relationships, hypotheses may be confirmed, leading to valid generali- zations.

The individual case study has been a time-honored procedure in the field of medicine and medical research.

Sigmund Freud was a pioneer in using case study methods in the field of psychiatry.
In an effort to treat his psychoneurotic patients, he began to discover consistent patterns of 194 Part Two Research Methods experience.

Under his careful probing, patients recalled long-forgotten, traumatic incidents in their childhood and youth.

Freud hypothesized that these incidents probably explained their neurotic behavior (Strachey, 1964).
His famous case history of Sergei Petrov, "the Wolf Man," published in 1918 under the title From the History of an Infantile Neurosis, is one of the classic examples of Freud's use of the case study.

He believed that these case studies confirmed his hypothesis, leading to psychoanalysis as a method of treatment.

He also used them to demonstrate how theoretical models could be used to provide concrete examples.

Case studies are not confined to the study of individuals and their behavioral characteristics.
Case studies have been made of all types of communities, frOm hamlet to great metropolis, and of all types of indi- viduals—alcoholics, drug addicts, juvenile delinquents, migratory work- ers, sharecroppers, industrial workers, members of professions, execu- tives, army wives, trailer court residents, members of social classes, Quakers, Amish, members of other religious sects and denominations, black Amer- icans, American Indians, Chinese-Americans, Hispanics, and many other social and ethnic groups.
Such institutions as colleges, churches, corrective institutions, welfare agencies, fraternal organizations, and business groups have been studied as cases.

These studies have been conducted for the purpose of understanding the culture and the development of variable relationships.

For example, a community study is a thorough observation and analysis of a group of people living together in a particular geographic location in a corporate way.
The study deals with such elements of com- munity life as location, appearance, prevailing economic activity, climate and natural resources, historical development, mode of life, social struc- ture, goals or life values and patterns, the individuals or powergroups that exert the dominant influence, and the impact of the outside world.

It also evaluates the social institutions that meet the basic human needs of health, protection, making a living, education, religious expression, and recreation.
The early community studies of Lynd and Lynd are well known.

The first, Middletown (1929), and the second, Middletown in Transition (1937), de- scribed the way of life in Muncie, Indiana, a typical midwestern, average- size city, tracing its development from the gas boom of the 1890sthrough World War I, the prosperity of the nineteen-twenties, and the depression of the nineteen-thirties.

West (1945) described the nature of a very small community in the Ozark region in Plainville, USA.
Sherman and Henry (1933) studied the way of life in five "hollow" communities, hidden in the Blue Ridge Mountains, in Hollow Folk.

- Some community studies have singled out particular aspects for spe- cial investigation.

Drake and Cayton (1945) described life in the black section of Chicago in Black Metropolis.

Hollingshead (1949) portrayed the life of adolescents in a small Illinois community in Elmt own's Youth.
Warner Chapter 8 Qualitative Research 195 and Lunt (1941) developed a hypothesis of social dass structure in a New England community in their study of Newburyport, Massachusetts, in Social Life of a Modern Community.

Lucas (1970) compared theway of life in three Canadian communities in Minetown, Militown, Railtown: Life in Canadian Communities of Single Industry.

Although the case study is a useful method of organizing research observations, certain precautions should be considered: 1.
The method may look deceptively simple.

To use it effectively, the researcher must be thoroughly familiar with existing theoretical knowledge of the field of inquiry, and skillful in isolating the sig- nificant variables from many that are irrelevant.

There is a tendency to select variables because of their spectacular nature rather than for their crucial significance.

• 2.

Subjective bias is a constant threat to objective data-gathering and analysis.
The danger of selecting variable relationships based upon preconceived convictions and the apparent consistency of a too lim- ited sample of observations may lead the researcher to an unwar- ranted feeling of certainty about the validity of his or her conclusions.

3.

Effects may be wrongly attributed to factors that are merely associ- ated rather than cause-andeffect related.
Ethnographic Studies Ethnography, sometimes known as cultural anthropology or more recently as naturalistic inquiry, is a method of field study observation that became popular in the latter part of the nineteenth century.

It has continued to show significant development, suggesting promising techniques for the study of behavior in an educational situation.
In its early application, it consisted of participant observation, conversation, and the use of inform- ants to study the cultural characteristics of primitive people: African, South Sea Island, and American Indian tribes.

These groups were small in num- ber, geographically and culturally isolated, with little specialization in social function, and with simple economies and technology.
Such cultural features as language, marriage and family life, child-rearing practices, religious beliefs and practices, social relations and rules of conduct, po- litical institutions, and methods of production were analyzed.

The data gathered consisted of observation of patterns of action, verbal and nonverbal interaction between members of the tribe as well as between the subjects and the researcher and hi or her informants, •and the examination of whatever records or artifacts were available.
Many early studies were subsequently criticized on the grounds that the anthropologist spent too little time among the people of the tribe to get more than a superficial view, didn't learn the native language and 196 Part Two ResearctiMethods had to depend too much on the reports of poorly trained informants, and relied too much on hIs or her own cultural perspective, reaching ethno- centric, judgmental conclusions that resulted in stereotyped theories of the development of the primitive society.
Later investigators realized that studies of this type would be invalid unless the observer 1.

Lived for a much more extensive period of time among the tribe and became an integrated member of the social group 2.

Learned the native language, enabling him or her to develop the sensitivity to think, feel, and interpret observations in terms of the tribe's concepts, feelings, and values, while at the same time sup- plementing his or her own objective judgment in interpreting ob- servations.

3.
3.

Trained his or her informants to systematically record field data in their own language and cultural perspective.

This refinement of participant observation resulted in more objective and valid observation and analysis.

Some stuckes were directed toward the examination of the total way of life of a group.

Other studies singled out a particular phase of the culture for intensive analysis, taking into account those elements that were relevant to the problem.
In her classic study, Coming of Age in Samoa, Mead (1928) observed the development of 53 adolescent girls in a permissive Samoan society.

She conduded that there were no differences in the physical processes of adolescent growth between Samoan and American girls: The differences were differences in response.

The difficulties of this period of develop- ment, a troublesome feature of American life, do not occur in Samoa.
She attributed the difference to Samoa's more homogeneous culture, a single set of religious and moral beliefs, and a wider kinship network that con- ferred authority and affection.

The difficulties of American girls were attributed to cultural restraints, not nature.

Many of the time-honored techniques of the ethnographic study involving integration into the group and observation are being applied to psychology and education, as well as anthropology and sociology.
An excellent example of this methodology applied to an educational issue is a recent study of school principals.

Morris, Crowson, Porter-Gehne, and Hurwitz (1984) were interested in determining exactly what principals actually do and how much time is spent on those activities.

Their pro- cedure was to have each principal observed for up to 12 full work days.

The observers followed the principal wherever he or she went.
The authors "were interested in whom the principal interacted with and by what means (verbal face to face, written word, telephone, etc.).

We wanted to know which party initiated each interchange, whether it was planned or spon- taneous, how long it lasted, and where it took place.
Most important, we Chapter 8 Qualitative Research 197 wanted to follow the changing subject matters of theseconversations, not only to see what topics consumed the principal's time but also to trace the rhythm of the principal's working hours" (Morris, et al., 1984, p. v).3 One of the conclusions of this study was that principals usually spend less than half their work day in their offices, that they have a good deal of discretion in their decision-making, and that the principal's behavior "affects four distinct 'constituencies' ": teachers and students, parentsand others in the community, superiors, and the principal him- or herself (Morris, et al., 1984, p. v).
Using the method of observation, the researcher observes, listens to, and sometimes converses with the subjects in as free and natural an atmosphere as possible.

The assumption is that the most important be- havior of individuals in groups is a dynamic process of complex interac- tions and consists of more than a set of facts, statistics, or evendiscrete incidents.
The strength of this kind of study lies in the observation of natural behavior in a real-life setting, free from the constraints of more conventional research procedures.

Another assumption is that human behavior is influenced by the setting in which it occurs.

The researcher must understand that setting and the nature of the social structure; its traditions, values, and norms of behavior.
It is important to observe and interpret as an outside observer but also tO observe and interpret in terms of the subjects—how they view the situation, how they interpret their own thoughts, words, andactivities, as well as those of others in the group.

The researcher gets inside the minds of the subjects, while at the same time interpreting thebehavior from his or her own perspective.

The relationship of researchers to their subjects is based upon trust and confidence.
Researchers do not allow themselves to be alignedwith either the authority figures or the subjects.

A position of neutrality is essential to objective participatt observation.

Unlike conventional deductive quantitative research, participant ob- servers begin without preconceptions and hypotheses.

Using inductive logic, they build their hypotheses as they are suggested by observations.
They periodically reevaluate them on the basis of new observations, mod- ifying them when they appear to be inconsistent with the evidence.

They look for negative evidence to challenge their temporary hypotheses.

In a sense, this type of research has the characteristics of a series of consecutive studies.

The interpretation is not deferred to the conclusion but is a con- stant ongoing process of testing tentative hypotheses against additional observations in a real situation.
Ethnographic methods of research ha'e been used to investigate such problems as: 1.

Student Leadership Roles in an Urban, Racially Integrated HighSchool 3Used with the permission of the authors and of Charles E. Merrill Publishing Co. 198 Part Two Research Methods 2.

Pupil-Teacher Relationships in a Suburban Junior High School 3.

Social Relationships in a Class of Emotionally Disturbed Children 4.

Changes in Attitudes and Behavior in a Drug Abuse Rehabilitation Center 5.
The Social Class Structure of a Florida, Cuban-American Community 6.

Staff-Parent Interactions in an Individualized Education Plan (IEP) Staffing Data Collection Techniques Chapter 9 covers a wide variety of data collection procedures, including tests, observation, and interviews, which can be used to collect either quantitative or qualitative data.

However, while qualitative methods are included, the major focus of that chapter is on the way that various tools can be used in quantitative studies.
Thus, this section will review some of the techniques that are most useful in qualitative research.

Marshall and Rossman (1989) point to two techniques that are the critical procedures for collecting qualitative data: observation and inter- viewing.

While a number of other approaches exist, and will be mentioned later, it appears that most qualitative research studies in education utilize observation, interviews, or a combination of the two as all or part of their data collection procedures.
Observations When observation is used in qualitative research, it usually consists of detailed notation of behaviors, events, and the contexts surrounding the events and behaviors.

On the other hand, in quantitative research, ob- servation is usually employed to collect data regarding the number of occurrences in a specific period of time, or the duration, of very specific behaviors or events (see Chapter 7).
The detailed descriptions collected in qualitative research can be converted later to numerical data and ana- lyzed quantitatively, but the reverse is not possible.

Patton (1990) proposes five dimensions along which observations vary.

First, the observer's role may vary from full participant to complete outsider.

A teacher observing in his or her own class would be a participant observer, whereas a research assistant sitting unobtrusively in the back of an auditorium would not.
Second, the observer may conduct the ob- servations covertly (e.g., from behind a one-way mirror or casually "hang- ing around" the halls), with the full knowledge of those being observed, or with only some of those being observed aware of the observation (e.g., the teacher knows but the students do not).

Third, those being observed Chapter 8 Qualitative Research 199 may be given full explanations, partial explanations, no explanations, or given a false explanation.

The fourth dimension is duration.


The obser- vations may take place over the course of an entire school year (or even long) or could be as brief as an hour.

The final dimension is the breadth of focus.

The observations may vary from quite broad (e.g., an entire elementary school's curriculum) to quite narrow (e.g., how students in a particular class respond to a substitute mathematics teacher).
Observations can be of the setting or physical environment, social interactions, physical activities, nonverbal communications, planned and unplanned activities and interactions, and unobtrusive indicators (e.g., dust on equipment may indicate a lack of use).

The observer also should be alert for nonoccurrences, things that should have happened but did not.
The second author and a colleague conducted a quantitative study some years ago on the percentage of-time that students were spending "on task" (performing the assigned activity) versus "off task."

The most intriguing finding was that for a large percentage of the time (over 40 percent of the average class period) no task was assigned.

The nonoc- currence of the teacher's assigning work to students partially explained the finding that students were on task less than 50 percent of the class period.
Thus, what was not occurring was the most interesting aspect of the results.

Interviews "The purpose of interviewing is to find out what is in or on someone else's mind.

The purpose of open-ended interviewing is not to put things in someone's mind (for example, the interviewer's preconceived categories for organizing the world) but to access the perspective of the person being interviewed" (Patton, 1990, p. 278).

In conducting interviews, it is im- portant to keep this quote from Patton in mind.
Interview data can easily become biased and misleading if the person being interviewed is aware of the perspective of the interviewer.

Too often, interviewees provide information based upon what they think the interviewer wants to hear.

Therefore, it is critical for the interviewer to make sure the person being interviewed understands that the researcher does not hold any precon- ceived notions regarding the outcome of the study.
Interviews range from quite informal and completely open-ended to very formal with the questions predetermined and asked in a standard manner (e.g., the questions may be read to the interviewee to assure the same wording with all those being interviewed).

Table 8—3 summarizes the characteristics of four types of interviews along a continuum from very informal to very formal and provides the strengths and weaknesses of each.
8 u E 8 •0)E •J:1 L8 0 I I I- 0 o E o bC 0 I- 0)— a) 8 0 : I IUI.I 0 I_I '4.1 Ia 0) .

0 > .—v 0 Ia 0 I 0— — J.

0) 0) .. • f2 a) ('4 C0 a, C a, E 2 'I, C a, C C (0 C0 (0 c) 'U- 200 .

Ic U L = rI V • V V V V•V 0• gj U U ) .. - ViI • U 0 5 V C,)V C,)D' • • • •E v— Hhi I ) .
V .4 0' V • V E '•ø NV C V• cio.E 0 V C,) j V ... V Cl)VcJ V Cl) OC,) I LU 2 201 202 Part Two Research Methods Interviews are used to gather information regarding an individual's experiences and knowledge; his or her opinions, beliefs, and feelings; and demographic data.

Interview questions can be asked so as to determine past or current information, as well as predictions for the future.

The preferred method for data collection is to tape record the interview if the respondent is willing.
Otherwise, the interviewer must keep notes con- temporaneously and expand on them as necessary immediately following the interview while the information is still fresh in the interviewer's mind.

Other Qualitative Data Collection Techniques Marshall and Rossman (1989) provide descriptions of 12 categories of "supplemental data collection techniques" (p. 83) following their deline- ation of the two "basic" ones, observation and in-depth interviewing.
While all of the 12 are worthy of use, we will include only the most pertinent for this discussion.

Questionnaires and surveys can be used to gather either quantitative or qualitative data.

This category, along with opinionnaires, is covered in Chapter 9 and needs no further description here.

Similarly, projective techniques and psychological tests can be used quan- titatively or qualitatively and are covered adequately in Chapter 9.
"Proxemics i the study of people's use of space and its relationship to culture" (Marshall & Rossman, 1989, p. 89).

Studies have been con- ducted in a variety of environments using this technique.

Uses in edu- cation would include studies of "the effect of seating arrangements on student behavior" (Marshall & Rossman, 1989, p. 89) or the effect of organizing classrooms into various work areas on student learning, stu- dent behavior, and/or teacher behavior.

Kinesics is the study of body movements.


People communicate jon- verbally in many situations.

This communication is often at a subconscious level and can provide insights into the individual's current state or mood.

Kinesics also can be useful in combination with interviews.

A person's posture, facial expressions, and the like can provide additional information beyond her or his answers to the interviewer's questions.

Street ethnography could be adapted to studies of schools.
This form of ethnography concentrates on the person's becoming a part of the place under study.

To study the homeless, it might mean spending a great deal of time with them on the street.

Similarly, to understand the perspective of the members of a local school council in a Chicago public school might require becoming a member so as to be a full participant on that "street."
All of the data collection techniques described in this chapter, and for that matter in the next chapter too, have strengths and weaknesses.

One way to emphasize the strengths and minimize the weaknesses is to use more than one method in a study.

By selecting complementary meth- ods, a researcher can cover the weaknesses of one method with the strengths Chapter 8 Qualitative Research 203 of another.

Thus, good qualitative research will often include multiple methods of data collection.
For instance, a research strategy described earlier, ethnography, most often includes observation and interviews.

This permits the researcher to verify interview information with observed events and to better understand what is observed through interview responses.

The use of multiple data collection techniques is known as triangulation.

Triangulation of data permits the verification and validation of qualitative data.
Data Analysis The data analyses described in Part Three of this book deal entirely with quantitative, statistical analyses.

Thus, we will provide a brief description of qualitative data analysis here.

For a more detailed discussion on the subject, we suggest the source we relied upon for this brief analysis of the topic, Patton's splendid book (1990, pp.

369—506).
369—506).

"The challenge is to make sense of massive amounts of data, reduce the volume of information, identify significant patterns, and construct a framework for communicating the essence of what the data reveal" (Pat- ton, 1990, pp.

371—372).

The problems are myriad.

Replication of the researcher's thought processes is impossible.

There are no tests of relia- bility and validity.

There are no commonly agreed-upon procedures for this task.
The first step in analyzing qualitative research involves organizing the data.

Qualitative research often results in voluminous notes from obser- vations, interviews, andlor documents.

The method of organizing these data will differ depending upon the research strategy and data collection technique(s) used.
Interview data, for instance, may be organized accord- ing to individual respondents or, if a standard interview format is used with a number of individuals, by grouping answers together across re- spondents.

Similarly, observations may be considered individually or by grouping similar types of occurrences together while also looking for differences among individuals, settings, or times.
Which approach is taken will depend upon the purpose of the research; the number of individuals, settings, and/or times observed or interviewed; and whether the focus is on the particular person, setting, or time under study or on similarities and differences among the persons, settings, or times under study.

Once the data have been organized, the researcher can move to the second stage in data analysis, description.
The researcher describes the various pertinent aspects of the study including: the setting, both tem- porally arid physically; the individuals being studied; the purpose of any activities examined; the viewpoints of participants; and the effects of any activities on the participants.

204 Part Two Research Methods Only after the data have been organized and described does the researcher begin the final and most critical phase of the analysis process, interpretation.
Interpretation involves explaining the findings, answering "why" questions, attaching significance to particular results, and putting patterns into an analytic framework.

It is tempting to rush into the creative work of inter- preting the data before doing the detailed, hard work of putting together coherent answers to major descriptive questions.

But description comes first.

The discipline and rigor of qualitative analysis depend on presenting solid descriptive data .

.

.
.

.

in such a way that others reading the results can under- stand and draw their own interpretations.4 This last point of Patton's is crucial.

The interpretation of qualitative research data is more dependent on the researcher's background, skills, biases, and knowledge than conclusions drawn from quantitative research which are derived more directly from the numerical analyses of the data.
Thus, it is critical that the reader of qualitative research have access to the descriptive information upon which the researcher's interpretations are based.

Only in this manner can the reader fully comprehendhow the researcher reached her or his conclusions and interpretations and agree or disagree with them.

Summary Qualitative research differs from the traditional logical-positivistic, quantitative re- search in a variety of ways.
In qualitative research the focus is on in-depth interviews, observations, and document analysis.

A holistic perspective permits a broader view of the complex issues facing educational researchers.

In addition, while some qual- itative research includes limited quantification (e.g., counting the number of occur- rences of an event), in general, qualitative research interprets data without numerical analysis.
This chapter has provided an overview of qualitative research and how it differs from the quantitative research methods described elsewhere in this book.

A number of themes of qualitative research were related to assist in describing the common elements of the various forms of qualitative methods.

Three of the most common forms of qualitative research also were presented: document and content analysis, case studies, and ethnography.
This chapter also includes a brief overview of qualitative data analysis.

Three steps in this process were described.

The first is data organization.

This is critical due to the often voluminous data collected in a qualitative study.

The second step is 4Patton, Qualitative Evaluation and Research Methods (0 1990), p. 375.

Used with per- missjon of Sage Publications.

Chapter 8 Qualitative Research 205 description of the data, the setting in which they were collected, and the participants in the study.
Finally, the data must be interpreted.

Exercises 1.

What distinguishes qualitative research from quantitative research?

De- scribe three distinguishing characteristics.

2.

Suggest a research topic that could be answered best by using: a. case study b. ethnography c. content and document analysis 3.

System theory is an interdisciplinary approach to qualitative resQarch.
Propose a research question that would use this perspective and name the appropriate disciplines that should participate in the study.

4.

There are five dimensions on which observations vary from one another.

What are these dimensions and how do they affect the process of ob- servational data collection?

5.

What are the strengths and weaknesses of each of the following types of interviews?

a.
a.

Informal conversational interview b. interview guide approach c. standardized open-ended interview d. closed, fixed-response interview 6.

Suggest an appropriate research topic for each of the types of interviews listed in question 5.

References York: John Wiley.

BROMLEY, D. B.

(1986).

The case study method in psychology and related disciplines.

New Ditu, S. C. & CAYTON, H. R. (1945) Black metropolis.

New York: Harcourt Brace, ERICKSON, F. (1985).

Qualitative methods in research on teaching.


In M. C. Wittrock (Ed.

), Handbook of research on teaching (3rd ed.).

New York: Macmillan, 119— 161. and World.

HOLLINGSHEAD, A.

B.

(1949).

Elmstown's youth.

New York: John Wiley.

JACOB, E. (1987).

Traditions of qualitative research: A review.

Review of Educational Research, 51, 1—50.

LINCOLN, Y. S. & GUBA, E. G. (1985).

Naturalistic inquiry.

Beverly Hills, CA: Sage.

LUCAS, R. A (1970).


Minetown, milltown, railtown: Life in Canadian communities of single industry.

Toronto, Canada: University of Toronto Press.

LYND, R. S. & LYND, H. M. (1929).

Middletown.

New York: Harcourt Brace.

206 Part Two Research Methods LYND, R. S. & LYND, H. M. (1937).

Middletown in transition.

New York: Harcourt, Brace, and World.

MARSHALL, C. & ROSSMAN, G. B.

(1989).

Designing qualitative research.

Newbury Park, CA: Sage.

MEAD, M. (1928).


Coming of age in Samoa.

New York: William Morrow.

MoRnIs, V. C., CROWSON, R., PORTER-GEHRIE, C., & Huiwrrz, E. (1984).

Principals in action.

Columbus, OH: Charles Merrill.

PATrON, M. Q.

(1990).

Qualitative evaluation and research methods (2nd ed.).

Newbury Park, CA: Sage.

SHERMAN, M. & HENRY, T. R. (1933).

Hollow folk.

New York: Thomas Y. Crowell.

Snci-sy, J.

(Ed.).

(1964).

The complete psychological works of Sigmund Freud, Vol.

XVII.
XVII.

London: Hogarth.

WARNER, W. L. & Lur'rr, P. S. (1941).

Social lift in a modern community, Vol.

1 New Haven, CT: Yale University Press.

WEST, J.

(1945).

Plainville, USA.

New York: Columbia University Press.

Additional Readings EISNER, E. W. (1991).

The enlightened eye: Qualitative inquiry and the enhancement of educational practice.

New York: Macmillan.

EVERTSON, C. M. & GREEN, J. L. (1985).

Observation as inquiry and method.

In M. Wittrock (Ed.
In M. Wittrock (Ed.

), Handbook of research on teaching (3rd ed.).

New York: Mac- millan.

CUBA, E. G. & LINCOLN, Y. S. (1981).

Effective evaluation.

San Francisco, CA: Jossey- Bass.

JAEGER, R. M.

(Ed.).

(1988).

Complementary methods: For research in education.

Wash- ington, DC: American Educational Research Association.

Scorr, W. R. (1965).

Field methods in the study of organization.

In J. C. March (Ed.

), Handbook of organizations.

Chicago, Rand McNally.

SMYLIE, M. A.


& BROWNLEE-CONYERS, J (1992).

Teacher leaders and their principals: Exploring the development of new working relationships.

Educational Admin- istration Quarterly, 28, 150—184.

SMYLIE, M. A.

& DENNY, J. W. (1990).

Teacher leadership: Tensions and ambiguities in organizational perspective.

Educational Administration Quarterly, 26, 235— 259.

STAKE, R. E. (1978).

The case method in social inquiry.

Educational researcher, 7, 5—8.

WILSON, S. (1977).
WILSON, S. (1977).

The use of ethnographic techniques in educational research.

Review of Educational Research, 47, 245—265.

YIN, R. K. (1984).

Case study research: Design and methods.

Beverly Hills, CA: Sage.

9 Methods and Tools of Research TO CARRY OUT any of the types of research investigation described in the preceding chapters, data must be gathered with which to test the hy- pothesis.

Many different methods and procedures have been developed to aid in the acquisition of data.
These tools employ distinctive ways of describing and quantifying the data.

Each is partiularly appropriate for certain sources of data, yielding information of the kind and in the form that can be most effectively used.

Many writers have argued the superiority of the interview over the questionnaire, or the use of the psychological test over the interview.
The late Arvil S. Barr, University of Wisconsin teacher and researcher, resolved discussions of this sort by asking, "Which is better, a hammer or a hand- saw?"

Like the tools in the carpenter's chest, each is appropriate in a given situation.

Some researchers become preoccupied with one method of inquiry and neglect the potential of others.
Examining the publications of some authors shows that many studies use the same method applied to many different problems, possibly indicating that the authors have become at- tached to one particular method and choose problems that are appropriate to its use.

There is probably too much dependence upon single methods of inquiry.
Because each data-gathering procedure or device has its own particular weakness or bias, there is merit in using multiple methods, supplementing one with others to counteract bias and generate more adequate data.

Students of research should familiarize themselves with 207 208 Part Two Research Methods each of these research tools and attempt to develop skill in their use and sensitivity to their effectiveness in specific situations.
- Reliability and Validity of Research Tools Reliability and validity are essential to the effectiveness of any data-gathering procedure.

These terms are defined here in the most general way.

A more detailed discussion is presented later in the chapter.

Reliability is the degree of consistency that the instrument or pro- cedure demonstrates: Whatever it is measuring, it does so consistently.
Validity is that quality of a data-gathering instrument or procedure that enables it to measure what it is supposed to measure.

Reliability is a necessary but not sufficient condition for validity.

That is, a test must be reliable for it to be valid, but a test can be reliable and still not be valid.

It is feasible through a variety of statistical treatments to quantita- tively assess the reliability and validity of psychological tests and inven- tories.
It is more difficult to determine these qualities for some other data- gathering instruments or procedures, such as observation, interview, or the use of the questionnaire, in which responses are more qualitative and yield data that are not always readily quantifiable.

One should attempt to improve the reliability and validity of the procedures, but precise de- termination of the degree to which they are achieved is often elusive, particularly in the case of validity.
A brief consideration of the problems of validity and reliability fol- lows the discussion of each type of data-gathering procedure.

Quantitative Studies Quantification has been defined as a numerical method of describing ob- servations of materials or characteristics.

When a defined portion of the material or characteristic is used as a standard for measuring any sample, a valid and precise method of data description is provide4.
Scientists distinguish among four levels of measurement, listed and described in the following.

A Nominal Scale.

A nominal scale is the least precise method of quan- tification.

A nominal scale describes differences between things by as- signing them to categories—such as professors, associate professors, as- sistant professors, instructors, or lecturers—and to subsets such as males or females (see Table 9—1).

Nominal data are counted data.


Each individual can be a member of only one set, and all other members of the set have the same defined characteristic.
Such categories as nationality, gender, socioeconomic sta- Chapter 9 Methods and Tools of Research 209 TABLE 9—1 Academic Rank of Members of the Instructional Staff of Southland College Professors Associate professors Assistant professors Instructors Lecturers Totals Male 20 34 44 26 17 141 Female 4 22 30 14 5 75 Total 24 56 74 40 22 216 tus, race, occupation, or religious affiliation provide examples.
Nominal scales are nonorderable, but in some situations this simple enumeration or counting is the only feasible method of quantification and may provide an acceptable basis for statistical analysis.

An Ordinal Scale.

Sometimes it is possible to indicate not only that things differ but that they differ in amount or degree.

Ordinal scales permit the ranking of items or individuals from highest to lowest.
The criterion for highest to lowest ordering is expressed as relative position or rank in a group: 1st, 2nd, 3rd, 4th, 5th,.

.

.

nth.

Ordinal measures have no absolute values, and the real differences between adjacent ranks may not be equal.

Ranking spaces them equally, though they may not actually be equally spaced.
The following example illustrates this limitation: Subject Jones Smith Brown Porter Taylor Height in Inches Difference in Inches 76 68 66 59 58 8 2 7 1 Rank 1st 2nd 3rd 4th 5th An Interval Scale.

An arbitrary scale based on equal units of measure- ments indicates how much of a given characteristic is present.

The dif- ference in amount of the characteristic possessed by persons with scores of 90 and 91 is assumed to be equivalent to that between persons with scores of 60 and 61.
The interval scale represents a decided advantage over nominal and ordinal scales because it indicates the relative amount of a trait or char- acteristic.

Its primary limitation is the lack of a true zero.

It does not have the capacity to measure the complete absence of the trait, and a measure 210 Part Two Research Methods of 90 does not mean that a person has twice as much of the trait as someone with a score of 45.
Psychological tests and inventories are interval scales and have this limitation, although they can be added, subtracted, multiplied, and divided.

A Ratio Scale.

A ratio scale has the equal interval properties of an interval scale but has two additional features: 1.

.The ratio scale has a true zero.

It is possible to indicate the complete absence of a property.

For example, the zero point on a centimeter scale indicates the complete absence of length or height.

2.
2.

The numerals of the ratio scale have the qualities of real numbers and can be added, subtracted, multiplied, and divided and expressed in ratio relationships.

For example, 5 grams is one-half of 10 grams; 15 grams is three times 5 grams; and on a laboratory weighing scale, two 1-gram weights will balance a 2-gram weight.

One of the ad- vantages enjoyed by practitioners in the physical sciences is the ability to describe variables in ratio scale form.
The behavioral sci- ences are generally limited to describing variables in interval scale form, a less precise type of measurement.

Proceeding from the nominal scale (the least precise type) to ratio scale (the most precise), increasingly relevant information is provided.

If the nature of the variables permits, the scale that provides the most precise description should be used.

In behavioral research, many of the qualities or variables of interest are abstractions and cannot be observed directly.
It is necessary to define them in terms of observable acts, from which the existence and amount of the variables are inferred.

This operational definition tells what the researcher must do to measure the variable.

For example, intelligence is an abstract quality that cannot be observed directly.

Intelligence may be defined operationally as scores achieved on a particular intelligence test.

Operational definitions have limited meaning.


Their interpretation is somewhat subjective, which may lead experts to disagree about their validity.

The fact that numerical data are generated does not insure valid observation and description, for ambiguities and inconsistencies are often represented quantitatively.

Some behavioral scientists feel that excessive emphasis on quantifi- cation may result in the measurement of fragmentary qualities not relevant to real behavior.
The temptation to imitate the descriptive measures of the physical scientist has led some behavioral researchers to focus their attention on trivial, easy-to-measure elements of behavior, resulting in pretentious studies of little value.

The limitations that have been mentioned are not intended to min- imize the significance of quantitative methods.

Progress is being made in Chapter 9 Methods and Tools of Research 211 developing more valid operational definitions'and better observation tech- mques.
The quantitative approach is not only useful but maybe considered indispensable in most types of research.

It has played an essential role in it pogrood from pure phi1 the history and developMeM of osophical speculation to modem empirical, verifiable observation.

Qualitative Studies As discussed in Chapter 8, qualitative studies are those in which the description of observations is not ordinarily expressed in quantitative terms.
It is not that numerical measures are never used but that other meansof description are emphasized.

For example, in the studies described in Chapter 8, when the researcher gathers data by participant observation, interviews, and the examination of documentary materials, little measure- ment may be involved.

However, observations may be dassified into discrete categories, yielding nominal level data.
Piaget, a scientist who had a distinguished research career of more than 50 years, came to the conclusion that a nonquantitative search for explanations would be fruitful in the study of human development.

His qualitative approach, known as genetic epistemology, has suggestedanother method of observing behavior and the nature of human growth and de- velopment.
He built his logic of operations upon what he observed when children of different age levels were confronted with tasks that required reasoning for their solution.

In some types of investigation, events and characteristics are appro- priately described qualitatively.

See Chapter 8 for some examples of re- search questions for which qualitative data are appropriate.

This chapter focuses on methods of collecUng quantitative data.
However, some of the techniques described in this chapter also are appropriate for qualitative data.

In particular, projective tests, observation, open-ended question- naires and opimonnaires, and interviews are used in qualitative research.

Chapter 8 also covers some of these data collection techniques from a qualitative research perspective.

To conclude this discussion on quantitative and qualitative studies, several observations may be appropriate.
It may be unwise to try todraw a hard-and-fast distinction between qualitative and quantitative studies.

The difference is not absolute; it is one of emphasis.

One emphasisshould not be considered superior to the other.

The appropriate approach would depend upon the nature of the questions under consideration and the objectives of the researchers.

Traditionally, educational research has emphasized the quantitative approach.
A substantial number of researchers feel that qualitative studies have, for too long, remained outside the mainstream of educational re- 212 Part Two Research Methods search.

Some investigations could be strengthened by supplementing one approach with the other.

Psychological Tests and Inventories As data-gathering devices, psychological tests are among the most useful tools of educational research, for they provide the data for most experi- mental and descriptive studies in education.
Because here we are able to examine only limited aspects of the nature of psychological testing, stu- dents of educational research should consult other volumes for a more comp'ete discussion (such as Anastasia, 1982; Cronbach, 1984).

A psychological test is an instrument designed to describe and mea- sure a sample of certain aspects of human behavior.

Tests may be used to compare the behavior of two or more persons at a particular time or of one or more persons at different times.
Psychological tests yield objec- tive and standardized descriptions of behavior, quantified by numerical scores.

Under ideal conditions, achievement or aptitude tests measure the best performance of which individuals are capable.

Under ideal conditions, inventories attempt to measure typical behavior.
Tests and inventories are used to describe status (or a prevailing condition at a particular time), to measure changes in status produced by modifying factors, or to predict future behavior on the basis of present performance.
In the simple experiment on reading headlines described in the chap- ter on experimental research (Chapter 6), test scores were used to equate the experimental and control groups, to describe relative skill at this task prior to the application of the teaching methods, to measure student gains resulting from the application of the experimental and control teaching methods, and to evaluate the relative effectiveness of teaching methods.
This example of classroom experimentation illustrates how experimental data may be gathered through the administration of tests.

In descriptive research studies, tests are frequently used to describe prevailing conditions at a particular time.

How does a student compare with those of his or her own age or grade in school achievement?

How does a particular group compare with groups in other schools or cities?
In school surveys for the past several decades, achievement tests have been used extensively in the appraisal of instruction.

Because tests yield quantitative descriptions or measure, they make possible more pre- cise analysis than can be achieved through subjective judgment alone.

There are many ways of classifying psychological tests.

One distinc- tion is made between performance tests and paper-and-pencil tests.
Perfor- mance tests, usually administered individually, require that the subjects manipulate objects or mechanical apparatis while their actions are ob- served, and recorded by the examiner.

Paper-and-pencil tests, usually administered in groups, require the subjects to mark their response on a prepared sheet.

Chapter 9 Methods and Tools of Research 213 Two other classes of tests are power versus timed or speed tests.
Power tests have no time limit, and the subjects attempt progressively more difficult tasks until they are unable to continue successfully.

Timed or speed tests usually involve the element of power, but in addition, they limit the time the subjects have to complete certain tasks.

Another distinction is that made between nonstandardized, teacher- made tests and standardized tests.
The test that the classroom teacher constructs is likely to be less expertly designed than that of the profes- sional, although it is based upon the best logic and skill that the teacher can command and is usually "tailormade" for a particular group of pupils.

Which type of test is used depends on the test's intended purpose.

The standardized test is designed for general use.


The items and the total scores have been carefully analyzed, and validity and reliability have been established by careful statistical controls.

Norms have been established based upon the performance of many subjects of various ages living in many different types of communities and geographic areas.
Not only has the content of the test been standardized, but the administration and scoring have been set in one pattern so that those subsequently taking the tests will take them under like conditions.

As far as possible, the interpretation has also been standardized.
Although it would be inaccurate to claim that all standardized tests meet optimum standards of excellence, the test authors have attempted to make them as sound as possible in the light of the best that is known by experts in test construction, administration, and interpretation.

Nonstandardized or teacher-made tests are designed for use with a specific group of persons.

Reliability and validity are not usually estab- lished.
However, more practical information may be derived from a teacher- made test than from a standardized one because the test is given to the group for whom it was designed and is interpreted by the teacher/test- maker.

Psychological tests may also be classified in terms of their purpose— that is, what types of psychological traits they describe and measure.

Achievement Tests Achievement tests attempt to measure what an individual has learned— his or her present level of performance.
Most tests used in schools are achievement tests.

They are particularly helpful in determining individual or group status in academic learning.

Achievement test scores are used in placing, advancing, or retaining students at particular grade levels.

They are used in diagnosing strengths and weaknesses and as a basis for awarding prizes, scholarships, or degrees.
Frequently achievement tests scores are used in evaluating the in- fluences of courses of study, teachers, teaching methods, and other factors considered to be significant in educational practice.

In using tests for 214 Part Two Research Methods evaluative purposes, it is important not to generalize beyond the specific elements measured.
For example, to identify effective teaching exclusively with the limited products measured by the ordinary achievement test would be to define effective teaching too narrowly.

It is essential that researchers recognize that the elements of a situation under appraisal need to be evaluated on the basis of a number of criteria, not merely on a few limited aspects.

Aptitude Tests Apttude tests attempt to predict the degree of achievement that may be expected from individuals in a particular activity.
To the extent that they measure past learning, they are similar to achievement tests.

To the extent that they measure nondeliberate or unplanned learning, they are different.

Aptitude tests attempt to predict an individual's capacity to acquire im- proved performance with additional training.

Actually, capacity (or aptitude) cannot be measured directly.
Apti- tude can only be inferred on the basis of present performance, particularly in areas where there has been no deliberate attempt to teach the behaviors to be predicted.

Inteffigence is a good example of a trait that cannot be measured directly.

An individual's intelligence quotient (IQ) is generally derived from comparing his or her current knowledge with a group of persons of equal chronological age who were administered the test by the author or the author's employees.
If a person scores relatively high, average, or low, we assume that it is a measure of how effectively a person has profited from both formal and informal opportunities for learning.

To the extent that others have had similar opportunities, we predict an individual's ability for future learning.

This is a matter of inference rather than of direct measurement.
Because it has proved useful in predicting future achievement, particularly in academic pursuits, we consider this concept of intelligence measurement a valid application.

Aptitude tests have been designed to predict improved performance with further training in many areas.

These inferred measurements have been applied to mechanical and manipulative skills, musical and artistic pursuits, and many professional areas involving many types of predicted ability..
In music, for example, ability to remember and discriminate between differences in pitch, rhythm pattern, intensity, and timbre seems to be closely related to future levels of development in musicianship.

Present proficiency in these tasks provides a fair predictive index of an individual's ability to profit from advanced instructioi, particularly when the individ- ual .has had little formal training in music prior to the test.
Aptitude tests may be used to divide students into relatively homo- geneous groups for instructional purposes, identify students for scholar- Chapter 9 Methods and Tools of Research 215 ship grants, screen individuals for particular educational programs, or help guide individuals into areas where they are most likely to succeed.
Aptitude tests, particularly those that deal with academic aptitude, that are used for purposes of placement and dassification have become highly controversial, and their use has been prohibited in many com- munities.

The fact that some individuals with culturally different back- grounds do not score well on these tests has led to charges of discrimi- nation against members of minority groups.
The case has been made that most of these tests do not accurately predict academic achievement be- cause their contents are culturally biased.

Efforts are being made to de- velop culture-free tests that eliminate this undesirable quality; However, it is extremely difficult to eliminate culture totally and develop one test that is equally fair for all.

Interest Inventories Interest inventories attempt to yield a measure of the types of activities that an individual has a tendency to like and to choose.
One kind of instrument has compared the subject's pattern of interest to the interest patterns of successful practitioners in a number of vocational fields.

A distinctive pattern has been discovered to be characteristic of each field.

The assumption is that an individual is happiest and most successful working in a field most like his or her own measured profile of interest.

Another inventory is based on the correlation between a number of activities from the areas of school, recreation, and work.
These related activities have been identified by careful analysis with mechanical, com- putational, scientific, persuasive, artistic, literary, musical, social service, and clerical areas of interest.

By sorting the subject's stated likes and dislikes into various interest areas, a percentile score for each area is obtained.

It is then assumed that the subject will find his or her area of greatest interest where the percentile scores are relatively high.
Interest blanks or inventories are examples of self-report instruments in which individuals note their own likes and dislikes.

These self-report instruments are really standardized interviews in which the subjects, through introspection, indicate feelings that may be interpreted in terms of what is known about interest patterns.

Personality Inventories Personality scales are usually self-report instruments.
The individual checks respOnses to certain questions or statements These instruments yield scores which are assumed or have been shown to measure certain per- sonality traits or tendencies.

216 Part Two Research Methods Because of individuals' inability or unwillingness to report their own reactions accurately or objectively, these instruments may be of limited value.

Part of this limitation may be-due to the inadequate theories of personality upon which some of these inventories have been based.
At best, they provide data that are useful in suggesting the need for further analysis.

Some have reasonable empirical validity with particular groups of individuals but prove to be invalid when applied to others.

For example, one personality inventory has proven valuable in yielding scores that correlate highly with the diagnoses of psychiatrists in clinical situa- tions.

But when applied to college students, its diagnostic value has proved disappointing.
The development of instruments of personality description and mea- surement is relatively recent, and it is likely that continued research in this important area will yield better theories of personality and better instruments for describing and measuring its various aspects.

The Mooney Problems Check List (1941) is an inventory to be used by students in reporting their own problems of adjustment.

The subjects are asked to indicate on the checklist the things that trouble them.
From a list of these items, classified into different categories, a picture of the students' problems, from their own viewpoint, is drawn.

Although the most useful interpretation may result frotu an item analysis of personal problems, the device does yield a quantitative score which may indicate the degree of difficulty that students feel they are experiencing in their adjustment.
This instrument has been used as a research device to identify and describe the nature of the problems facing individuals andgroups of individuals in a school.

The tendency to withhold embarrassing responses and to express those that are soc,ially acceptable, emotional involvement of individuals with their own problems, lack of insight—all these limit the effectiveness of personal and social-adjustment scales.
Some psychologists believe that the projective type of instrument offers greater promise, for these devices attempt to disguise their purpose so completely that the subject does not know how to appear in the best light.

Projective Devices A projective instrument enables subjects to project their internal feelings, attitudes, needs, values, or wishes to an external object.

Thus the subjects may unconsciously reveal themselves as they react to the external object.
The use of projective devices is particularly helpful in counteracting the tendency of subjects to try to appear in their best light, to respond as they believe they should.

Projection may be accomplished through a number of techniques: Chapter 9 Methods and Tools of Research 217 1.

Association.

The respondent is asked to indicate what he or she sees, feels, or thinks when presented with a picture, cartoon, ink blot, word, or phrase.
The Thematic Apperception Test, the Rorschach Ink Blot Test, and various word-association tests are familiar examples.

2.

Completion.

The respondent is asked to complete an incomplete sen- tence or task.

A sentence-completion instrument may indude such items as: My greatest ambition is My greatest fear is I most enjoy I dream a great deal about I get very angry when If I could do anything I wanted it would be to 3.

Role-playing.
Role-playing.

Subjects are asked to improvise or act out a situation in which they have been assigned various roles.

The researcher may observe such traits as hostifity, frustration, dominance, sympathy, insecurity, prejudice—or the absence of such traits.

4.

Creative or constructive.

Permitting subjects to model clay, finger paint, play with dolls, play with toys, or draw or write imaginative stories about assigned situations may be revealing.
The choice of color, form, words, the sense of orderliness, evidence of tensions, and other reactions may provide opportunities to infer deep-seated feelings.

Qualities of a Good Test or Inventory Reliability A test is reliable to the extent that it measures whatever it is measuring consistently.

In tests that have a high coefficient of reliability, errors of measurement have been reduced to a minimum.
Reliable tests are stable in whatever they measure and yield comparable scores upon repeated administration.

An unreliable test is comparable to a stretchable rubber yardstick that yields different measurements each time it is applied.

The reliability or stability of a test is usually expressed as a correlation coefficient.

There are a number of types of reliability: 1.

Stability over time (test—retest).


The scores on a test will be highly correlated with scores on a second administration of the test to the same subjects at a later date if the test has good test—retest reliability.

2.

Stability over item samples (equivalent or parallel forms).

Some tests have two or more forms that may be used interchangeably.
In these cases, the scores on a test will be highly correlated with scores on an alternative form of the test (for example, scores on form A will 218 Part Two Research Methods be highly correlated with scores on form 13) if the test has this type of reliability.

3.

Stability of items (internal consistency).

Scores on certain test items will be highly correlated with scores on other test items.

There are two methods of measuring for internal consistency.

a.

Split halves.
a.

Split halves.

This can be accomplished in two different ways.

Scores on the odd-numbered items can be correlated with the scores on the even-numbered items.

Second, on some but not most tests, the scores on the first half of the test can be cor- related with scores on the second half of the test.
Because the correlations that would result from the above splits would be for only half a test, and because generally the longer a test is, the more internal consistency it has, the correlation coefficient is modified by using the Spearman—Brown formula.

b. Kuder—Richardson formula.

This formula is a mathematical test that results in the average correlation of all possible split half correlations (Cronbach, 1951).

• 4.

Stability over scorers (interscorer reliability).


Certain types of tests, in particular projective tests, leave a good deal to the judgment of the person scoring the test.

Scorer reliability can be determined by having two persons independently score the same set of test papers and then calculating a correlation between their scores, determined by the scores.

5.

Standard error of measurement.

This statistic permits the interpre- tation of individual scores obtained on a test.
Because tests are not perfectly reliable, we know that the score an individual receives on a given test is not necessarily a true measure of his or her trait.

The standard error of measurement tells us how much we can expect an obtained score to differ from the individual's true score.

The reliability of a test may be raised by increasing the number of items of equal quality to the other items.
Carefully designed directions for the administration of the test with no variation from group to group, providing an atmosphere free from distractions and one that minimizes boredom and fatigue, will also improve the reliability of the testing instrument.

Validity In general, a test is valid if it measures what it claims to measure.

Validity can also be thought of as utility.

For the tester's particular purpose, is the Chapter 9 Methods and Tools of Research 219 test useful?
There are several types of validity, and different types of tests and uses of tests need different types of validity.

Content validity refers to the degree to which the test actually measures, or is specifically related to, the traits for which it was designed.

It shows how adequately the test samples the universe of knowledge and skills that a student is expected to master.
Content validity is based upon careful examination of course textbooks, syllabi, objectives, and the judgments of subject matter spe- cialists.

The criterion of content validity is often assessed by a panel of experts in the field who judge its adequacy, but there is no numerical way to express it.

Content validity is particularly important for achieve- ment tests but not very important for aptitude tests.
Construct validity is the degree to which scores on a test can be accounted for by the explanatory constructs of a sound theory.

If one were to study such a construct as dominance, one would hypothesize that people who have this characteristic will perform differently from those who do not.

Theories can be built describing how dominant people behave in a distinctive way.

If this is done, dominant people can be identified by observation of their behavior, rating or classifying them in terms of the theory.
A device could then be designed to have construct validity to the degree that instrument scores are systematically related to the judgments made by observation of behavior identified by the theory as dominant.

Intelligence tests also require adequate construct validity.

Because different tests are based on different theories, each test should be shown to measure what the appropriate theory defines as intelligence.

Construct validity is particularly important for personality and aptitude tests.
Criterion-related validity is a broad term that actually refers to two different types of validity with different time frames.

1.

Predictive validity refers to the usefulness of a test in predicting some future performance, such as the usefulness of the high school Scho- lastic Aptitude Test in predicting college grade-point averages.
If a test is designed to pick out good candidates for appointment as shop foremen, and test scores show a high positive correlation with later actual success on the job, the test has a high degree of predictive validity, whatever factors it actually measures.

It predicts well.

It serves a useful purpose But before a test can be evalUated on the basis of predictive validity, success on the job must be accurately described and mea- sured.
The criteria of the production of the department, the judgment of supervisors, or measures of employee morale might serve as evi- dence.

Because these criteria might not be entirely satisfactory, how- ever, predictive validity is not easy to assess.

It is often difficult to discover whether the faults of prediction lie in the test, in the criteria of success, or both.

220 Part Two Research Methods 2.


Concurrent validity refers to the usefulness of a test in closely relating to other measures, such as present academic grades, teacher ratings, or scores on another test of known validity.

Tests are often validated by comparing their results with a test of known validity.

A well-known scale of personal adjustment, the Minnesota Multiphasic Personality Inventory, required sorting nearly 500 cards into three categories, yes, no, and cannot say.
The equipment was expensive, and it could not be easily administered to large groups at the same time.

A paper-and-pencil form was devised, using the simple process of checking responses to printed items on a form.

This form could be administered to a large group at one time and then scored by machine, all with little expense.

The results were so similar to the more time-consuming expensive card-sorting process, that the latter has been almost completely replaced.
This is the process of establishing concurrent validity; in this case, by com- paring an expensive individual device with an easy-to-administer group instrument.

In like manner, performance tests have been validated against paper-and-pencil tests, and short tests against longer tests.

Through this process, more convenient and more appropriate tests can be devised to accomplish the measurement of behavior more effectively.
Criterion-related validity is expressed as the coefficient of correlation between test scores and some measure of future performance, or between test scores and scores on another test or measure of known validity.

The subject of correlation is explained in detail in Chapter 8.

A test may be reliable even though it is not valid.

However, in order for a test to be valid, it must also be reliable.
That is, a test can consistently measure (reliability) nothing of interest (be invalid), but if a test mea- sures what it is designed to measure (validity), it must do so consistently (reliably).

Economy Tests that can be, given in a shoft period of time are likely to gain the cooperation of the subject and to conserve the time of all those involved in test administration.
The matter of expense of administering a test is often a significant factor if the testing program is being operated on a limited budget.

Ease of administration, scoring, and interpretation is an important factor in selecting a test, particularly when expert personnel or an adequate budget are not available.

Many good tests are easily and effectively ad- ministered, scored, and interpreted by the classroom teacher, who may not be an expert.
Chapter 9 Methods and Tools of Research 221 Interest - Tests that are interesting and enjoyable help to gain the cooperation of the subject.

Those that are dull or seem silly may discourage or antagonize the subject.

Under these unfavorable conditions, the test is not likely to yield useful results.

In selecting a test, it is important to recognize that a good test doçs not necessarily possess all the desirable qualities for all subjects on all levels of performance.
Within a certain range of age, maturity, or ability, a test may be suitable.

For other individuals outside that range, the test may be quite unsatisfactory and a more appropriate one needed.

The selection should be made after careful examination of the stand- ardizing data contained in the test manual and extensive analysisof pub- lished evaluations of the instrument.

Research workers should select the most appropriate standardized tests available.
Detailed reports of their usefulness and limitations are usually supplied in the manual furnished by the publisher.

The considered judgments of outside experts are also available.

Mitchell's (1985) Mental Measurements Yearbook, the best single reference on psychological tests, contains many critical evaluations of published tests, each contributed by an expert in the field of psychological measurement.

Usually, several different evaluations are induded for each test.

Because the reports are.


not duplicated from one volume to another, it is advisable to consult Tests in Print (Mitchell, 1983) or previousYearbooks for additional reports not included in the current volume.

In addition to the reviews and evaluations, the names of test publishers, prices, forms, and appropriate uses are included.

Readers are also urged to consult the listings and reviews of newly published psychological tests in the JoIrnal of Educational Measurement.
When psychological tests are used in educational research, one should remember that standardized test scores are only approximate measures of the traits under consideration.

This limitation is inevitableand may be ascribed to a number of possible factors: 1.

Errors inherent in any psychological test—no test is completely valid or reliable 2.

Errors that result from poor test conditions, inexpert or careless administration or scoring of the test, or faulty tabulationof test scores 3.
Inexpert interpretation of test results 4.

The choice of an inappropriate test for the specific purpose in mind Observation From the earliest history of scientific activity, observation has, been the prevailing method of inquiry.

Observation of natural phenomena1 aided 222 Part Two Research Methods by systematic classification and measurement, led to the development of theories and laws of nature's forces.

Observation continues to characterize all research: experimental, descriptive, and qualitative.
The use of the technique of participant observation in ethnological research was described in Chapter 8.

The importance of observational techniques for single-subject research, and some aspects of the methodology involved in using them, were discussed in Chapter 7.

Most of the discussion here focuses on quantitative research.

The use of observation in qualitative research is covered in Chapter 8.
A reason why observation is most often used in single-subject ex- perimental research is that it is very costly to observe a sufficient sample of behavior for a large number of subjects.

Observation must occur during a number of baseline and intervention sessions in this type of research.

In a study described in Chapter 7, Fantuzzo and Clement (1981) observed the attending behavior of their subjects.
This is an example of the type of observation technique known as time sampling (see Chapter 7 for a description).

Every 60 seconds, the subjects were observed to see if they were attending to their task.

In Chapter 6, a study by Hall, et al.

(1973) was used as an example of an equivalent time-samples design.

Observation was used to collect the data in this study, also.
The observers counted the number of occurrences of aggressive behavior, the technique known as frequency count (described in Chapter- 7).

In experimental research, observation is most frequently the method of choice for behavior modification studies that frequently use single- subject research designs (e.g., Fantuzzo & Clement, 1981).
It is rare to see observation used in group designs (those described in Chapters 5 and 6), unfortunately more because of the cost than because it is less appro- priate than the other measures used in its place.

As a data-gathering device, direct observation may also make an important contribution to descriptive research.

Certain types of informa- tion can best be obtained through direct examination by the researcher.
When the information concerns aspects of material objects or specimens, the process is relatively simple, and may consist of classifying, measuring, or counting.

But when the process involves the study of a human subject in action, it is much more complex.
One may study the characteristics of a school building by observing and recording such aspects as materials of construction, number ofrooms •for various purposes, size of rooms, amount of furniture andequipment, presence or absence of certain facilities, and other relevant aspects.

Ad- equacy could then be determined by comparing these facilities with rea- sonable standards previously determined by expert judgment and research.
• In university athletic departments or professional sports organiza- tions, observation has been used effectively to scout the performance of opposing teams.

For instance, in football, careful observation and recording Chapter 9 Methods and Tools of Research 223 of the skills and procedures of both team and individual players are made, and defenses and offenses are planned to cope with them.

What forma- tions or patterns of attack or defense are employed?

Who carries the ball?


Who does the passing, and where and with what hand does he pass?

Who are the likely receivers, and how do they 'pivot and cut?

During a game a coaching assistant may sit high in the stands, relaying strategic observations by phone to the coach on the bench.

At the same time, every minute of play is being recOrded on filn for careful study by the coaching staff and players.

Who missed his tackl when that play went through for 2O yards?
Who missed his block when pay number two lost 6 yards?

Careful study of these films provides valuable data on weaknesses to be corrected before the following game.

Through the use of binoculars, the phone, the motion picture camera, and the video tape recorder, observations can be carefully made and recorded.
Although this example may seem inappropriate in a discussion of observation as a research technique, improving the performance of a foot- ball team is not altogether different from analyzing learning behavior in a classroom.

The difference is one of degree of complexity.

The objectives of the football team are more concretely identifiable than are the more complex purposes of the classroom.
Yet some of the procedures of ob- servation so effective in football coaching may also be systematically em- ployed in studying classroom performance.

In some schools, teachers make short periodic classroom or playground observations of pupil be- havior, which are filed in the cumulative folder.

These recorded obser- vations, known as anecdotal reports, may provide useful data for research studies.
Laboratory experimentation seeks to describe action or behavior that will take place under carefully arranged and controlled conditions.

But many important aspects of human behavior cannot be observed under the contrived conditions of the laboratory.

Educational research seeks to describe behavior under less rigid controls and more natural conditions.

The behavior of children in a classroom situation cannot be effectively analyzed by observing their behavior in a laboratory.
It is necessary to observe what they actually do in a real classroom.

This does not suggest that observation is haphazard or unplanned.

On the contrary, observation as a research technique must always be systematic, directed by a specific purpose, carefully focused, and thor- oughly recorded.

Like other research procedures, it must be subject to the usual checks for accuracy, validity, and reliability.

The observer must know just what to look for.


He or she must be able to distinguish between the significant and insignificant aspects of the situation.

Of course, objectivity is essential, and careful and accurate meth- ods of measuring and recording must be employed.

Because human behavior is complex, and many important traits and characteristics are difficult or.

impossible to observe directly, they must be carefully defined in precise operational form.
Perhaps a subject's interest 224 / Part Two Research Methods can be operationally defined by the number of times a student volunteers to participate in discussion by raising his or her hand within a time sample period.

Lack of concentration during a study period can be operationally defined by the number of times the student looks around, talks to another student, fiddles with a book, pen, or paper, or engages in other distracting acts within a time sample period.
These examples of operational defini- tions may be unsatisfactory, but they do illustrate the kinds of behavior that can be directly observed.

Behaviors that might mean different things to different observers must also be carefully defined.

Acting-out behavior may mean very dis- ruptive acts such as fighting or, at the other extreme, any behavior for which the child did not first obtain permission, such as sharpening a pencil.
In defining which behaviors meet the meaning of acting out, the researcher would need first to determine the class rules to avoid labeling permissible behavior as "acting out."

Instruments such as the stopwatch, mechanical counter, camera, audiometer, audio and videotape recordings, and other devices make possible observations that are more precise than mere sense observations.
Having a permanent record on videotape also permits the researcher to start and stop the action for more accurate recording of data (especially when more than one subject is to be observed), to collect interobserver reliability data (see next section) without having two or more observers at the observation site, and to reexamine his or her ideas and decide on a new format for coding behaviors.

Where feasible, we recommend the video recording of the behaviors under study.
Systematic observation of human behavior in natural settings (e.g., classrooms) is to some degree an intrusion into the dynamics of the sit- uation.

This intrusion may be reactive, that is, affect the behavior of the person(s) being observed.

These potential confounding effects cannot be ignored.

It is widely believed that individuals do not behave naturally when they know that they are being observed.

The situation may become too artificial, too unnatural, to provide for a valid series of observations.
Concealing the observer has been used to minimize this reactive effect.

Cameras and one-way screens were used by Gesell (1948) to make unobtrusive observations of infant behavior.

One-way glass and concealed microphones and videotape recorders have been used in observing the.. behavior of children in natural group activities so that the observers could see and hear without being seen and heard.
Some authorities believe that the presence of an outside observer in the classroom over a period of time will be taken for granted, viewed as a part of the natural setting, and have little effect on the behavior observed.

Others feel that introducing observers as active participants in the activities of the group will minimize the reactive effect more efficiently.

Should the participant observers make their purposes known to the members of the group observed?
Some feel that concealing the intentions Chapter 9 Methods and Tools of Research 225 of the participant observers raises ethical questions of invasion of privacy and establishes a false, hypocritical, interpersonal relationship with the individuals in the group.

Do the ends of science justify the means of deception?

In a society that increasingly questions the ethics of science, this isstle must be confronted.
Validity and Reliability of Observation For the researcher's observations to achieve a satisfactory degree of content validity, the truly significant incidents of behavior must be identified and sampled.

Supplementing the knowledge and skill of the researcher, the judgment of experts in the field may help in selecting a limited number of observable incidents whose relationship to the qualities of interest is based upon sound, established theories.
Criterion-related and construct validity may also be necessary de- pending on the purpose of the study and inferences made regarding behaviors.

For instance, if certain behaviors were considered to be evi- dence of a person being shy, construct validity is needed to demonstrate a relationship between the behaviors and the underlying construct.

The reactive effect of the intrusion of the observer as a threat to the reliability of the process has been mentioned.
In addition, when research- ers are sole observers, they unconsciously tend to see what they expect,.

to see and to overlook those incidents that do not fit their theory.

Their own values, feelings, and, attitudes, based upon past experience, may distort their observations.

It may be desirable to engage others who are then well-prepared as observers, restricting the researchers' role to that of interpreter of the observations.

Kazdin (1982) recommends that the researcher not be the observer.
To further reduce the possibility of bias, the observers should be kept as ignorant as possible regarding the pur- poses and hypotheses of the study.

This is called a blind.

If the persons being observed also are unaware that they are participants in an experi- ment, thereby reducing the chances of a placebo effect, this becomes a double-blind.

Independent observers should be prepared by participation in 1.

The development of the procedures for observing and recording observations 2.
The try-out or dry-run phase of the procedure 3.

The critique of the results of the try-out phase.

If more than one observer is necessary (as is usually the case), 're- liability among the observers should be demonstrated.

This is done by having each participant observe with at least one other participant for a period of time and compare their recorded observations.
Percentage of 226 Part Two Research Methods agreement among observers should be quite high (usually 90 percent or higher) if the observations are to be considered reliable.

High interobserver reliability is most likely when the behaviors to be observed are well defined and the observers well trained.

Recording Observations If it does not distract or create a barrier between observer and those observed, simultaneous recording of observations is recommended.
This practice minimizes the errors that result from faulty memory.

There are other occasions when recording would more appropriately be done after observation.

The recording of observations should be done as soon as possible, while the details are still fresh in the mind of the observer.
But many authorities agree that objectivity is more likely when the interpre- tation of the meaning of the behavior described is deferred until a later time, for simultaneous recording and interpretation often interfere with objectivity.

Obviously, a video record permits later recording and coding of the observed behaviors.

Systematizing Data Collection To aid in the recording of information gained through observation, a number of devices have been extensively used.
Checklists, rating scales, scorecards, and scaled specimens provide systematic means of summa- rizing or quantifying data collected by observation or examination.

CheckliSt The checklist, the simplest of the devices, is a prepared list of behaviors or items.

The presence or absence of the behavior may be indicated by checking yes or no, or the type or number of items may be indicated by inserting the appropriate word or number.
This simple "laundry-list" type of device systematizes and facilitates the recording of observations and helps to ensure the consideration of the important aspects of the object or act observed.

Readers are familiar with checklists prepared to help buyers purchase a used car, choose a home site, or buy an insurance policy, which indicate characteristics or features that one should bear in mind before making a decision.
Appendix G illustrates a checklist of this type for the evaluation of a research report.

Chapter 9 Methods and Tools of Research 227 Checklists also can be used to count the number of behaviors oc- curring in a given time period.

In Chapter 7, we described a study by Fantuzzo and Clement (1981) in which they observed whether each child was attentive every 60 seconds during a class period.
They most likely used a checklist to mark, and later count, the number of times each child was and was not attending to the task.

Rating Scale The rating scale involves qualitative description of a limited number of aspects of a thing or of traits of a person.
The classifications may be set up in five to seven categories in such terms as: above average 1. superior 2. excellent good 3. always frequently average average occasionally fair below average rarely inferior poor never Another procedure establishes positions in terms of behavioral or situational descriptions.

These statements may be much more specific and may enable the judge to identify more clearly the characteristic to be rated.
Instead of deciding whether the individual's leadership qualities are su- perior or above average, it may be easier to decide between "Always exerts a strong influence on his associates," and "Sometimes is able to move others to action."

One of the problems of constructing a rating scale is conveying to the rater exactly which quality one wishes evaluated.

It is likely that a brief behavioral statement is more objective than an adjective that may have no universal meaning in the abstract.
For this to be considered an effective method in observational research, the traits and categories must be very carefully defined in observable (behaviOral) terms.

Rating scales have several limitations.

In addition to the difficulty of clearly defining the trait or characteristic to be evaluated, the halo effect causes raters to carry qualitative judgment from one aspect to another.
Thus there is a tendency to rate a person who has a pleasing personality high on other traits such as intelligence or professional interest.

This halo effect is likely to appear when the rater is asked to rate many factors, on a number of which he has no evidence for judgment.

This suggests the advisability of keeping at a minimum the number of characteristics to be rated.

Another limitation of rating is the raters' tendency to be too generous.
A number of studies have verified the tendency to rate 60 to 80 percent of an unselected group above average in all traits.

Rating scales should 228 Part Two Research Methods carry the suggestion that raters omit the rating of characteristics that they have had no opportunity to observe.

Scorecard The scorecard, similar in some respects to both the checklist and the rating scale, usually provides for the appraisal of a relatively large number of aspects.
In addition, the presence of each characteristic or aspect, or the rating assigned to each, has a predetermined point value.

Thus the score- card rating may yield a total weighted score that can be used in the evaluation of the object observed.

Scorecards are frequently used in eval- uating communities, building sites, schools, or.

textbooks.

Accrediting agencies sometimes use the scorecard in arriving at an overall evaluation of a school.
Scorecards have been designed to help estimate the socioeconomic status of a family.

Such aspects as type of neighborhood, home ownership, number of rooms, ownership of a piano, number of books in the library, number and type of periodicals subscribed to, presence of a telephone, occupations of parents, and organizational membership of the adults are all considereçl significant and have appropriate point values assigned.

The limitations of the scorecard are similar to those of the rating scale.
In addition to the difficulty of choosing, identifying, and quantifying the significant aspects of the factor to be observed, there is the suspicion that the whole of a thing may be greater than the sum of its parts.

Colleges and universities are frequently evaluated in terms of such elements as size of endowment, proportion of faculty members holding the earned doctoral degree, pupil—teacher ratio, and number of volumes in the library.
Although these aspects are important, the effectiveness of an institution may not be accurately appraised by their summation, for certain important intangibles do not lend themselves to scorecard ratings.

The Scaled Specimen The scale specimen, although not frequently encountered in behavioral research, provides a method for evaluating certain observed levels of performance or measures of a quality in question.

Testing a solution for acidity in a chemistry laboratory invoives a pH test.
A drop of color indicator is introduced into a sample of the solution.

The resulting color of the solution is matched with the color of one of a set of display vials, indicating the percentage of acidity in the solution.

One of the early scaled specimens developed in the field of education was the handwriting scale developed by Thorndike.

From a large sample of handwriting exhibits taken at different ages and grade levels, norms Chapter 9 Methods and Tools of Research 229 were established.
The handwriting to be evaluated was then matched with the exhibit sample, yielding a measure of handwriting quality.

The Goodenough—Harris Drawing Test (Harris, 1963) provides a 71- point scale with examples for comparing various details of a child's draw- ing of a man, a woman, or a self-portrait.

Each point is scored + or 0, indicating the presence or absence of a part of body detail in the figurt drawn.

The total of + scores is equated with separate age norms, estab- lished for boys and girls.
The scale is based on the assumption that as individuals mature intellectually, they perceive greater detail in the human figure that they reveal in their drawings.

Variations of the test include Draw a Man, Draw a Woman, and Draw Yourself.

Studies have reported correlations as high as + .60 to +.72 with the Stanford—Binet Inteffigence Scale.

CharacterIsticS of Good Observation Observation, as a research data-gathering process, demands rigorous ad- herence to the spirit of sdentific inquiry.
The following standards should characterize observers and their observations: Observation is carefully planned, systematic, and perceptive.

Ob- servers know what they are looking for and what is irrelevant in a situation.

They are not distracted by the dramatic or the spectacular.

Observers are aware of the wholeness of what is pbserved.

Although they are alert to significant details, they know that the whole isoften greater than the sum of its parts.

Observers are objective.


They recognize their likely biases, and they strive to eliminate their influence upon what they see and report.

Observers separate the facts from the interpretation of the facts.

They observe the facts and make their interpretation at a later time.

Observations are checked and verified, whenever possible by repe- tition, or by comparison with those of other competent observers.

Observations are carefully and expertly recorded.


Observers use ap- propriate instruments to systematize, quantify, and preserve the re- suits of their observations.

Observations are collected in such a way as to make sure that they are valid and reliable.

Inquiry Forms: The Questionnaire The general category of inquiry forms includes data-gathering instruments through which respondents answer questions or respond to statements 230 Part Two Research Methods in writing.
A questionnaire is used when factual information is desired.

When opinions rather than facts are desired, an opinionnaire or attitude scale is used.

Questionnaires administered personally to groups of individuals have a number of advantages.

The person administering the instrument has an opportunity to establish rapport, explain the purpose of the study, and explain the meaning of items that may not be clear.
That availability of a number of respondents in one place makes possible an economy of time and expense and provides a high proportion of usable responses.

It is likely that a principal would get completely usable responses from teachers in the building, or a teacher from students in the classroom.

However, individuals who have the desired information cannot always be contacted personally without the expenditure of a great deal of time and money in travel.
It is in such situations that the mailed questionnaire may be useful.

The mailed questionnaire is one of the most used and probably most criticized data-gathering device.

It has been referred to as the lazy person's way of gaining information, although the careful preparation of a good questionnaire takes a great deal of time, ingenuity, and hard work.

There is little doubt that the poorly constructed questionnaires that flood the mails have created a certain amount of contempt.
This is particularly true when the accompanying letter pleads that the sender needs the infor- mation to complete the requirements for a graduate course, thesis, or dissertation.

The recipient's reaction may be, "Why should I ga to all this trouble to help this person get a degree?"

Filling out lengthy questionnaires takes a great deal of time and effOrt, a favor that few senders have any right to expect of strangers.
The unfavorable reaction is intensified when the questionnaire is long, the subject trivial, the items vaguely worded, and the form poorly organized.

The poor quality of so many mailed questionnaires helps to explain why so small a proportion is returned.

As a result of low response rates, often less than 40 percent, the data obtained are often of limited validity.

The information in the unreturned questionnaires might have changed the results of the investigation materially.
The very fact of no response might imply certain types of reactions, reactions that can never be included in the summary.

of data.

Unless one is dealing with a group of respondents who have a genuine interest in the problem under investigation, know the sender, or have some common bond of loyalty to a sponsoring institution or orga- nization, the rate of returns is frequently disappointing and provides a flimsy basis for generalization.
Although the foregoing discussion may seem to discredit the ques- tionnaire as a respectable research technique, we have tried to consider the abuse or misuse of the device.

Actually the questionnaire has unique advantages, and properly constructed and administered, it may serve as a most appropriate and useful data-gathering device in a research project.
Chapter 9 Methods and Tools of Research 231 The Closed Form Questionnaires that call for short, check-mark responses are known as the restricted or closed-form type.

Here you mark a yes or no, write a short response, or check an item from a list of suggested responses.

The fol- lowing example illustrates the closed-form item: Why did you choose to do your graduate work at this university?
Kindly indicate three reasons in order of importance, using the number I for the most important, 2 for the second most important, and 3 for the third most important.

Rank (a) Convenience of transportation (b) Advice of a friend (c) Reputation of institution (d) Expense factor (e) Scholarship aid (f) Other __________ (kindly indicate) Even when using the closed form, it is well to provide for unanticipated response.
Providing an "other" category permits respondents to indicate what might be their most important reason, one that the questionnaire builder had not anticipated.

Note the instruction to rank choices in order of importance, which enables the tabulator to properly classify all responses.

For certain types of information the closed-form questionnaire is entirely satisfactory.
It is easy to fill out, takes little time, keeps the re- spondent on the subject, is relatively objective, and is fairly easy to tabulate and analyze.

The Open Form The open-form or unrestricted questionnaire calls for a free response in the respondent's own words.

The following open-form item seeks the same type of information as did the closed-form item: Why did you choose to do your graduate work at this university?

Note that no clues are given.


The open form probably provides for greater depth of response.

The respondents reveal their frame of reference and possibly the reasons for their responses.

But because it requires greater effort on the part of the respondents, returns are often meager.

Atso, the 232 Part Two Research Methods open-form item can sometimes be difficult to interpret, tabulate, and sum- marize in the research report.

Many questionnaires include both open- and closed-type items.
Each type has its merits and limitations, and the questionnaire builder must decide which type is more likely to supply the information wanted.

Improving Questionnaire Items Inexperienced questionnaire makers are likely to be naive about the clarity of their questions.

One author of this book recalls a brilliant graduate student who submitted a questionnaire for his approval.
She was some- what irritated by his subsequent questions and suggestions, remarking that anyone with any degree of intelligence should know what she meant.

At the advisor's suggestion, she duplicated some copies and personally administered the questionnaire to a graduate class in research.

She was swamped with questions of interpretation, many of which she could not answer clearly.

There was considerable evidence of con- fusion about what she wanted to know.
After she had collected the com- pleted copies and had tried to tabulate the responses, she began to see the questionnaire's faults.

Even her directions and explanation in class had failed to darify the ambiguous intent of her questionnaire.

Her second version was much improved.

Many beginning researchers are not really sure what they want to know.
They use a shotgun approach, attempting to cover their field broadly in the hope that some of the responses will provide the answers for which they are groping.

Unless researchers know exactly what they want, how- ever, they are not likely to ask the right questions or to phrase them properly.

In addition to the problem of knowing what one wants, there is the difficulty of wording the questionnaire clearly.

The limitations of words are particular hazards in the questionnaire.
The same words mean differ- ent things to different people.

After all, even questionnaire makers have their own interpretation, and the respondents may have many different interpretations.

In the interview, as in conversation, we are able to clear up misunderstandings by restating our question, by inflection of the voice, by suggestions, and by a number of other devices.

But the written question stands by itself, often ambiguous and misunderstood.
A simple example illustrates the influence of voice inflection alone.

Consider the following question.

Read it over, each time emphasizing the underlined word, noting how the change in inflection alters the meaning.

Were you there last night?

Were you there last night?

•Were you there last night?

Chapter 9 Methods and Tools of Reseatth 233 Were you there last night?

Were you there last night?

Questionnaire makers must depend on written language alone.
Ob- viously they cannot be too careful in phrasing questions to insure their clarity of purpose.

Although there are nu certain ways of producing fool- proof questions, certain principles can be employed to make questionnaire items more precise.

A few are suggested here with the hope that studçnts constructing questionnaires and opinionnaires will become critical of their first efforts and strive to make each item as clear as possible.

Define or qualify terms that could easily be misinterpreted.
What is the value of your house?

The meaning of the term value is not clear.

It could imply several different meanings: the assessed value for tax purposes, what it would sell for on the present market, what you would be willing to sell it for, what it would cost to replace, or what you paid for it.

These values may differ considerably.

It is essential to frame questions spedfically, such as, "What is the present market value of your house?"

As simple a term as age is often misunderstood.


When is an individual twenty-one?

Most people would say that a person is twenty-one from the day of the twenty-first birthday until the day of the twenty-second birth- day.

But an insurance company considers a person twenty-one from age twenty and six months until age twenty-one and six months.

Perhaps this question could be clarified by asking age to nearest birthday or date efbirth.
Hundreds of words are ambiguous because of their many interpre- tations.

One has only to think of such words and phrases as curriculum, democracy, progressive education, coo peratipn, and integration—and even such simple words as how much and now.

To the question, "What work are you doing now?"

the respondent might be tempted to answer, "Filling out your foolish questionnaire."

Be careful in using descriptive adjectives and adverbs that have no agreed- upon meaning.
l'his fault is frequently found in rating scales as well as in questionnaires.

Frequently, occasionally, and rarely do not have the same meanings to different persons (Hakel, 1968).

One respondent's occasionally may be another's rarely.

Perhaps a stated frequency—times per week or times per month—would make this classification more precise.

Beware of double negatives.

Underline negatives for clarity.

Are you opposed to not requiring students to take showers after gym class?
234 Part Two Research Methods Federal aid should not be granted to those states in which education is not equal regardless of race, creed, or color.

Be careful of inadequate alternatives.

Married?

Yes _____ No _____ Does this question refer to present or former marital status?

How would the person answer who is widowed, separated, or divorced?

How late at night do you permit your children to watch television?

There may be no established family policy.


If there is a policy, it may differ for children of different ages.

It may be different for school nights or for Friday and Saturday nights, when watching a late movie may be permitted.

Avoid the double-barreled question.

Do you believe that gifted students should be placed in separate groups for instructional purposes and assigned to special schools?
One might agree on the advisability of separate groups for instruc- tional purposes but be very much opposed to the assignment of gifted students to special schools.

Two separate questions are needed.

Underline a word if you wish to indicate special emphasis.

A parent should not be told his child's IQ score.

Should all schools offer a modem foreign language?

When asking for ratings or comparisons, a point of reference is necessary.

How would you rate this student teacher's classroom teaching?
Superior Below average Average With whom is the student teacher to be compared—an experienced teacher, other student teachers, former student teachers—or should the criterion be what a student teacher is expected to be able to do?

Avoid unwanted assumptions.

Are you satisfied with the salary raise that you received last year?

Chapter 9 Methods and Tools of Research 235 A no answer might mean either I did not get a raise or that I did get a raise but am not satisfied.
Do you feel that you benefited from the spankings that you received as a child?

A no response could mean either that the spankings did not help me, or that my parents did not administer corporal punishment.

These unwarranted assumptiors are nearly as bad as the classic, "Have you stopped beating your wife?"

Phrase questions so that they are appropriate for all respondents.

What is your monthly teaching salary?


Some teachers are paid on a nine-month basis, some on ten, some on eleven, and some on twelve.

Three questions would be needed.

Your salary per month?

_________ Number of months in school term?

_________ Number of salary payments per year?

Design questions that will give a complete response.

Do you read the Indianapolis Star?

Yes ______ No ______ A yes or no answer would not reveal much information about the reading habits of the respondent.
The question might be followed with an additional item, as in Figure 9—1.

Provide for the systematic quantification of responses.

The type of question that asks respondents to check a number of items from a list is difficult to summarize, especially if not all respondents check the same number.

One solution is to ask respondents to rank, in order of preference, a specific number of responses.

What are your favorite television programs?


Rank in order of pref- erence your first, second, third, fourth, and fifth choices.

The items can then be tabulated by inverse weightings.

1st choice 2nd choice 3rd choice 5 points 4 points 3 points 236 Part Two Research Methods 2 points 1 point 4th choice 5th choice The relative popularity of the programs could be described for a group in terms of.

total weighted scores, the most popular having the largest total.
Consider the possibility of classifying the responses yourself, rather than having the respondent choose categories.

If students were asked to classifytheir father's occupation in one of the following categories, the results might be quite unsatisfactory.

Unskilled labor Skilled labor aencal work Managerial work — Profession Proprietorship It is likely that by asking the children one or two short questions about their father's work, it could be classified more accurately.
If your answer is Yes, kindly check how often and what sections of the Star you read.

Section Always Usually Seldom Never National and inter- national news State and local news Editorial Sports Comic Society Financial Advertising Want ad Syndicated features Special features Other (specify) FIGURE 9—1 Sample Questionnaire Item.

Chapter 9 Methods and Tools of Research 237 1.

At what place does your father work?

2.

What kind of work does he do?


Very often, a researcher wants to gather information (facts) and attitudes (opinions).

This allows later analyses that can determine if atti- tudes are related to personal characteristics such as age, sex, or race.

Figure 9—2 is an example of just such a combination.

This questionnaire/opinionriaire collects information about the individual and then asks for the opinion of the person regarding factors that contribute to teacher morale.
Characteristics of a Good Questionnaire 1.

It deals with a significant topic, one the respondent wifi recognize as important enough to warrant spending his or her time on.

The significance should be clearly and carefully stated on the question- naire, or in the letter that accompanies it.

2.

It seeks only that information which cannot be obtained from other sources such as school reports or census data.

3.

It is as short as possible, and only long enough to get the essential data.
Long questionnaires frequently find their way into the waste- basket.

Keep the writing required of the respondent to a minimum and make the response system clear and easy to complete.

4.

It is attractive in appearance, neatly arranged, and clearly duplicated or printed.

5.

Directions for a good questionnaire are clear and complete.

Important terms are defined.

Each question deals with a single idea and is worded as simply and clearly as possible.
Avoid asking two questions FIGURE 9—2 Teacher Morale Questionnaire-Opinionnaire.

married 1.

Male ______ Female ______ 2.

Age 3.

Marital status: single 4.

Number of dependent children _____; their ages 5.

Number of other dependents 6.

Highest degree held _____ 7.

Years of teaching experience 8.

Years of teaching at present school _____ 9.

Teaching level; primary divorced/separated Jr. intermediate H.S.

_____ Sr. H.S.

_____; If secondary, your major teaching area upper grades 10.
Enrollment of your school _____ 11.

Your average class size ______ 12.

Population of your community or school district _____ 13.

Your principal is: male _____ female _____ Continued 238 Part Two Research Methods FIGURE 9—2 (Continued) 14.

How does your salary schedule com- pare with those of similar school districts?

15.

How would you rate your principal on these traits?

16.

How would you rate the consulting or advisory services that you receive?

17.
17.

Provision made for teacher free time competence friendliness helpfulness ability to inspire encourage creativity availability 18.

How would you rate your faculty lounge?

19.

How would you rate your faculty professional library?

.20.

How would you evaluate the adequacy of teaching materials and supplies?

21.

How would you evaluate the assign- ment of your nonteaching duties?
(leave blank if item does not apply) ( I .1 supervision of: s I relaxation preparation lunch conferences books periodicals references textbooks references AV aids supplies reports meetings halls lunchroom playground study hall extra-class organizations 22.

How would you rate the compatibility of your faculty?

23.

How would you rate the parent support of your school?

24.

How would you rate your morale as a teacher?


Chapter 9 Methods and Tools of Research 239 FIGURE 9—2 (Continued) i. more effective faculty meetings 25.

Kindly rank in order of importance to you at least jive factors that you would consider most important in increasing your morale or satisfaction with your working conditions: Rank 1. most important, 2 next in importance, etc.
— a. higher salary — b. smaller class size — c. more free time — d. more adequate faculty lounge — e. more compatible faculty — f. more adequave teaching materials — g. more effective principal — h. better consulting services — j. assistance of a teacher aide — I. fewer reports to make out ........m. fewer nonteaching duties — n. better provision for atypical students — o. more participation in policy making — p. fewer committee meetings — q. teaching in a higher socioeconomic area — r. teaching in a lower socioeconomic area — s. other (kindly specif') On the back of this sheet kindly add any comments that you believe would more adequately express your feelings of satisfaction or dissatisfaction with teaching.
k. more attractive classroom/building in one.

The categories provide an opportunity for easy, accurate, and unambiguous responses.

6.

The questions are objective, with no leading suggestions as to the responses desired.

Leading questions are just as inappropriate on a questionnaire as they are in a court of law.

7.

Questions are presented in good psychological order, proceeding from general to more specific responses.
This order helps respon- dents to organize their own thinking so that their answers are logical and objective.

It may be well to present questions that create a favorable attitude before proceeding to those that may be a bit del- icate or intimate.

If possible, annoying or embarrassing questions should be avoided.

When delicate questions are necessary, the ques- tionnaire should be anonymous.

8.

It is easy to tabulate and interpret.


It is advisable to preconstruct a tabulation sheet, anticipating how the data will be tabulated and interpreted, before the final form of the questionnaire is decided upon.

This working backward from a visualization of the final anal- ysis of data is an important step for avoiding ambiguity in question- naire form.
If computer tabulation is to be used, i is important to 240 Part Two Research Methods designate code numbers for all posible responses to permit easy transference to a computer program's format.

Preparing and Administering the Questionnaire Get all the help you can in planning and constructing your questionnaire.

Study other questionnaires, and submit your items for criticism to other members of your class or your faculty, especially those who have had experience in questionnaire construction.
In designing an inquiry form (questionnaire or opinionnaire), it is advisable to use a separate card or slip for each item.

As the instrument is being developed, items can be refined, revised, or replaced by better items without recopying the entire instrument.

This procedure alsopro- vides flexibility in arranging items in the most appropriate psychological order before the instrument is put into its final form.

Try out your questionnaire on a few friends and acquaintances.
When you do this personally, you may find that a number of your items are ambiguous.

What may seem perfectly clear to you may be confusing to a person who does not have the frame of reference that you have gained from living with and thinking about an idea over a long period.

It is also a good idea to "pilot test" the instrument with a small group of persons similar to those who will be used in the study.

These dry runs will be well worth the time and effort.
They may reveal defects that can be corrected before the final form is printed and committed to the mails.

Once the instrument has been sent out, it is too late to remedy its defects.

Choose respondents carefully.

It is important that questionnaires be sent only to those who possess the desired information and are likely to be sufficiently interested to respond conscientiously and objectively.
A preliminary card, asking whether the individual would be willing to par- ticipate in the proposed study; is recommended by some research au- thorities.

This is not only a courteous approach but a practical way of discovering those who will cooperate in furnishing the desired information.
In a study on questionnaire returns, See (1957) discovered that a better return was obtained when the original request was sent to the administrative head of an organization rather than directly to the person who had the desired information.

It is likely that when a superior officer gives a staff member a questionnaire to fill out, there is an implied feeling of obligation.

Getting PermissiOn.
Getting PermissiOn.

If the questionnaire is to be used in a public school, it is essential that approval of the project be secured from the principal, Chapter 9 Methods and Tools of Research 241 who may then wish to secure approval from the superintendent of schools.

Schools are understandably sensitive to public relations.

One can imagine the unfavorable publicity that might result from certain types of studies made by individuals not officially designated to conduct the research.
School officials may also want to prevent the exploitation of teachers and pupils by amateur researchers, whose activities require an excessive amount of time and effort in activities not related to the purposes of the school.

Parental permission may also need to be secured.

Students should be informed that participation is voluntary.

Particularly if sensitive ques- tions (e.g., about drug use) are to be asked, parental and student consent is essential.
If the desired information is delicate or intimate in nature, consider the possibility of providing for anonymous responses.

The anonymous instrument is most likely to produce objective and honest responses.

There are occasions, however, for purposes of classification or for a possible follow-up meeting, when it might be necessary to identify the respon- dents.
If identification is needed, it is essential to convince the respondents that their responses will be held in strict confidence and that their answers will in no way jeopardize the status and security of their position.

Try to get the aid of sponsorship.

Recipients are more likely to answer if a person, organization, or institution of prestige has endorsed the proj- ect.

Of course, it is unethical to claim sponsorship unless it has been expressly given.

The Cover Letter.
The Cover Letter.

Be sure to include a courteous, carefully constructed cover letter to explain the purpose of the study.

The letter should promise some sort of inducement to the respondent for compliance with the re- quest.

Commercial agencies furnish rewards in goods or money.

In ed- ucational circles, a summary of questionnaire results is considered an appropriate reward, a promise that should be scrupulously honored after the study has been completed.
The cover letter shøuld assure the respondent that all information will be held in strict confidence or that the questionnaire is anonymous.

And the matter of sponsorship might well be mentioned.

Of course, a stamped, addressed return envelope should be included.

To omit this would virtually guarantee that many of the questionnaires would go into the wastebasket.
Some researchers suggest that two copies of the ques- tionnaire be sent, one to be returned when completed and the other for the respondent's own file.

Follow-Up Procedures.

Recipients are often slow to return completed questionnaires.

To increase the number of returns, a vigorous follow-up procedure may be necessary.

A courteous postcard reminding the recipient that the completed questionnaire has not been received may bring in 242 Part Two Research Methods some additional responses.
This reminder will be effective with those who have just put off filling out the document or have forgotten to mail it.

A further step in the follow-up process may involve a personal letter of reminder.

In extreme cases a telegram, phone call, or personal.

visit may bring additional responses.

In some cases it may be appropriate to send another copy of the questionnaire with the follow-up letter.

However, the researcher must know who has already responded so as not to receive potential duplicates.
It is difficult to estimate, in the abstract, what percentage of ques- tionnaire responses is to be considered adequate.

The importance of the project, the quality of the questionnaire, the care used in selecting recip- ients, the time of year, and many other factors may be significant .in determining the proportion of responses.

In general, the smaller the per- centage of responses, the smaller the degree of confidence one may place in the data collected.
Of course, objectivity of reporting requires that the proportion of responses received should always be included in the re- search report.

Babbie (1973) suggests that a response rate of 50 percent is adequate, 60 percent good, and 70 percent very good.

A Sample Questionnaire Figure 9—3 provides a questionnaire that has been used with teachers in Texas for a number of years by David Henderson of Sam Houston State University.

The data from these questionnaires are reported in Figure 9—4.
As can be seen, the questionnaire is short (one page) and easy to understand, complete, and tabulate.

Validity and Reliability of Questionnaires All too rarely do questionnaire designers deal consciously with the degree of validity or reliability of their instrument.

Perhaps this is one reason why so many questionnaires are lacking in these qualities.

It must be recognized, however, that questionnaires, unlike psychological tests and inventories, have a very limited purpose.
They are often one-time data- gathering devices with a very short life, administered to a limited popu- lation.

There are ways, however, to imprOve both validity and reliability of questionnaires.

Basic to the validity of a questionnaire is asking the right questions, phrased in the least ambiguous way.

In other words, do the items sample a significant aspect of the purpose of the investigation?

The meaning of all terms must be clearly defined so that they have the same meaning to all respondents.
Researchers need all the help they can get; suggestions from colleagues and experts in the field of inquiry Chapter 9 Methods and Tools of Research 243 DIRECTIONS: Please circle or answer jj items that apply to you.

Add comments If you wish.

1.

What is your age?

2.

Sex?

3.

Marital Status?

4.

If married, does your spouse work?

5.

What is your highest degree?

6.

Are you the major bread winner in your household?

7.

Should Texas retain the Career Ladder for eachers?

8.
8.

Are you seriously considering leaving the teaching profession' If yes, why are you considering leaving?

9.

In what type of district do you teach?

10.

What grade level do you primarily teach 11.

How many years have you taught in the public schools' 12.

What is your current teaching salary per year?

13.

Are you pleased with the current NO PASS-NO PLAY rule' 14.

What is your Career Ladder status for 1987-88?

15.

Quality of teaching at my school compared to two years ago' 16.
Do you have an extra job during the summer?

Years_ Male Married Yes BA Yes Yes Yes Female Single No MEd No No No Other NA PhD Equal _______________________ Urban Suburban Rural K-5 Years $ Yes Better Yes 6-8 9-12 No II Worse No None Same 17.

How much do you earn during the summer?

$______________ None 18.

Do you have an )fj (mooniighting) job during the reouiar school 5E to supplement your teaching salary?

Yes No If your answer to Question #18 is yes, please answer tile following questions.

19.
19.

Do you feel that the quality of your teaching would improve if you did j have a second job during the reguiar school year?

Yes No 20.

How much money do you earn during the regular school year?

$________________________ 21.

How many hours per week during the regular school year do you spend working at the moonlightina job?

Hours_________________ 22.

Would you quit the second job if your teaching salary would enable you to give up moonlighting during the school year?

Yes No 23.
Yes No 23.

How large a raise In your teaching salary would you have to get to enable you to give up moonlighting during the regular school year?

$_______________________ 24.

What Is your g job during the school year?

(Please give a job title such as bookkeeper, sales clerk rancher, etc.)

_____________________________ FIGURE 9—3 Teachers and Moonlighting Questionnaire.

(Used with the permission of the author, David L.

Henderson.)
Henderson.)

may reveal ambiguities that can be removed or items that do not contribute to a questionnaire's purpose.

The panel of experts may rate the instrument in terms of how effectively it samples significant aspects of its purpose, providing estimates of content validity.
244 Part Two Research Methods Table 1 SURVEY OF TEXAS PUBLIC SCHOOL TEACHERS Characteristics 1980 1982 1984 1986 1988 $14,113 38.6 19.9% 80.1% 76.6% 70.1% 63.9% 35.7% 0.3% 39.9% 40.5% 32.6% 26.8% 50.9% 19.9% 29.2% 11.8 38.4% 30.2% $1252 22.0% 13.6 $2799 64.1% 75.0% * Average Salary Average Age Sex: Male Femele Married Spouse Works Degree: BA MEd PhD Major Breadwinner Districts: Urban Sthirban R.xal Grade Taught: K-5 9-12 Years Experience Consider Leaving Extra Job in Summer Summer Ean*igs Moonlight MoonlightHoursWeekly Moon Earnings Moonlight Detrimental Quit Moonlighting No Pass—No Play Retain Career Ladder Quality of Teaching: Better Worse (Conparedto T Years ) Same (Current) Career Ladder: Level I Level Il Level III None Negotiation Rights: Yes No $17,351 39.2 19.7% 80.3% 74.9% 69.6% 62.5% 37.2% 0.3% 39.8% 43.3% 36.7% 20.1% 49.8% 19.7% 30.4% 12.1 373% 36.4% $2076 28.8% 11.9 $3189 68.5% 75.0% * $20,259 41.2 14.8% 85.2% 75.3% 66.3% 55.4% 44.0% 0.6% 39.5% 42.8% 38.9% 18.4% 45.8% 24.4% 29.8% 13.7 39.8% 33.7% $2205 26.2% 14.4 $3615 70.1% 81.6% * $26,161 43.0 15.9% 84.1% 72.8% 67.3% 53.3% 46.7% 0.0% 43.1% 38.4% 40.1% 21.5% 52.4% 22.8% 24.8% 15.4 37.7% 29.0% $2480 20.1% 10.4 $4627 65.7% 77.5% 70.4% 57.7% 37.2% 13.7% 48.8% 14.7% 63.2% 9.8% 12.4% $24,601 41.3 17.5% 82.5% 77.4% 71.8% 50.3% 48.7% 1.0% 38.5% 46.2% 36.5% 17.3% 51.5% 22.9% 25.6% 14.3 41.7% 31.2% $1891 22.6% 12.8 $3522 50.1% 60.9% 54.5% 14.3% 68.2% 23.1% FIGURE 9—4 Data from Teachers and Moonlighting Questionnaire.
(Used with the permission of the author, David L.

Henderson.)

It is possible to estimate the predictive validity of some types of questionnaires by follow-up observations of respondent behavior at the present time or at some time in the future.

In some situations, overt behavior can be observed without invading the privacy of respondents.
A comparison of questionnaire responses with voting data on a campus Chapter 9 Methods and Tools of Research 245 or.. community election may provide a basis for estimating predictive validity.

Reliability of questionnaires may be inferred by a second adminis- tration of the instrument, comparing the responses with those of the first.

Reliability may also be estimated by comparing responses of an alternate form with the original form.
Inquiry Forms: The Opinionnaire An information form that attempts to measure the attitude or belief of an individual is known as an opinionnaire, or attitude scale.

Because the terms opinion and attitude are not synonymous, clarification is necessary.

How people feel, or what they believe, is their attitude.

But it is difficult, if not impossible, to describe and measure attitude.

Researchers must depend upon what people say are their beliefs and feelings.

This is the area of opinion.


Through the use of questions, or by getting people's expressed reaction to statements, a sample of their opinions is obtained.

From this statement of opinion, one may infer or estimate their attitude— what they really believe.

Inferring attitude from expressed opinion has many limitations.

Peo- ple may conceal their attitudes and express socially acceptable opinions.
They may not really know how they feel about a social issue, never having given the idea serious consideration.

People may be unaware of their attitude about a situation in the abstract; until confronted with a real situation, they may be unable to predict their reaction or behavior.

Even behavior itself is not always a true indication of attitude.

When politicians kiss babies, their behavior may.

not be a true expression of affection toward infants.
Social custom or the desire for social approval makes many overt expressions of behavior mere formalities, quite unre- lated to people's inward feelings.

Even though there is no sure method of describing and measuring attitude, the description and measurement of opinion may, in many instances, be closely related to people's real feelings or attitudes.
With these limitations in mind, psychologists and sociologists have explored an interesting area of research, basing their data upon people's expressed opinions.

Several methods have been employed: 1.

Asking people directly how they feel about a subject.

This technique may employ a schedule or questionnaire of the open or closed form.

It may employ the interview process, in which the respondents ex- press their opinions orally.

2.

Asking people to check in a list the statements with which they agree.
246 Part Two Research Methods 3.

Asking people to indicate their degree of agreement or disagreement with a series of statements about a controversial subject.

- 4.

Inferring their attitudes from reactions to projective devices, through which they may reveal attitudes unconsciously.

(A projective device is a data-gathering instrument that conceals its purpose so that the subjects cannot guess how they should respond to appear in their best light.

Thus their real characteristics are revealed.)


Three procedures for eliciting opinions and attitudes have been used extensively in opinion research, and they warrant a brief description.

Thurstone Technique The first method of attitude assessment is known as the Thurstone Tech- nique of Scaled Values (Thurstone & Chave, 1929).

A number of state- ments, usually twenty or more, are gathered that express various points of view toward a group, institution, idea, or practice.
They are then submitted to a panel of judges, each of whom arranges them in eleven groups ranging from one extreme to another in position.

This sorting by each judge yields a composite position for each of the items.

When there has been marked disagreement among the judges in assigning a position to an item, that item is discarded.

For items that are retained, each is given its median scale value (see Chapter 10) between one and eleven as established by the panel.
The list of statements is then given to the subjects, who are asked to check the statements with which they agree.

The median value of the statements that they check establishes their score, or quantifies their opinion.

- Likert Method The second method—the Likert Method of Summated Ratings—can be performed without a panel of judges and has yielded scores very similar to those obtained by the Thurstone method.
The coefficient of correlation (see Chapter 10) between the two scales was reported as high as + .92 in one study (Edwards & Kenney, 1946).

Since the Likert-type scale takes much less time to construct, it offers an interesting possibility for the student of opinion research.

The first step in constructing a Likert-type scale is to collect a number of statements about a subject.

The correctness of the statements is not important, as long as they express opinions held by a substantial number of people.
It is important that they express definite favorableness or un- favorableness to a particular point of view and that the number of favorable and unfavorable statements is approximately equal.

Chapter 9 Methods and Tools of Research 247 After the statements have been gathered, a trial test should be ad- ministered to a number of subjects.

Only those items that correlate with the total test should be retained.
This testing for internal consistency will help to eliminate statements that are ambiguous or that are not of the same type as the rest of the scale.

The attitude or opinion scale may be analyzed in several ways The simplest way to describe opinion is to indicate percentage responses for each individual statement.

For this type of analysis by item, three re- sponses—agree, undecided, and disagree—are preferable to the usual five.
If a Likert-type scale is used, it may be possible to report percentage responses by combining the two outside categories: "strongly agree" and "agree"; "disagree" and "strongly disagree."

Strongly agree Undecided Agree Disagree Strongly disagree For example, 70 percent of the male respondents agree with the statement, "Merit rating will tend to encourage conformity and discourage initiative."

The Likert scaling technique assigns a scale value to each of the five responses.
Thus the instrument yields a total score for each respondent, and a discussion of each individual item, although possible, is not nec- essary.
Starting with a particular point of view, all statements favoring the above position are scored: Strongly agree Agree Undecided Disagree Strongly disagree Scale Value 5 4 3 2 1 For statements opposing this point of view, the items are scored in the opposite order: Strongly agree Agree Undecided Disagree Strongly disagree Scale Value The opinionnaire illustrated in Figure 9—5 attempts to measure Chris- tian religious orthodoxy or conservatism.
It is apparent that this type of 248 Part Two Research Methods The following statements represent opinions, and your agreement or disagree- ment will be determined on the basis of your particular beliefs.

Kindly check your position on the scale as the statement first impresses you.

Indicate what you believe, rather than what you think you should believe.

III' I I I I I I I I I 1.

Heaven does not exist as an actual place or location.

— 2.

God sometimes sets aside natural law, performing mira- des.

3.
3.

Jesus was born ofa virgin, without a human father.

4.

Hell does .

!

!g.f exist as an actual place or location.

_______ 5.

The inspiration that resulted in the writing of the Bible was no different from that of any other great religious literature.

__________________________________________ 6.

There is a final day of judgment for all who have lived onearth.

7.

The devil exists as an actual person.

8.


Prayer directly affects the lives of persons, whether or not they know that such prayer has been offered.

_________ 9.

There is another life after the end of organic life on earth.

10.

When on earth, Jesus possessed and used the power to restore the dead to life.

____________________________ 11.

God isa cosmic force, rather than an actual person.

— 12.

Prayer does not have the power to change such conditions as a drought.

13.
13.

The creation of the world did not literally occur in the way described in the Old Testament.

________________ 14.

After Jesus was dead and buried, he actually rose from the dead, leaving an empty tomb.

___________________ 15.

Everything in the Bible should be interpreted as literally true.

FIGURE 9—5 A Likert-Type Opinionnaire.

instrument could be used to measure opinion in many controversial areas: racial integration, merit rating of teachers, universal military training, and many others.
The test scores obtained on all the items would then measure the respondent's favorableness toward the given point of view.

Figure 9—6 illustrates an instrument that was used to seek the opin- ions of a group of classroom teachers toward merit rating.

Chapter 9 Methods and Tools of Research 249 MERIT RATING OPINIONNAIRE Male —Female— Age Teaching level: elementary —secondary— Marital status: single — married — divorced/separated — - widowed — Years of teaching experience — years.
The following statements represent opinions, and your agreement or disagreement will be determined on the basis of your particular convic- tions.

Kindly check your position on the scale as the statement first im- presses you.

Indicate what yoi believe, rather than what you think you should believe.

Ii 1.

It is possible to determine what constitutes merit, or effective teaching.

2.

A valid and reliable instrument can be developed to measure varying degrees of teaching effec- tiveness.

_____ 3.
_____ 3.

Additional remuneration will not result in im- 4.

Merit rating destroys the morale of the teaching force by creating jealousy, suspicion, and dis- trust.

proved teaching.

5.

Mutual confidence between teachers and admin- istrators is impossible if administrators rate teachers for salary purposes.

6.

Merit salary schedules will attract more high- quality young people to the teaching profession, 7.

Merit salary schedules will hold quality teachers in the profession.

8.
8.

Parents will object to having their children taught by nonmerit teachers.

______________ 9.

Merit rating can be as successful in teaching as it is in industry.

10.

The hidden purpose of merit rating is to hold down salaries paid to most teachers by paying only a few teachers well.

II.

There is no justification for paying poor teachers as well as good teachers are paid.

12.

Apple-polishers will profit more than superior teachers from merit rating.

13.
13.

Merit rating will encourage conformity and dis- courage initiative.

14.

The way to make teaching attractive is to reward excellence in the classroom.

15.

Most administrators do not know enough about teaching to rate their faculty members fairly.

16.

Salary schedules based on education and experi- ence only encourage mediocre teaching.

FIGURE 9—6 A Likert-type Opinionnaire on Merit Rating.
250 Part Two Research Methods If an opinionnaire consisted of 30 statements or items, the following score values would be revealing: 30 x 5 = 150 30 x 3 = 90 30 x 1 = 30 Most favorable response possible A neutral attitude Most unfavorable attitude The scores for any individual would fall between 30 and 150—above 90 if opinions tended to be favorable to the given point of view, and below 90 if opinions tended to be infavorable.
It would be wise to conclude this discussion with a recognition of the limitations of this type of opinion measure.

Obviously it is somewhat inexact and fails to measure opinion with the predsion one would desire.

There is no basis for belief that the five positions indicated on the scale are equally spaced.

The interval between "strongly agree" and "agree" may not be equal to the interval between "agree" and "undecided."
It is also unlikely that the statements are of equal value in "for-ness" or "against- ness."

It is unlikely that the respondent can validly react to a short state- ment on a printed form in the absence of real-life qualifying situations.
It is doubtful whether equal scores obtained by several individuals indicate equal fav.orableness toward the given position: Actually, different com- binations of positions can yield equal score values without necessarily indicating equivalent positions of attitude or opinion.

And even though the opinionnaire provides for anonymous response, there is a possibility that people may answer according to what they think they should feel rather than how they do feel.
Semantic Differential The third method of attitude assessment was developed by Osgood, Suci, and Tannenbaum (1957).

The semantic differential is similar to the Likert method in that the respondent indicates an attitude or opinion between two extreme choices.

This method usually provides the individual with a seven-point scale with two adjectives at either end of the scale, such as good—bad, unhealthy—healthy, clean—dirty.
The respondent is asked to rate a group, individual, or object on each of these bipolar scales.

Orte author of this book had a student who used the semantic dif- ferential method to compare the attitudes of regular teachers and special- education teachers toward mentally retarded, learning-disabled, and be- havior-disordered children.

The results of the semantic differential can be graphically displayed as profiles.
Figure 9—7 shows a partial profile of the regular and spedal-education teachers when asked about mentally re- tarded children.

The semantic differential has limitations similar to those of the Thur- stone and Likert approaches.
In spite of these limitations, however, the Chapter 9 Methods and Tools of Research 251 The Average Retarded Child Is: — — — — — 4- — — \ oft wfui Weak Innocent Hard Nice — — —•f — — —-——,t—- — — —\ / r— Strong Clean Healthy Honest Good Moral Fair — — — —u —1 — — Immoral — — — __ ____ — Unfair 3 2 1 0 -1 -2 -3 FIGURE 9—7 Semantic Profiles for Regular Class and Special Class Teachers.

(Dots represent regular class teachers and Xs represent special class teachers.)

process of opinion measurement has merit.


Until more precise measures of attitude are developed, these techniques can serve a useful purpose in social research.

The Interview The interview is in a sense an oral questionnaire.

Instead of writing the response, the subject or interviewee gives the needed information orally and face-to-face.

Most of this discussion focuses on quantitative research.

The use of interviews in qualitative research is covered in Chapter 8.
With a skillful interviewer, the interview is often superior to other data-gathering devices.

One reason is that people are usually more willing to talk than to write.

After the interviewer gains rapport orestablishes a friendly, secure relationship with the subject, certain types of confidential information may be obtained that an individual might be reluctant to put in writing.
(In order to establish sufficient rapport, however, it may be necessary to consider the sex, race, and possibly other characteristics of the interviewer in relation to the interviewee.

For instance, a woman should probably interview rape victims, and a black person should inter- 252 Part Two Research Methods view other blacks regarding instances of discrimination that they have experienced.)
Another advantage of interviewing is that the interviewer can explain more explicitly the investigation's purpose and just what information he or she wants.

If the subject misinterprets the question, the interviewer may follow it with a clarifying question.

At the same time, he or she may evaluate the sincerity and insight of the interviewee.

It is also possible to seek the same information in several ways at various stages of the inter- view, thus checking the truthfulness of the responses.
And through the interview technique the researcher may stimulate the subject's insight into his or her own experiences,, thereby exploring significant areas not antic- ipated in the original plan of investigation.

The interview is also particularly appropriate when one is dealing with young children.

If one were to study what junior high school students like and dislike in teachers, some sort of written schedule would probably be satisfactory.
But in order to conduct a similar study with first-grade pupils, the interview would be the only feasible method of getting re- sponses.

The interview is also well suited for illiterates and those with language difficulties.

Preparation for the interview is a critical step in the procedure.

In- terviewers must have a clear conception of just what information they need.
They must clearly outline the best sequence of questions and stim- ulating comments that will systematically bring out the desired responses.

A written outline, schedule, or checklist will provide a set plan for the interview, precluding the possibility that the interviewer will fail to get important and needed data.

An open-form question, in which the subject is encouraged to answer in his or her own words at some length, is likely to provide greater depth of response.
In fact, this penetration exploits the advantage of the inter- view in getting beneath-the-surface reactions.

However, distilling the es- sence of the reaction is difficult, and interviewer bias may be a hazard.

The closed-form question (in the pattern of a multiple-choice response) is easier to record but may yield more superficial information.

Leading questions that unconsciously imply a specific answer should be avoided.
The question, "Do you think that the United Nations has failed in its peace-keeping function?"

illustrates the danger of eliciting agreement to an idea implanted in the question.

It would be preferable to phrase it, "How effective do you feel the United Nations has been in its peace-keeping function?"

This form is neutral and does not suggest a particular response.

A question of this type would appropriately be fol- lowed by, "Could you explain how you reached this conclusion?"
The relationship between interviewer and subject requires an ex- pertness and sensitivity that might well be called an art.

The initial task of securing the confidence and cooperation of the subject is crucial.

Talking Chapter 9 Methods and Tools of Research 253 in a friendly way about a topic of interest to the subject will often dispel hostility or suspicion, and before he or she realizes it, the subject is freely giving the desired information.
As in the use of the questionnaire1 the interviewer must be able to assure the subject that responseswill be held in strict confidence.

When interviews are not tape recorded, it is necessary for the interviewer to take written notes, either during the interview or immediately thereafter.

The actual wording of the responses should be retained, It is advisable to make the interpretation later, separatingthis phase of analysis from the actual recording of responses.
Recording interviews on tape is preferred because they are conven- ient and inexpensive and obviate the necessity of writing during the interview, which may be distracting to both interviewer and subject.

In- terviews recorded on tape may be replayed as often as necessary for complete and objective analysis at a latertime.

In addition to the words, the tone of voice and emotional impact of the response are preserved by the tapes.
It is unethical to record interviews withoutthe knowledge and permissiori of the subject.

In order to obtain reliable and objective data, interviewers must be carefully trained.

This training should includeskills in developing rapport, the interview, and a host of other asking probing questions, preparing for details.
The Institute for Social Research at the University of Michigan has published an excellent interview-training manual that includes a 90-minute audio cassette of a model interview and some exercises (Guenzel, Berk- mans, & Cannell, 1983).

U ValidIty and Reliability of the interview The key to effective interviewing is establishing rapport.

This skill is somewhat intangible, including both a personality qualityand a developed ability.
Researchers have studied the relationship of interviewer status to the achievement of this confidence.

Many studies have been conducted in which interviewers of different status have interviewed the same re- spondents.

The responses were often significantly different both in how much the subject was willing to reveal and in the nature of the attitudes expressed.

Ethnic origin seems to be important.


Interviewers of the sameethnic background as their subjects seem to be more successful in establishing rapport.

When there is an ethnic difference, a certain amount of suspicion and even resentment may be encountered.

The same relationship seems to prevail when the social status of the interviewer and respondent is different.

Even the interviewer's clothing may have an inhibiting effect.
Younger interviewers seem to be more successful than older, particularly when middle-aged respondents are involved.

Women seem to have a 254 Part Two Research Methods slight advantage over men in getting candid responses, although de- pending on the topic (e.g., male impotence), male interviewers might be more successful.

Of course, experience tends to improve interviewing skill.
Validity is greater when the interview is based upon a carefully designed structure, thus ensuring that the significant information is elic- ited (content validity).

The critical judgment of experts in the field of inquiry is helpful in selecting the essential questions.

Reliability, or the consistency of response, may be evaluated by restating a question in slightly different form at a later time in the inter- view.
Repeating the interview at another time may provide another es- timate of the consistency of response.

If more than one interviewer is used, the researcher must demonstrate reliability of technique and scoring among the interviewers.

This.

can be done through observing the inter- views and having more than one interviewer score each tape or transcript.
As a data-gathering technique, the interview has unique advantages In areaS where human motivation is revealed through actions, feelings, and attitudes, the interview can be most effective.

In the hands of a skillful interviewer, a depth of response is possible that is quite unlikely to be achieved through any other means.

This technique is time-consuming, however, and one of the most difficult to employ successfully.

The danger of interview bias is constant.


Because the objectivity, sensitivity, and insight of the interviewer are crucial, this procedure is one that requires a level of expertness not or- dinarily possessed by inexperienced researchers.

0 Methodology Q methodology, devised by Stephenson (1953), is a technique for scaling objects or statements.
It is a method of ranking attitudes or judgments (similar to the first step in the Thurstone technique) and is particularly effective when the number of items to be ranked is large.

The procedure is known as a Q-sort, in which cards or slips bearing the statements or items are arranged in a series of numbered piles.

Usually nine or eleven piles are established, representing relative positions on a standard scale.

Some examples of simple polarized scales are illustrated.
most important most approve most liberal most favorable most admired most like .me least important least approve least liberal least favorable least admired least like me Chapter 9 Methods and Tools of Research 255 The respondent is asked to place a specified number of items on each pile, usually on the basis of an approximatelynormal or symmetrical distribution.

From 50 to 100 items should be used.


Most Like Me Pile % 1 3 2 4 7 12 4 17 5 20 Least Like Me 6 7 8 9 12 7 4 17 Self-concept 0-sort Let us assume that a Q-sort has been designed to measure the before- and-after therapy status of a subject.

A few examples of appropriate traits are presented to be placed on the scale.
ignored admired disliked cheerful happy discouraged energetic loved hated stupid afraid suspicious successful enthusiastic friendly A change in position of items from before-therapy to after-therapy would indicate possible change or improvement in self-esteem.

Comput- ing the coefficient of correlationbetween the pile positions of items before and after therapy would provide a measure of change.
If no change in item placement had occurred, the coefficient of correlation would be + 1.00. coefficient would be —1.00.

If a completely opposite profile appeared, the Although a perfect + 1.00 or —1.00 coefficient is improbable a high pos- itive coefficient would indicate little change, whereas a high negative coefficient would indicate significant change.

Another type of Q-sort solicits the composite judgment of a selected panel of experts (in this case, professors of educational research).
The criterion of judgment involves the relative importance of research concepts that should be included in the introductory course in educational research.

One hundred slips, each listing a concept, were to be sorted into nine to least important.
A few of the con- piles, ranging from most important cepts that were considered are listed: hypothesis probability dependent variable coefficient of correlation sources of reference materials preparing the research report randomization post hoc fallacy experimental method interviewing level of significance the research proposal 256 Part Two Research Methods attitude studies historical method survey null hypothesis preparing a questionnaire deductive method descriptive method sampling intervening variables independent variable Q-sorts standard deviation nonparametric statistics action research The mean value of the positions assigned to each item indicates the composite judgment of the panel as to its relative importance.
Two applications of the Q-sort technique have been illustrated in our simplified discussion.

The first attempted to measure change in the atti- tude of an individual toward himself or herself, the second the composite judgment of a group of individuals.

Many types of analysis may be carried on in the area of attitudes by the use of Q methodology.

Researchers contemplating the use of this technique should carefully consider the theoretical assumptions underlying the criteria and the items selected.
Social Scaling Soclometry Sociometry is a technique for describing the social relationships among individuals in a group.

In an indirect way it attempts to describe attractions or repulsions between individuals by asking them to indicate whom they would choose or reject in various situations.
Children in a school classroom may be asked to name in order of preference (usually two or three) the child or children that they would invite to a party, eat lunch with, sit next to, work on a class project with, or have as a dose friend.

Although some researchers object to the method, it is also common to ask the children to name the children, again in order of preference, that they would least like to invite to a party, eat lunch with, sit next to, and so forth.
There is an extensive body of sociometric research on classroom groups from kindergarten through college, fraternities and sororities, dor- mitory residents, camp groups, factory and office workers, military combat units, and entire communities.

The United States Air Force has used sociometry to study the nature of leadership in various situations.
For example, the following question was used in a study of air combat crews: "What member of the crew would you select, disregarding rank, as the most effective leader if your plane were forced down in a remote and primitive area?

Name three, in order of your preference."

Chapter 9 Methods and Tools of Research 257 Scoring Sociometric Choices One widely used procedure is to count the number of times an individual is chosen, disregarding the order of choice.
This is the simplest method, and it is widely used.

The objection has been raised that it is insensitive, for it does not distinguish between a first and third choice.

Another procedure is to score a first choice three points, a second choice two points, and a third choice one point.

This plan's weakness is that it suggests that the difference between a third choice and ne choice at all is identical to the differences between third, second, and first choices.

This assumption is difficult to defend.


A third scoring procedure is based upon the concept of the normal curve standard score distribution.

However, this method is more complex and seldom used.
Once obtained, the scores for each individual in the group can be related to such measures as intelligence or other traits that can be meas- ured by tests, or to such categories as sex, race, nationality, religious affiliation, economic status, birth order, family size, grade-point average, teacher, employer, or other characteristics that may be of interest to the researcher.

The Sociogram Sociometric choices may be represented graphically on a chart known as a sociogram.
There are many versions of the sociogram pattern, and the reader is urged to consult specialized references on sociometry.

A few observations will ifiustrate the nature of the sociogram.

In consulting a sociogram, boys may be represented by triangles and girls by circles.

A choice may be represented by a single-pointed arrow, a mutual choice by an arrow pointing in opposite directions.

Those chosen most often are referred to as stars, those not chosen by others as isolates.
Small groups made up of individuals wo choose one another are cliques.

Identifying numbers are placed within the symbols.

Numbers of those chosen most often are placed nearest the center of the diagram, and numbers of those chosen less often are placed further outward.

Those not chosen are, literally, on the outside (see Figure 9—8).

Remember, however, that relationships among individuals in a group are changeable.

Children's choices are most temporary, for stability tends to develop only with age.
Students of group relationships and classroom teachers may con- struct a number of sociograms over a period of time to measure changes that may have resulted from efforts to bring isolates into closer group relationships or to transform cliques into more general group membership.

The effectiveness of socializing or status-building procedures can thus be 258 Part Two Research Methods FIGURE 9—8 Sociogram Showing First and Second Choices in a Third Grade Class.
measured by the changes revealed in the sociogram.

Because sociometry is a peer rating rather than a rating by superiors, it adds another dimension to the understanding,pf members of a group.

"Guess-who" Technique A process of description closely related to sociometry is the "guess-who" technique.

Developed by Hartshorne and May (1929), the process consists of descriptions of the various roles played by children in a group.

Children are asked to name the individuals who fit certain verbal descriptions.
This one is always happy.

This one is always picking on others.

Chapter 9 Methods and Tools of Research 259 This one is always worried.

This one never likes to do anything.

This one wifi always help you.

Items of this type yield interesting and significant peer judgments and are useful in the study of individual roles.

Of course, the names of children chosen should not be revealed.
Social-distance Scale Another approach to the description and measurement of social relation- ships is the social-distance scale, developed by Bogardus (1933).

This device attempts to measure to what degree an individual or group of individuals is accepted or rejected by another individual or group.

Various scaled situations, with score values ranging from acceptance to rejection, are established.

The individual checks his or her position by choosing one of the points on the scale.
For example, in judging acceptance of different minority groups, the choices might range between these ex- tremes: Complete acceptance Partial acceptance Rejection I wouldn't object to having a member of this group become a member of my family by marriage.

I wouldn't mind sitting next to a member of this group on a bus.

I don't think that members of this group should be admitted into our country.
When applied to an individual in a classroom situation, the choices might range between these extremes: Complete acceptance Partial acceptance Rejection I'd like to have this student as my best friend.

I wouldn't mind sitting near this student.

I wish this student weren't in my room.


Of course, in the real social-distance scale, illustrated by the sample items above, there would be a larger number of evenly spaced scaled positions (usually seven in number), giving a more precise measure of acceptance or rejection.

Devices of the type described here have many possibilities for the description and measurement of social relationships and, in this important area of social research, may yield interesting and useful data.
260 Part Two Research Methods Organization of Data Collection The following discussion is directed to the beginner and does not suggest appropriate procedures for the advanced researcher.

Theses, dissertations, and advanced research projects usually involve sophisticated experimental designs and statistical analysis.

The use of the computer has become standard procedure.

Because it can effectively process complex variable relationships, it has made a significant contribution to research.
Chapter 12 discusses computers and their uses for organizing and analyzing data.

When the results of an observation, interview, questionnaire, opin- ionnaire, or test are to be analyzed, problems of organization confront the researcher.

Even when a computer will be used, the first problem is to designate appropriate, logical, and mutually exclusive categories for tab- ulation of the data.

At times the hypothesis or question to be answered may suggest the type of organization.
If the hypothesis involved the difference between the attitudes of men and women toward teacher merit rating, the categories male and female would be clearly indicated.

In other instances the categories are not determined by the hypothesis, and other subdivisions of the group under investigation may be desirable.

The re- searcher should keep these issues in mind when selecting, or designing, the data collection procedure.
Proper attention given to this matter of organization early in the research process can save a great deal of time at the data analysis phase.

When the responses or characteristics of a group are analyzed, it is sometimes satisfactory to describe the group as a whole.

In simple types of analysis, when the group is sufficiently homogeneous, no breakdown into subgroups is necessary.

But in many situations the picture of the whole group is not clear.
The heterogeneity of the group may yield data that have little meaning.

One tends to get an unreal picture of agroup of subjects that are actually very different from one another, and the differences are concealed by a description of a nonexistent or unreal av- erage.

In such cases it may be helpful to divide the group into more homogeneous subgroups that have in common some distinctive charac- teristics that may be significant for the purpose of the analysis.
Distin- guishing between the response of men and women, between elementary and secondary teachers, or between gifted and average-learning children may reveal significant relationships.

For example, a new type of classroom organization may seem to have little impact on a group of students.

But after dividing the group into two subgroups, the gifted and the average learners, some interesting relationships may become clear.
The grouping may be effective for the bright students but most ineffective for the average learners.

Many studies employ the classification of data into dichotomous, or twofold, categories.

When the categories are established on the basis of test scores, rankings, or some other quantitative measure, itmay be ad- visable to compare those at the top rith those at the bottom, omitting Chapter 9 Methods and Tools of Research 261 from the analysis those near the middle of the distribution.
It is possible to compare the top third with the bottom third, or the top 25 percent with the bottom 25 percent.

This eliminates those cases near the midpoint that tend to obscure the differences that may exist.

Through elimination of the middle portion, sharper contrast is achieved, but the risk of the regression effect is increased.

Comparisons are not always diqhotomous.


At times it is desirable to divide a sample into more than two categories, depending on the nature of the variables that are to be considered.

Outside Criteria for Comparison In addition to the comparisons that may be made between subgroups within the larger group, the group may be analyzed in terms of some outside criteria.

Of course, it must be assumed that reasonably valid and reljable measuring devices are available fOr making such comparisons.
These "measuring sticks" may consist of standardized tests, scorecards, frequency counts, and physical as well as psychological measuring, de- vices.

Some of these outside criteria include the following: 1.

Prevailing conditions, practices, or performance of comparable units.

Com- parison may be made with other communities, schools, and classes.
Comparisons may be made with groups that represent best condi- tions or practices or typical or average status, or with equated groups that have been matched in terms of certain variables, leaving one variable or a limited number of variables for comparison.

2.

What experts believe to constitute best conditions or practices.

These experts may comprise a panel specially chosen for the purpose.
A group of practitioners in the field who are assumed to be most familiar with the characteristics under consideration, or the survey staff itself, may constitute the body of experts.

The judgments of recognized au- thorities who publish their opinions are frequently selected as cri- teria.

3.

What a professional group, a commission, an .accrediting agency, or another scholarly deliberative body establishes as appropriate standards.
These stand- ards may be expressed as lists of objectives or may be quantitative measures of status for accreditation or approval.

The American Med- ical Association's standards for accreditation of medical schools, the accreditation standards of the North Central Association of Second- ary Schools and Colleges, or the standards of the National Council for the Accreditation of Teacher Education for programs of teacher education are examples of evaluative criteria.

4.
4.

Laws or rules that have been enacted or promulgated by a legislative or quasi- legislative body.

Teacher certification regulations, school-building 262 Part Two Research Methods standards, or health and safety regulations provide appropriate cri- teria for comparison.

5.

Research evidence.

The factors to be analyzed may be examined in the light of principles confirmed by published scholarly research.

6.

Public opinion.
6.

Public opinion.

Although not always appropriate as a criterion of what should be, the opinions or views of "the man on the street" are sometimes appropriate as a basis for comparison.

Sorting and Tabulating Data Tabulation is the process of transferring data from the data-gathering in- struments to the tabular form in which they may be systematically ex- amined.

This process may be performed in a number of ways.

In simple types of research, hand-tabulating procedures are usually employed.
In more extensive investigations, a card-tabulating process may be used, possibly including machine methods.

Most simple research studies employ the method of hand-sorting and recording, with tabulations written on tabulation sheets.

To save time and ensure greater accuracy, it is recommended that one person read the data while the other records them on the tabulation sheet.

In constructing tally form sheets, it is important to provide enough space to record the tallies in each category.
The following discussion on hand tabulation emphasizes the impor- tance of careful planning before the sorting and tabulation begin.

Without careful planning, inexperienced researchers may waste effort when tab- ulating responses on a set of questionnaires filled out by a group of teachers.

After completing the tabulation, they may decide to compare the responses of elementary teachers with secondary teachers.

This would involve retabulating the responses of the questionnaire.
It might then occur to them that it would be interesting to compare men's responses to women's.

Another handling of the questionnaires would be necessary.

If they had decided upon their categories before tabulation, one handling of the questionnaires would have been sufficient.

Sorting the questionnaires into two piles, one for elementary teachers and another for secondary teachers, then sorting each of these into separate piles for men and for women, would have yielded four stacks.
Then, through the separate tabulation of each pile, one planned operation would have yielded the same amount of information as three unplanned operations.

Before tabulating questionnaires or opinionnaires, it is always im- portant to decide upon the categories that are to be analyzed.

If this decision is delayed, it may be necessary to retabulate the items a number of times, needlessly consuming a great deal of time and effort.
If the data-gathering device called for a larger number of responses, the system of presorting would be similar.

It would be advisable, however, Chapter 9 Methods and Tools of Research 263 to set up a separate tabulation sheet for each of the categories, because a single sheet would become unwieldy.

For all but the simplest cases, a computer would handle this easily as long as ead variable is coded properly (e.g., 1 = male, 2 = female).
Figure 9—9 illustrates how a three-item opinionnaire response could be tabulated for a question such as the following: An honor system would eliminate cheating in examinations.

lagree I don't know__ I disagree — Students may apply these procedures to dassify and tabulate similar types of data These data sheets are not ordinarily presented in the report, but they may suggest ways in which some of the data maybe presented as tables or graphic figures.
Relatively simple computer programs are available that can handle much of this sorting and tabulatingif the data are properly organized initially (see Chapter 12).

Tables and FIgures The process of tabulation which has just been described is the first step in the construction of the tables for a research report.

It is likely that the beginning researcher thinks of tables purely as aids to understanding.
Displaying data in rows and columns according to some logical plan of classification may serve an even more important purpose in helping re- searchers to see the similarities and relationships of their data in bold relief.

A discussion of the construction and use of tables and figures is presented in some detail in Chapter 13.
AGREE DON'T KNOW DISAGREE Freshmen Sophomores Juniors Seniors FIGURE 9—9 Tabulation Form Providing for the Analysis of 12 Possible Response Categories Based upon Question 1 on an Opinionnaire.

264 Part Two Research Methods Percentage Comparisons Presenting data by frequency Counts has a number of limitatiOns.

If the groups to be compared are unequal in size, the frequency count may have little meaning.
Converting to percentage responses enables the researcher to compare subgroups of unequal size meaningfully.

Translating frequency counts into percentages indicates the number-per-hundred compared.

The provision of a common base makes the comparison clear.

Several limitations should be recognized in using percentage com- parisons.

Unless the number of frequencies is reasonably large, a per- centage may be misleading and may seem to suggest an unwarranted generalization.
It may be appropriate to indicate that, of four physicians interviewed, one believed that a particular medication would be harmful.

To indicate that 25 percent of physicians interviewed believed that the medication would be harmful creates an image of a larger sample of physicians than was actually interviewed.

It is essential that both fre- quency counts and percentage responses be included in the presentation and analysis of data.
In converting frequency counts to percentages, rounding to the near- est percentage point is preferable.

Because the type of data presented in educational research is not very precise, there is little value inexpressing percentages in decimal values.
In other situations, however, such as the drug industry, where ratio scales of measurement are often used, it would be extremely important to carry a percentage reported to four or five decimal places, particularly when a trace of an element would be harmful if exceeded.

When using percentages in dichotomous comparisons, it is necessary to state the percentage in only one of the categories.

If 65 percent of the respondents are men, it is not necessary to indicate that 35 percent are women.
Unnecessary duplication is evidence of poor reporting.

Crossbreaks A crossbreak table is a way of presenting observations; it is a useful device for organizing and describing a data relationship.

An example of an opin- ionnaire response is Figure 9—10.

The topic presented on theopinionnaire was: "A legal abortion, during the first trimester, should be the right of any woman."

Ranking and Weighting Items There are times when response categories are not mutually exclusive.
Preferences for certain things or reasons for an act are usually explained in terms of a number of factors, rarely single ones.

It would be unrealistic Chapter 9 Methods and Tools of Research 265 Roman Catholic Protestant Agree 8(14) 30(61) Jewish Numbers are expressed frequencies of response Numbers in, parentheses are expressed as percentages 25 (69) No Opinion Disagree 10(17) 7 (14) 9(25) 40(69) 12 (24) 2 (6) FIGURE 9—10 Crossbreak Tabulation of Attitudes Regarding Abortion.
to expect respondents to indicate their favorite type of recreation or the single reason they dedded to attend a particular university.

In such in- stances it would be appropriate to ask the respondents to indicate two or three responses in order of importance or preference.

This ranking of items makes possible a useful method of analysis.

Items may be weighted in mverse order.


For example, if three items are to be ranked, it is ap- propriate to assign weightings as follows: 1st choice 2nd choice 3rd choice 3 points 2 points 1 point A composite judgment of the importance of the items could be de- termined by the weighted totals or averages for all the respondents.

Remember that when items are ranked in order, the differences between ranked items may not be equal.

Ranking is not the most refined method of scaling.


Limitations and Sources of Error A number of limitations and sources of error in the analysis and inter- pretation of data can jeopardize the success of an investigation.

New researchers in particular need to be aware of these potential pitfalls.

Some of these problems include: 1.

Confusing statements with facts.

A common fault is the acceptance of statements as facts.
What individuals report may be a sincere expression of what they believe to be the facts in a case, but these statements are not necessarily true.

Few people observe skillfully, and many forget quickly.

It is the researcher's responsibility to verify all statements as completely as possible before they are accepted as facts.

2.

Failure to recognize limitations.


The very nature of research implies certain restrictions or limitations about the group or the situation 266 Part Two Research Methods described—its size, its representativeness, and its distinctive com- position.

Failure to recognize these limitations may lead to the for- mulation of generalizations that are not warranted by the data col- lected.

3.

Careless or incompetent tabulation.


When one is confronted with a mass of data, it is easy to make simple mechanical errors.

Placing a tally in the wrong cell or incorrectly totaling a set of scores can easily invalidate carefully gathered data.

Errors sometimes may be attrib- uted to clerical helpers with limited ability and little interest in the research project.

4.

Faulty logic.

This rather incluive category embraces a number of errors in the thought processes of the researcher.
Invalid assump- tions, inappropriate analogies, inversion of cause and effect, con- fusion of a simple relationship with causation, failure to recognize that group phenomena may not be used indiscriminately to predict individual occurrences or behavior, failure to realize that the whole may be greater than the sum of its parts, belief that frequency of appearance is always a measure of importance, and many other errors are limitations to accurate interpretation.

5.

The researcher's unconscious bias.


Although objectivity is the ideal of research, few individuals achieve it completely.

There is great temptation to omit evidence unfavorable to the hypothesis and to overemphasize favorable data.

Effective researchers are aware of their feelings and the likely areas of their bias and constantly endeavor to maintain the objectivity that is essential.
Summary The researcher chooses the most appropriate instruments and procedures that pro- vide for the collection and analysis of data upon which hypotheses may be tested.
The data-gathering devices that have proven useful in educational research include psychological tests and inventories, questionnaires, opinionnaires, 0 methodology, observation, checklists, rating scales, scorecards, scaled speóimens, document or content analyses, interviews, sociograms, "guess-who" techniques, and social- distance scales.

Some research investigations use but one of these devices.

Others employ a number of them in combination.


Students of educational research should make an effort to familiariz&Themselves with the strengths and limitations of these tools and should attempt to develop skill in constructing and using them effectively.

The analysis and interpretation of data represent the application of deductive and inductive logic to the research process.
The data are often classified by division Into subgroups and then analyzed and synthesized in such a way that hypotheses may be verified or rejected.

The final result may be a new principle or generalization.

Data are examined in terms of comparisons between the more homogeneous seg- ments within the whole group and by comparison with some outside criteria.
Chapter 9 Methods and Tools of Research 267 The processes of classification, sorting, and tabulation of data are important parts of the research process.

In extensive studies, mechanical and/or computer methods of sorting and tabulating are used to save time and effort and to minimize error.

In smaller projects, hand-sorting and hand-tabulating processes are still often employed.
The researcher must guard against the limitations and sources Of error inherent in the processes of analysis and interpretation of data.

Exercises 1.

For what type of problem and under what circumstances would you find the following data-gathering techniques most appropriate?

a. Likert scale b. Questionnaire c. Interview d. Observation e. Q-sort 2.

Construct a short questionnaire that could be administered in class.

The following topics are suggested: a.

Leisure Interests and Activities b.


Reasons for Selecting Teaching as a Profession c. Methods of Dealing with School Discipline d. Political Interests and Activities 3.

Construct a Likert-type opinionnaire dealing with a controversial problem.

One of the following topics may be appropriate: a.

Teacher Affiliation with Professional Organizations b.

Teacher Strikes and Sanctions c. Religious Activities in the School Program d. The Nongraded School 4.
Construct a short rating scale to be used for the evaluation of the teaching performance of a probationary teacher.

5.

To what extent is the administration of personal and social adjustment inventories an invasion of a student's privacy?

References ANASTASIA, A.

(1982).

Psychological testing (5th ed.).

New York: Macmillan.

BABBlE, E. R. (1973).

Survey research methods.

Belmont, CA: Wadsworth.

BOGARDUS, E. S. (1933).

A social distance scale.

Sociology and Social Research, 17, 265—271.


CRONBACH, L. J.

(1951).

Coefficient alpha and the internal structure of tests.

Psy- chometrika, 16, 297—334.

268 Part Two Research Methods CRONBACH, L. J.

(1984).

Essentials of psychological testing (4th ed.).

New York: Harper and Row.

EDWARDS, A. L. & KENNEY, K. C. (1946).

A comparison of the Thurstone and Likert techniques of attitude scale construction.

Journal of Applied Psychology, 30, 72—83.

FANTUZZO, J. W., & CLEMENT, P. W. (1981).


Generalization of the effects of teacher- and self-administered token reinforcements to --nontreated students.

Journal of Applied Behavior Analysis, 14, 435—447.

GESELL, A.

(1948).

Studies in child development.

New York: Harper and Brothers.

GUENZEL, P. J., BERIUvIANS, T. R., & CANNELL, C. F. (1983).

General interviewing techniques: A self-instructional workbook for telephone and personal interviewer train- ing.
Ann Arbor, MI: Institute of Social Research, University of Michigan.

HAKEL, M. (1968).

How often is often?

American Psychologist, 23, 533—534.

HALL, H. V., PRICE, A.

B., SHINEDLING, M., PEIZER, S. B., & MASSEY, R. H. (1973).

Control of aggressive behavior in a group of retardates, using positive and negative reinforcement.

Training School Bulletin, 70, 179—186.

HARRIS, D. B.

(1963).

Children's drawings as measures of intellectual maturity.

New York: Harcourt, Brace and World.


HARTSHORNE, H. & MAY, M. A.

(1929).

Studies in deceit.

New York: Macmillan.

KAZDIN, A. E. (1982).

Single-case research designs.

New York: Oxford.

MITCHELL, J. V., JR.

(Ed.).

(1983).

Tests in print, III.

Lincoln: University of Nebraska Press.

MITCHELL, J. V., JR.

(Ed.).

(1985).

Mental measurements yearbook (9th ed.).

Highland Park, NJ: Gryphon.

MOONEY, R. L. (1941).

Problem checklist, high school form.


Columbus, OH: Bureau of Educational Research, Ohio State University.

OSGOOD, C. E., Suci, G. J., & TANNENBAUM, P. H. (1957).

The measurement of meaning.

Urbana, IL: University of Illinois Press.

SEE, H. W. (1957).

Send it to the President.

Phi Delta Kappan, 38, 129—130.

STEPHENSON, W. (1953).

The study of behavior.

Chicago: University of Chicago Press.

THURSTONE, L. L. & CHAVE, E. J.

(1929).

The measurement of attitudes.


Chicago: University of Chicago Press.

Additional Readings ALEVIZOS, P. N., ET AL.

(1978).

Behavior observation instrument: A method of direct observation for program evaluation.

Journal of Applied Behavior Analysis, 11, 243—257.

ALVERSON, L. C. (1978).

Comparison of indirect measures for long duration be- havior.

Journal of Applied Behavior Analysis, 11, 530.

American Psychological Association Standards for Educational and Psychological Tests.

(1974).
(1974).

Washington, DC: APA.

BONNEY, M. F. (1960).

Sociometric methods.

In Encyclopedia of educational research.

New York: Macmillan.

CARTWRIGHT, C. A.

& CARTWRIGHT, C. P. (1974).

Developing observation skills.

New York: McGraw-Hill.

EBEL, R. L. (1972).

Essentials of educational measurement.

Englewood Cliffs, NJ: Pren- tice-Hall.

FENLASON, A.

(1952).

Essentials of interviewing.

New York: Harper & Row.

GRONLUND, N. B.

(1959).

Sociometry in the classroom.

New York: Harper & Brothers.


HEIuuorr, R. E. (1960).

Survey research method.

In Encyclopedia of educational re- search.

New York: Macmillan.

Chapter 9 Methods and Tools of Research 269 Hopxir.js, K. D. & STANLEY, J. C. (1972).

Educational and psychological measurement and evaluation (5th ed.).

Englewood Cliffs, NJ: Prentice-Hall.

HYMAN, H. H. (1975).

Survey design and analysis.

Beverly Hills, CA: Glencoe Press.

HYMAN, H. H., EF AL.

(1954).

Interviewing in social research.


Chicago: University of Chicago Press.

KAHN, R. L. & CANNELL, C. F. (1957).

The dynamics of interviewing.

New York: John Wiley & Sons.

KERLINGER, F. N. (1973).

Foundations of behavioral research.

New York: Holt, Rinehart & Winston.

KISH, L. (1965).

Survey sampling.

New York: John Wiley &.

Sons.

MOREF.Jo, J. L. (1951).

Sociometry: Experimental method and the science of society.

New York: Beacon Press.

NUNNALY, J. C. (1972).


Educational measurement and evaluation.

New York: McGraw- Hill.

OPPENHEIM, A. N. (1966).

Questionnaire design and attitude measurement.

New York: Basic Books.

SUMMERS, G. F.

(Ed.).

(1970).

Attitude measurement.

Chicago: Rand McNally.

TORGERS0N, W. S. (1958).

Theory and methods of scaling.

New York: John Wiley & Sons.

WEBB, E. J., CAMPBELL, D. T., SCHWARTZ, R. D. & SECHREST, L. (1966).
Unobtrusive measures: Nonreactive research in the social sciences.

Chicago: Rand McNally.

___ PART THREE Data Analysis THE FINAL THREE CHAPTERS of this book all deal with quantitative anal- ysis of data utilizing statistical procedures.

The purpose is to give the reader an overview of commonly used statistical procedures and enough information to calculate many of them by hand (using a calculator, it is to be hoped) or with the aid of a computer.
Chapter 10, Descriptive Data Analysis, provides a general depiction of the types of statistics used in educational research (descriptive versus in- ferential; parametric \ersus nonparametric), a brief description of data or- ganization methods, and includes a variety of statistical procedures for de- scribing a group of subjects.
The statistical procedures included in Chapter 10 include measures of central tendency, measures of dispersion or varia- bility, the normal distribution, standard scores, correlation, and the standard error of estimate.

Chapter 11, Inferential Data Analysis, explains the concepts of statistical inference and the central limit theorem and includes a number of common inferential statistical procedures.
Among the statistical procedures included are various forms of the t test, analysis of variance, the test for homogeneity of variance, partial correlation, multiple regression, chi square, and the Mann- Whitney test.

Chapter 12, Computer Data Analysis, demonstrates how computers can be used to calculate the statistical procedures described in Chapters 10 and 11.

This chapter describes how data can be organized for easy entry into a computer program, and provides four actual computer analyses.
The computer analyses include two programs and their outputs run on a university main frame computerip eSASacka 9frorL These examples consist of a program for descriptive statistics, including correlation, and a program for charting data.

Also included are the programsand their outputs• from a main frame SPSS multiple regression program and an SPSS for personal computer analysis of variance program.
10 Descriptive Data Analysis BECAUSE THIS TEXTBOOK CONCENTRATES on educational research methods, the following discussion of statistical analysis is in no sense complete or exhaustive.

Only some of the most simple and basic concepts are pre- sented.

Students whose mathematical experience includes high school algebra should be able to understand the logic and the computational processes involved and should be able to follow the examples without difficulty.

The purpose of this discussion is threefold: 1.


To help the student, as a consumer, develop an understanding of statistical terminology and the concepts necessary to read with under- standing some of the professional literature in educational research.

2.

To help the student develop enough competence and know-how to carry on research studies using simple types of analysis.

3.

To prepare the student for more advanced coursework in statistics.
The emphasis is upon intuitive understanding and practical appli- cation rather than on the derivation of mathematical formulas.

Those who expect and need to develop real competence in educational research will have to take some of the following steps: 1.

Take one or more courses in behavioral statistics and experimental design.

2.
2.

Study more specialized textbooks in statistics, particularly those deal- ing with statistical inference (e.g., Ferguson, 1981; Glass & Hopkins, 1984; Guilford & Fruchter, 1978; Hays, 1981; Kirk, 1982; Siegel, 1956; Winer, 1971).

273 274 Part Three Data Analysis 3.

Read research studies in professional journals extensively and critically.

4.

Carry on research studies involving some serious use of statistical procedures.

What Is Statistics?
What Is Statistics?

Statistics is a body of mathematical techniques or processesjthering, orgaruzing, aiyzing, and interprng numencar dita Becausifie- search yiekTh such quantitative data, statistics is a basic tool of measure- ment, evaluation, and research.

The word statistics is sometimes used to describe the numerical data that are gathered.
Statistical data describe group behavior or group char- acteristics abstracted from a number of individual observations that are combined to make generalizations possible.

Everyone is familiar with such expressions as "the average family income," "the typical white-collar worker," or "the representative city."

These are statistical concepts and, as group characteristics, may be ex- pressed in measurement of age, size, or any other traits that can be described quantitatively.
When we say that "the average fifth-grade boy is ten years old," we are generalizing about all fifth-grade boys, not any particular boy.

Thus the statistical measurement is an abstraction that may be used in place of a great mass of individual measures.

The research worker who uses statistics is concerned with more than the manipulation of data.

The statistical method serves the fundamental purposes of description and analysis, and its proper application involves answering the following questions: 1.
What facts need to be gathered to provide the information necessary to answer the question or to test the hypothesis?

2.

How are these data to be selected, gathered, organized, and analyzed?

3.

What assumptions underlie the statistical methodology to be em- ployed?

4.

What conclusions can be validly drawn from the analysis of the data?
Research consists of systematic observation and description of the characteristics or properties of .objects or events for the purpose of dis- covering relationships between variables.

The ultimate purpose is to de- velop generalizations that may be used to explain phenomena and to predict future occurrences.

To conduct research, we must establish prin- ciples so that the observation and description have a commonly under- stood meaning.
Measurement is the most precise and universally accepted process of description, assigning quantitative values to the properties of objects and events.

Chapter 10 Descriptive Data Analysis 275 Parametric and Nonparametric Data In the application of statistical treatments, two types of data are recognized.

1.

Parametric data.


Data of this type are ured data, and parametric statistical tests assume that the data are normally or nearly ribieLarametric tests are applied to both interval- and ratio- scaled data.

2.

Non parametric data.

Data of this type are her counted or ranked.

Nonparametric tests, sometimes known as tbjon-frees do not rest upon the more stringent assumption of normally distributed populatiois.
Table 10—1 presents a graphic summary of the levels of quantitative description and the types of statistical analysis appropriate for each level.

These concepts will be developed later in the discussion.

However, the reader should be aware that many of the parametric statistics (t test, analysis of variance, and Pearson's r in particular) are still appropriate even when the assumption of normality is violated.
This robustness has been demonstrated for the t test, analysis of variance, and, to a lesser extent, analysis of covariance by a number of researchers TABLE 10—1 Levels of Quantitative Description1 Level Scale Process Data Treatment Some Appropriate Tests 4 3 2 1 Ratio measured equal intervals true zero ratio relationship Interval measured equal intervals no true zero Ordinal ranked in order Nominal .
classified and counted parametric t test analysis of variance analysis of covariance factor analysis Pearson's r nonparametric Spearman's rho (p) Mann-Whitney Wilcoxon chi square median .

sign 1Refer to Chapter 9 for a discussion of the four levels of measurements.

276 Part Three Data Analysis including Glass, Peckham, and Sanders (1972), Lunney (1970), and Mande- vile (1972).
Thus, with ordinal data and even with dichotomous data (two choices such as Pass—Fail), these statistical procedures, which were de- signed for use with interval and ratio data, may be appropriate and useful.

Pearson's r, which can also be used with any type of data, will be discussed later in this chapter.

Descriptive and Inferential Analysis Up until now we have not discussed the limits to which statistical analysis may be generalized.

Two types of statistical application are relevant.


Descriptive Analysis.

Descriptive statistical analysis limits generalization to the particular group of individuals observed.

Noçon4iipns areex- tendedypnd sand any similarity to those outside the group ?Tbe assumed The data descnbe one group and that gp only Much simple action rèarch involves deffàiiTa.nd provides valuable information about the nature of a particular group of individuals.

Inferential Analysis.


Inferential statistical analysis always involves the process of sampling and the selection of a small group that is assumed to be related to the population from which it is drawn.

The small group is known as the sample, and the large group is the population.

Drawin conclusions about populations based upon observations of samples is the trrpose F1iWerential analysts A siátitk1s a méasuri based on observations of the characteristics of a sample.
A statistic computed from a sample may be açj, the corresponding value in the population from which the sample is selected.

Statistics are usually represented by letters of our Roman alphabet suchTas r. Parameters, on the other hand, are usually represented by letters of the Greek alit such Before any assumptions can be made, it is essential that the individ- uals selected be chosen in such a way that the small group, or sample, approximates the larger group, or population.
Within a margin of error, which is always present, and by the use of appropriate statistical tech- niques, this approximation can be assumed, making possible the esti- mation of population characteristics by an analysis of the characteristics of the sample.

It should be emphasized that when data are derived from a group without careful sampling procedures, the researcher should carefully state • that findings apply only to the group observed and may not apply to or describe other individuals or groups.
The statistical theory of sampling is complex and involves the estimation of error of mferred meisurèments Chapter 10 Descriptive Data Analysis 271 error that is inherent in estimating the relationship between a random sample and the population from which it is drawn.

Inferential data analysis is presented in Chapter 11.

The Organization of Data The list of test scores in a teacher's grade book provides an example of unorganized data.
Because the usual method of listing is alphabetical, the scores are difficult to interpret without some other type of organization.

Alberts, James Brown, John Davis, Mary Smith, Helen Williams, Paul 60 78 90 70 88 The Array.

Arranging the same scores in descen produces what is known as an array.

of magnitude 90 88 78 70 60 The array provides a more convenient arrangement.

The higl1 score (90), the lowest score (60), and the middjçore (78) are.

easily identified.
easily identified.

Thus Iige (the difference between the highest and lowest scores, plus one) can easily be determined.

Illustrated in Table 10—2 is an ungrouped data arrangement in array form.
TABLE 10—2 Scores of 37 Students on a Semester Algebra Test Range = 98 — 57 = 41 + 1=42 67 67 64 60 57 278 Part Three Data Analysis Grouped Data Distributions Data are often more clearly presented when scores are grouped and a frequency coftimn is included.

Data can be presented in fre uen table (see Table 10—3) with different class intervals, depending on t e number and range of the scores.
A score interval with an 2 rointsrnay be preferable because its midpoint is a whole number rather than a fraction.

Because all scores are assumed to fall at the midpoint (for purposes of computing the mean) the computation is less complicated: Even interval of four: 8 9 10 11 (midpoint 9.5) Odd interval of five: (midpoint 10) There is no rule that rigidly determines the proper score interval, and intervals of ten are fretuently used.
Statistical Measures Several basic types of statistical measures are appropriate in biig and analyzin& data in arneaninfu1waX.

Measures of central tendency or averages Mean Median Mode TABLE 10—3 Scores on Algebra Test Grouped in Intervals of Five Score Interval Tallies Frequency (1) Includes (96 97 98 99 100) (91 92 93 94 95) etc.
96—100 91—95 86—90 81—85 76—80 71—75 66—70 61—65 56—60 11 11 1111 id4tll 14T .1+t 1 111 J44T 1 11 2 2 4 7 11 3 5 1 2 N = 37 chapter 10 DescriptIve Data Analysis 279 Measures of or dispersion Range Variance Standard deviation Measures of Percentile rank Percentile score Standard scores Measures of ojjg Coefficient of correlation Measures of Central Tendency Nonstatisticians use averages to describe the characteristics of groups in a general way.
The climate of an area is often noted by average temperature or average amount of rainfall.

We may describe students by grade-point averages or by average age.

Socioeconomic status of groups is indicated by average income, and the return on an investment portfolio may be judged in terms of average income return.

But to the statistician, the term average is unsatisfactory, for there are a numberf sof averages only one of which may be appropnate to use in describing given characteristics of a group.
Of the myyg that may be used, three have beeri selected as mojfjje1jil in educational research: the mean, the median, and the mo — • The Mean ().

The mean of a distribution is commonly understood as averge.

The term gi raiamiliar to students, the is a mean value.

It is computed by dividing the sum of all the scores by the number of scores.
In formula form: — N where = mean = sum of X = scores in a distribution N = number of scores 280 Part Three Data Analysis Example x 6 5 4 3 2 1 X = 21 N= 6 X=21/6= 3.50 The mean is probably the most usefLQLll statistical measures, for, in addition to the information that it provides, it is the base from which The Median (Md).

The median is a point (not necessarilyjore) in an arra above and below which one-half of the scores fall.

atj a rniüie ____pchoL rat ihan by calculation.


When there are an odd number of untied scores, the median is the middle score, as in the example below.
7 6 5 4—median 3 2 1 3 scores above 3 scores below When there are an even number of untied scores, the median is the p9i between the two middle scores as in the example below.. 6 5 4 3 scores above —median = 3.50 3 2 1 3 scores below Chapter 10 Descriptive Data Analysis 281 If the data include tied scores at the median point, interpolation within the tied scores will be necessary.
Each integer would be thought of as representing the interval from halfway between it and the next lower score to halfway between it and the next higher score.

When ties occur at the midpoint of a set of scores, we portion out this interval into the number of tied scores and find the midpoint or median.

Consider the set of scores in Figure 10—1.

Because there are four scores tied, we divide the interval from 74.5 to 75.5 into four equal parts.
Each of the scores is then considered to occupy 0.25 of the interval, and the median is calculated.

One purpose of the mean and the median is to represent the "typical" score; most of the time we are satisfied to use the mean for this purpose.

However, when the distribution of scores is such that most scores are at and relatively few are at the other (known as a skewed distribution, the median is refràbTe because it is not influenced by extreme scores at either end of the distribution.
In the following examples, the medians are identical.

However, the mean of Group A is 4 and the mean of Group B is 10.

The mean and median are both representative of Group A, but the median better represents the "typical" score of Group B.

Group A 7 6 5 4—Md 3 2 1 Group B 50 6 5 4—Md 3 2 0 .

• • Thusijkejyd data djjjjpns, the median isamore reali measure of central tendency than the mean.
In a small school with five faculty members, the salaries might be Teacher A B C D E $36,000 22,000 21,400 Md 21,000 19,600 $120,000 = 120,000 = 24,000 282 Part Three Data Analysis 70 73 74 80 0.25 0.25 0.25 0.25 74.50 4— lower limit 74.75 4....... median 75.00 75.25 75.50 4— upper limit FIGURE 10—1 Median Calculation.
The average salary of the group is represented with a different em- phasis by the median salary ($21,400) than by the mean salary ($24,000), which is substantially higher than that of four of the five faculty members.

Thus we see again that the ir edian is less sensitive than the mean to extreme values at either end of a distribution.

The Mode (Mo) 6 ]Mode The mode is the score that occurs most frequently in a distribution.
It is ed In grouped data distributions, the mode is assumed to be the midscore of the interval in which the greatest frequency occurs.

For example, if the modal age of fifth-grade children is ten years, it follows that there are more ten-year-old fifth-graders than any other age.

Or a menswear salesman might verify the fact that there are more sales of size 40 suits than of any other size; consequently, a larger number of size 40 suits are ordered and stocked, size 40 being the mode.
In some distributions there may be more than one mo.

A two- mode distribution isbimQd4l, more than two, multirnodal.

If the number Chapter 10 Descriptive Data Analysis 283 of auto accidents on the streets of a city were tabulated by hours of occurrence, it is likely that two modal periods would become apparent— between 7 and 8 A.M. and between 5 and 6 P.M., the hours when traffic to and from stores and offices is heaviest and when drivers are in the and it greatest hurry.
Jn.aj fails at the midpoint, just as the mean and median do.

In some unusual distributions, however, the mode may fail at some other point.

When the mode or rnodesy1 suchu sual behavior they do ni- uresofcentraltenden, but they do reveal usiiflformation about the nature of the distributioi.

Measures of Spread or Dispersion Measures of central tendency describe location along an ordered scale.

There are characteristics of data distributions that call for additional types of statistical analysis.
The scores in Table 10—4 were made by a group of students on two different tests, one in reading and one in arithmetic.

The mean and the median are identical for both tests.

It is apparent thatstudeptsjJt!

To contrast their performance, it is necessary to use a measure of score spread or dispersion The anthmetic test scores are homogenous, with little difference between adjacent scores.
TABLE 10—4 Sample Data Reading Pupil Arthur Betty John Katherine Charles Larry Donna Edward Mary Score 95 90 85 80 75 70 65 60 55 X=675 Md=75 Arithmetic Academic Grade Score —Aeademic Grade A A B B C C D D F 76 78 77 71 75 79 73 72 74 C C C C C C C C C X=675 Md—75 284 Part Three Data Analysis The reading test scores are decidedly heterogeneous, with performances ranging from superior to very poor.

The Range.

The range, the simplest measure of dispersion, is the dif- ference and lowest scores plus one.
For reading scores, the range is 41(95 — 55 + 1).

For arithmetic scores, the range is 9(79—71+1).

The Deviation from the Mean (x).

A score expressed as its distance from the mean is called a deviation score.

Its formula is x=(X—X) / If the score falls above the mean)the deviation score is positive (+), if it falls below the mean the deviation score is negative (—).
Using the same example, compare two sets of scores: Reading X (X-) +20 +15 +10 +5 0 —5 —10 —15 —20 • • 95 90 85 80 75 70 65 60 55- Arithmetic X ' (X-X) +1 +3 +2 —4 0 +4 —2 —3 —1 76 78 77 71 75 79 73 72 74 X = 675 x =0 N = 9 = 75 : X= 675 x =0 N= 9 x= 75 it is interesting to note that the sum of the score deviations fromjj.

jquals zero.

= 0 In fact, we can give an alternative definition of the mean: The mean is that value in a distribution around which the sum of the deviation score eauals zero.
Chapter 10 Descriptive Data Analysis 285 t aiance.

Wflv The Variance (2).

The sum of the squared deviations from the mean, that the sum of divided by N, is knot the deviations from the mean equals zero ( x = 0).

From a mathematical point of view it would be impossible to find a mean value to describe these deviations (unless the signs were ignored).

Squaring each deviation score yields a positive score.

They can then be summed, divided by N, and the mean of the squared deviations computed.
The variance formula or Thus the variance is a value that describes hy all of the scores in a disthbution are dispersed or spead about the mean This value is"iei eful inescnj,in,gtjcharactenstics ota 4istruticand will be em- ployed in a number of very important statistical tests.

However, since all of the deviations from the mean have been squared to find the variance, ills niiich too large to represent the spread of scores The Standard Deviation (a).
The stundarddeviation, the square root of the variance, is most frequently used as a measure of spread or dispersion of scores in a distribution.

The formula for standard deviation is - _____ N or In the following example using the reading scores from Table 10—4, the variance and the standard deviation are computed.

x 95 90 85 80 75 70 65 60 55 .


x +20 +15 +10 +5 0 —5 —10 —15 —20 x2 +400 +225 +100 +25 0 +25 +100 +225 +400 x2=1500 Variance a2 = 1500/9 = 166.67 Standard deviation a = /i500/9 = V166.67 = 12.91 286 Part Three Data Analysis As can clearly be seen, a variance of 166.67 cannq1iresent, for most cml 41 ut the standard purposes, a spread of scores itltatota1 deviation of 12.91 does make sense.
Although the deviation approach (just used in the previous calcu- lation) provides a dear example of the meaning of variance and standard deviation, in actual adice the deviation: method can be awkward to use in computi jA less coth1ké t&,di whffresiiT in the same answer, uses thejji stead of the deviation scores.

The number valueF tend to be large, but the use of a calculator facilitates the computation.
tflevariaes or LVa1n a = N X2— X)27 Standard deviation o• = INX2—(X)2 The following example demonstrates the process of computation, using the raw score method: x 95 90 85 80 75 70 65 60 — X=675 N=9 x2 (72_ — 9(52,125) — (675)2 — 469,125 — 81 — 9(9) x2 9025 8100 7225 6400 5625 4900 4225 3600 3025 =52,125 455,624 02 = 1300 = 166.67 a = /166.67 12.91 The standard deviation is a very useful device for comparing char- acteristics that may be quite different or may be expreisédTih different Chapter 10 Descriptive Data Analysis 287 units of measurement.
The discussion that follows shows that when the normality of distributions can be assumed, it is possible to compare the proverbial apples and branges.

The standard deviation is independent of jjagnitude of the mean and provides a common unit of measurement To use a rather farfefched example, imagine a man whose height is one standard deviation below the mean and whose weight is one standard deviation above the mean.
Because we assume that there is a normal relationship between height and weight (or that both characteristics are normally distributed>, we have a picture of a short, overweight individual.

cent of the pop- His height, expressed in inches, is in the lowest i4 ulation, and his weight, expressed in pounds, is in the higbUá.cent This concept is developed later, but before we ikiiss ulng the standard deviation to describe status or position in a gro we need to examine the normal distribution.
Normal Distribution The earliest mathematical analysis of the theory of probability dates to the eighteenth century.

Abraham DeMoivre, a French mathematician, discov- ered that a mathematical relationship explained the probabilities associated with various games of chance.

He developed the equation and the graphic pattern that describes it.
During the nineteenth century, a French astron- omer, LaPlace, and a German mathematidan, Gauss, independently ar- rived at the same principle and applied it more broadly to areas of mea- surement in the physical sciences.

From the limited applications made by these early mathematicians and astronomers, the theory of probability, or the curve of distribution of error, has been applied to data gathered in the areas of biology, psychology, sociology, and other sciences.
The theory describes the fluctuations of chance errors of observation and measure- ment.

(it is necessary to understand the theory of probability and the nature of the curve of normal distribution in order to comprehend many impor- tant statistical concepts, particularly in the area of standard scores, the theory of sampling, and inferential statistics.

The law of probability and the normal curve that illustrates it are based upon the law of chance or the probable occurrence of certain events.
When any body of observations conforms to this mathematical form, it can be represented by a bell-shaped curve with definite characteristics (see Figure 10—2).

1.

The curve is symmetrical around its vertical axis.

2.

The terms cluster around the center (the median).

3.

The mean, median, and the mode of the distribution have the same value.

288 Part Three Data Analysis FIGURE 10—2 The Normal Curve.

4.
4.

The curve has no boundaries in either direction, for the curve never touches the base line, no matter how far it is extended.

The curve is a curve of probability, not of certainty.

The operation of chance prevails in the tossing of coins or dice.

It is believed that many human characteristics respond to the influence of chance.
For example, if certain limits of age, race, and gender were kept constant, such measures as height, weight, intelligence, and longevity would approximate the normal distribution pattern.

But the normal dis- tribution does not appear in data based upon observations of samples.

There just are not enough observations.

The normal distribution is based upon an infinite number of observations beyond the capability of any observer; thus there is usually some observed deviation from the sym- metrical pattern.
But for purposes of statistical analysis, it is assumed that many characteristics do conform to this mathematical form within certain limits, providing a convenient reference.

The concept of measured intelligence is based upon the assumption that intelligence is normally distributed throughout limited segments of the population.

Tests are so constructed (standardized) that scores are normally distributed in the large group that is used for the determination of norms or standards.
Insurance companies determine their premium rates by the application of the curve of probability.

Basing their expectation on observations of past experience, they can estimate the probabilities of survival of a man from age 45 to 46.

They do not purport to predict the survival of a particular individual, but from a large group they can predict the mortality rate of all insured risks.

The total area under the normal curve may be considered to approach 100 percent probability.
Interpreted in terms of standard deviations, areas betiveen the mean and the various standard deviations from the mean under the curve show these percentage relationships (Figure 10—3).

Chapter 10 Descriptive Data Analysis 289 Note the graphic conformation of the characteristics of the normal curve: 1.

It is symmetrical—the percentage of frequendes is the same for equal intervals below or above the mean.

2.
2.

The terms or scores "cluster" or "crowd around the mean"—note how the percentages in a given standard deviation are greatest around the mean aM decTease as one moves away from the mean.

Xto ±1.OOz ±1.00 to ±2.OOz ±2.00 to ±3.OOz 34.13 percent 13.59 percent 2.15 percent 3.

The curve is highest at the mean—the mean, median, and mode have the same value.

4.
4.

The curve has no boundaries—a small fraction of 1 percent of the space fails outside of ± 3.00 standard deviations from the mean The normal curve is a curve that also describes probabilities.
For example, if height is normally distributed for a given segment of the population, the chances are that a person selected at random will be between the mean and one standard deviation above the mean in height, and 34.13 100 that the person selected will be between the mean and one standard deviation below the mean in height—or that the FIGURE 10—3 Percentage of Frequencies in a Normal Distribution Falling within a Range of a Given Number of Standard Deviations from the Mean.
290 Part Three Data Analysis selected person will be within one standard deviation (above or below) the mean in height.

Another interpretation is that 68.26 percent of this population segment will be between the mean and one standard deviation above or below the mean in height.

An example may help the reader understand this concept.

IQ (in- telligence quotient) is assumed to be normally distributed.
The Wechsler Intelligence Scale for Children—Revised (WISC-R) has a meanof 100 and a standard deviation of 15.

Thus, a WISC-R IQ score that is one standard deviation above the mean is 115, and a score of 85 is one standard deviation below the mean.

From this information we know that approximately 68 percent of the population should have WISC-R scores between 85 and 115.

For practical purposes the curve is usually extended to ±3 standard deviations from the mean (± 3z).
Most events or occurrences (or proba- bilities) will fall between these limits.

The probability is —j-- that these limits account for observed .or predicted occurrences.

This statement does not suggest that events or measures could not fall more than three stand- ard deviations from the mean but that the likelihood would be too small to consider when making predictions or estimates based upon probability.
Statisticians deal with probabilities, not certainty, and there is always a degree of reservation in making any prediction.

Statisticiansdeal with the probabilities that cover the normal course of events, not the events that are outside the normal range of experience.
99.74 Nonnormal Distributions As mentioned earlier in our discussions of parametric and nonparametric data and the relative usefulness of the mean and median, not all distri- butions, particularly of sample data, are identical to or even close to a normal curve.

There are two other types of distribUtions that can occur: skewed and bimodal.

With skewed distributions, the majority of scores are near the high or low end of the range, with relatively few scores at the other end.
The distribution is considered skewed in the direction of the tail (fewest scores).

In Figure 10—4, distribution A. is skewed positively and distribution B is skewed negatively.

Skewed distributions can be caused by a number of factors, including a test that is too easy or hard, or an atypical sample (very bright or very low intelligence).

Bimodal distributions have two modes (see distribution C in Figure 10—4) rather than the single mode of normal or skewed distributions.
This often results from a sample that consists of persons from two populations.

For instance, the height of American adults would be bimodally distrib- uted, females clustering around a mode of about 5 feet 4 inches, and males around a mode of about 5 feet 10 inches.

291 Chapter 10 Descnptive Data Analysis A BcFIGURE 10—4 Nonnormal Distributions Interpreting the Normal Probability Distribution When scores are normally or near normally distributed, a normal proba- bility table is useful.
The values presented in the normal probability table in Appendix B are critical because they provide data for normal distri- butions that may be interpreted in the following ways: 1.

The percentage of total space included between the mean and a given sigma distance (z) from the mean 2.

The percentage of cases, or the number when N is known, that fall between the mean and a given sigma distance (z) from the mean 3.
The probability that an event will occur between the mean an4 a given sigma distance (z) from the mean z = number of standard deviations from the mean x- 0 Figure 10—5 demonstrates how the area under the normal curve can be divided.

In a normal distribution the following characteristics hold true: 1.

The space included between the mean and + 1.OOz is .3413 of the total area under the curve.

2.

The percentage of cases that fall between the mean and +1.OOz is .3413.

3.
3.

The probability of an event's occurring (observation) between the mean and + 1.OOz is .3413.

4.

The distribution is divided into two equal parts; one half above the mean and the other half below the mean.

5.

Because one half of the curve is above the mean and .3413 of the total area is between the mean and + 1.OOz, the area of the curve that is above +1.OOz is .1587.
292 Part Three Data Analysis Because the normal probability curve is symmetrical, the shape of the right side (above the mean) is identical to the shape of the left side (below the mean).

As the values for each side of the curve are identical, only one set of values is presented in the probabffity table, expressed to one-hundredth of a sigma (standard deviation) unit.

The normal probability table in Appendix B provides the proportion of the curve that is between the mean and a given sigma (z) value.
The remainder of that half of the curve is beyond the sigma value.

Above the mean Below the mean Above +1.96z Below + .32z Below — .32z .5000 .5000 .5000 — .4750 = .0250 .5000 + .1255 .6255 .5000 — .1255 = .3745 Probability 50/100 50/100 2.5/100 .62.5/100 37.5/100 Practical Applications of the Normal Curve In the field of educational research the normal curve has a number of practical applications: 1.

To calculate the percentile rank of scores in a normal distribution.

2.
2.

To normalize a frequency distribution, an important process in stan- dardizing a psychological test or inventory.

3.

To test the significance of observed measures in experiments, relating fluctuations or errors that are inherent in the them to the process of slI and geiia&)ti pifulations fromwhich the samples are drawn.

FIGURE 10-5 The Space Included under the Normal Curve between the Mean and ±1 .OOz.
Chapter 10 Descriptive Data Ana'ysis Measures of Relative Position: Standard Scores Standard scores provide a method of expressing any scorein a distribution in terms of its distance from the mean in standard deviation units.

The utility of this conversion of a raw score to a standard score will become clear as each type is introduced and illustrated.

Three types of standard scores are considered.

1.

Sigma score (z) 2.

T score (T) 3.


College board score (ZCb) Remember that the distribution is assumed to benormal when using any type of standard score.

The Sigma Score (z).

In describing a score in a distribution, its deviation from the mean—expressed in standard deviation units—is often more meaningful than the score itself.

The unit of measurement is the standard deviation.


x-• or — (7 if where X = raw score X = mean = standard deviation x = (X — X) score deviation from the mean Example A X=76 3=82 (7= 4 Example B X=67=62 (7= 5.

76—82 —6 z— —1.50 z= 67 —62 5—-—= +1.00 The raw score of 76 in Example A may be expressed as a sigma score of —1.50, indicating that 76 is 1.5 standard deviations below the mean.
The 294 Part Three Data Analysis score of 67 in Example B may be expressed as a sigma score of + 1.00, indicating that 67 is one standard deviation above the mean.

In comparing or averaging scores on distributions where total points may differ, the use of raw scores may create a false impression of a basis for comparison.

A sigma score (z) makes possible a realistic comparison of scores and may provide a basis for equal weighting of the scores.
On the sigma scale, the mean of any distribution is converted to zero and the standard deviation is equal to 1.

For example, a teacher wishes to determine a student's equally weighted average (mean) achievement on an algebra test and on an Eng- lish test.
Subject Test Score Mean Highest Possible Score Standard Deviation Algebra English 40 84 47 110 60 180 5 20 It is apparent that the mean of the two raw test scores would not provide a valid summary of the student's performance, for the mean would be weighted overwhelmingly in favor of the English test score.

The conver- sion of each test score to a sigma score makes them equally weighted and comparable, for both test scores have been expressed on a scale with a mean of zero and a standard deviation of one.
x- (7 Algebra z score = 40—47 —7 = = —1.40 English z score = 84—110 —26 = 20 = —1.30 On an equally weighted basis, the performance of the student was fairly consistent: 1.40 standard deviations below the mean in algebra and 1.30 standard deviations below the mean in English.
Because the normal probability table describes the percentage of area lying between the mean and successive deviation units under the normal curve (see Appendix B), the use of sigma scores has many other useful applications to hypothesis testing, determination of percentile ranks, and probability judgments.

Chapter 10 Descriptive Data Analysis 295 The T Score (T) T=50+10 or 50+lOz Although the sigma score (z) is most frequently used,it is sometimes awkward to have negatives or scores with decimals.
Therefore, another version of a standard score, the T score, has been devised to avoid some confusion resulting from negative z scores (below the mean) and also to eliminate decimal values.

Multiplying the z score by 10 and adding 50 results in a scale of positive whole number values.

Using the scores in the previous example, T = 50 + lOz: Algebra T = 50 + 10(—1.40) = 50 + (—14) = 36 English T = 50 + i0(—1.30) = 50 + (—13) = 37 T scores are always rounded to the nearest whole number.
A sigma score of + 1.27 would be converted to a T score of 63.

T=50+10(+1.27)=50+(+12.70)=62.70=63 The College Board Score (Z).

The College Entrance Examination Board and several other testing agencies use another conversion that provides a more precise measure by spreading out the scale (see Figure 10—6).

ZCb = 500 + 100(X = 500 + lOOz The mean of this scale is 500.

The standard deviation is 100.

The range is 200—800.

Percentile Rank.


Often useful to describe a score in relation to other scores, the percentile rank is the point in the distribution below which a given percentage of scores fall.

If the eightieth percentile rank is a score of 65h.

80 percent of the scores fall below 65.

The median is the fiftieth percentile rank, for 50 percent of the scores fall below it.

When N is small, the definition needs an added refinement.
To be completely accurate, the percentile rank is the score in the distribution below which a given percentage of the scores fall, plus one half the percentage of space occupied by the given score.

296 Part Three Data Analysis FIGURE 10—6 A Comparison of Three Types of Standard Scores.

This point can be demonstrated by a rather extreme example.

Scores 50 47 43 39 30 Upon inspection it is apparent that 43 is the median, oroccupies the fiftieth percentile rank.
Fifty percent of the scores should fall below it, but in fact only two out of five scores fall below 43.

That would indicate 43 has a percentile rank of 40.
But by adding the phrase "plus one half the percentage of space occupied by the score," we reconcile the calcu- lation: 40% of scores fall below 43: each score occupies 20% of the total space 40% + 10% = 50 (true percentile rank) When N is large, this qualification is unimportant because percentile ranks are rounded to the nearest whole number, ranging from the highest percentile rank of 99 to the lowest of zero.

High schools frequently rate their graduating seniors in terms of rank in class.
Because schools vary so much in size, colleges find these rankings of limited value unless they are converted to some common basis for Chapter 10 Descriptive Data Analysis 297 comparison.

The percentile rank provides this basis by converting class rank into a percentile rank.

Percentile rank = 100 — (100RK — 50) N where RK = rank from the top.

Jones ranks twenty-seventh in his senior dass of 139 students.

Twenty- six students rank above him, 112 below him.
His percentile rank is 190 — (2 39 50) = 100 — 19 = 81 In this formula, 50 is subtracted from 100RK to account for half the space occupied by the individual's score.

Measures of Relationship Correlation.

Correlation is the relationship between two or more paired variables or two or more sets of data.

The degree of relationship is mea- sured and represented by the coefficient of correlation.
This coefficient may be identified by either the letter r, the Greek letter rho (p), or other symbols depending upon the data distributions and.

the way the coefficient has been calculated.

Students who have high intelligence quotients tend to receive high scores in mathematics tests, whereas those with low IQs tend to score low.

When this type of relationship is obtained, the factors of measured intelligence and scores on mathematics tests are said- to be positively correlated.
Sometimes variables are negatively correlated when a large.

amount of one variable is associated with a small amount of the other.

As one increases, the other tends to decrease.

When the relationship between two sets of variables is a pure-chance relationship, we say that there is no correlation.

These pairs of variables are usually positively correlated: As one increases the other tends to increase.

1.

Intelligence 2.

Productivity per acre 3.

Height 4.


Family income Academic achievement Value of farm land Shoe size Value of family home 298 Part Three Data Analysis These variables are usually negatively correlated: As one increases the other tends to decrease.

1.

Academic achievement 2.

Total corn production 3.

Time spent in practice 4.

Age of an automobile Hours per week of TV watching Price per bushel Number of typing errors Trade-in value There are other traits that probably have no correlation.

1.
1.

Body weight 2.

Shoe size Intelligence Monthly salary The degree of linear correlation can be represented quantitatively by the coefficient of correlation.

A perfect positive correlation is +1.00.

A perfect negative correlation is —1.00.

A complete lack of relationship is zero (0).

Rarely, if ever, are perfect coefficients of correlations of +1.00 or —1.00 encountered, particularly in relating human traits.
Although some relationships tend to appear fairly consistently, there are variations or exceptions that reduce the measured coefficient from either a —1.00 or a +1.00 toward zero.

A definition of perfect positive correlation specifies that for every unit increase in one variable there is a proportional unit increase in the other.

The perfect negative correlation specifies that for every unit increase in one variable there is a proportional unit decrease in the other.
That there can be no exceptions explains why coefficients of correlation of +1.00 or —1.00 are not encountered in relating human traits.

The sign of the coefficient indicates the direction of the relationship, and the numerical value its strength.

The Scattergram and Linear Regression Line.

Whenthe relationship be- tween two variables is plotted graphically, paired variable values are plot- ted against each other on the X and Y axis.
The line drawn through, or near, the coordinate points is known as the "line of best fit," or the regression line.

On this line the sum of the deviations of all the coordinate points has the smallest possible value.

As the coefficient approaches zero (0) the coordinate points fall further from the regression line (see Figure 10—7 for examples of different correlations' scattergrams).
When the coefficient of correlation is either +1.00 or —1.00, all of the coordinate points fall on the regression line, indicating that, when r = +1.00, for every increase in X there is a proportional increase in Y; and when r = - 1.00, for every increase in X there is a proportional decrease in Y.

There are no individual exceptions.

If we know a person's Chapter 10 Descriptive Data Analysis 299 FIGURE 10—7 Scatter Diagrams Illustrating Different Coefficients of Correlation.
score on one measure, we can determine his or her exact score on the other measure.

The slope of the regression line, or line of best fit, is not determined by guess or estimation but by a geometric process that will be described later.

There are actually two regression lines.

When r = +1.00 or —1.00, the lines are superimposed and appear as one line.

As r approaches zero, the lines separate further.


300 Part Three Data Analysis Only one of the regression lines is described in this discussion, the Y on X (or Y from X) line.

It is used to predict unknown Y values from known X values.

The X values are known as the predictor variable, and the Y values, the predicted variable.

The other regression line (not de- scribed here) would be used to predict X from Y. Plotting the Slope of the Regression Line.
The slope of the regression (Y from X) line is a geometric representation of the coefficient of correlation and is expressed as a ratio of the magnitude of the rise (if r is +) to the run, or as a ratio of the fail (if r is —) to the run, expressed in standard deviation units.

-r Run ______________ Run For example, if r = + .60, for every sigma unit increase (run) in X, there is a .60 sigma unit increase (rise) in Y.
+.6Oz 1.oozx If r = — .60, for every sigma unit increase (run) in X, there is a .60 sigma unit decrease (fall) in Y.

_.60zr!

1.oozx The geometric relationship between the two legs of the right triangle determines the slope of the hypotenuse, or the regression line.

Because all regression lines pass through the intersection of the mean of X and the mean of Y lines, nly one other point is necessary to deter- mine the slope.
By measuring one standard deviation of the X distribution on the X axis and a .60 standard deviation of the Y distribution on the Y axis, the second point is established (see Figures 10—8 and 10—9).

Chapter 10 Descriptive Data Analysis 301 FIGURE 10—8 A Positive Regression Line, r = + .60.

FIGURE 10—9 A Negative Regression Line, r = — .60.

The regression line (r) involves one awkward feature: all values must be expressed in sigma scores (z) or standard deviation units.
It would be more practical to use actual scores to determine the slope of the regression line.

This can be done by converting to a slope known as b.

The slope of the b regression line Y on X is determined by the formula: b = r crx Part Three Data Analysis For example, if r = +.60 and = 5 b= +.60=f.= +.72 Thus an r of + .60 becomes b = + .72.

Now the ratio of the rise to the run has another value and indicates a different slope of the regression line (Figure 10—10).
Pearson's Product-Moment CoeffIcient of Correlation (r) The most often used and most precise coefficient of correlation is known as the Pearson product-moment coefficient (r).

This coefficient may be calcu- lated by converting the raw scores to sigma scores and finding the mean value of their cross-products.
r= N z, +1.50 +2.00 —.75 +.20 —1.00 —.40 +1.40 +.55 —.04 —.10 z3, +1.20 +1.04 —.90 +.70 +.20 +.30 -I-.70 +.64 +.10 +.30 (z)(z) +1.80 +2.08 +.68 +.14 —.20 —.12 +.98 +.35 —.00 —.03 (z)(z) = 5.68 r = +68 = +.568 If most of the negative z values of X are associated with negative z values of Y, and positive z values of X with positive z values of Y, the Chapter 10 Desctlptive Data Analysis 303 1.oozx (Sigma scores) ] 1.oox (Raw scores) FIGURE 10—10 Two Regression Unes, r and b.
An r of + .60 Is Converted to a b of + .72 by the Formula b=r ax - correlation coefficient will be positive.

If most of the paired values are of opposite signs, the coefficient will be negative.

positive correlation (+)( +) = + high on X, high on Y = + low on X, low on Y negative correlation( +)( —) = — high on X, low on Y (— )( +) = — low on X, high on Y The z score method is not often used in actual computation because it involves the conversion of eac c re into a sigma score.
Two other methods, a deviation method and a raw score met od, are more conven- ient, more often used, and yield the same result.

The deviation method uses the following formula and requires the setting up of a table with seven columns.
where x2 = the sum of the (X-X)2 - subtracted from each X score squared = the sum of the Y subtracted from each Y score squared xy = the cross-products of the mean subtracted from that score (Y-Y) (X-X)(Y-Y) Using the data from Table 10—4, with reading scores being the X variable and arithmetic scores being the Y variable, we calculate r like this: 304 Part Three Data Analysis x 95 90 85 80 75 70 65 60 55 Y x 20 76 15 78 10 77 5 71 0 75 79 —5 73 —10 72 —15 74 —20 Variables x2 y 1 400 3 225 2 100 25 —4 0 0 4 25 100 —2 225 —3 400 —1 X=675=75 Y=675 V=75 x2=1500 y2 xy +20 +45 +20 —20 0 —20 +20 +45 +20 y2=60 xy=l30 1 9 4 16 0 16 4 9 1 T = 130 V(1500)(60) = 130 /90,000 130 = The raw score method requires the use of five columns, as illustrated below using the same data.
- NXY-(X)(Y) where X = sum of the X scores Y = sum of the Y scores X2 = sum of the squared X scores = sum of the squared Y scores XY = sum of the products of paired X and Y scores N = number of paired scores x 95 90 85 80 75 70 65 60 55 X = 675 Y 76 78 77 71 75 79 73 72 74 Y 675 Variables X2 9025 8100 7225 6400 5625 4900 4225 3600 3025 X2 = 52,125 xY 7220 7020 6545 5680 5625 5530 4745 4320 4070 XY = 50,755 5776 6084 5929 5041 5625 6241 5329 5184 5476 = 50,685 Chapter 10 Descripthie Data Analysis 305 — — — — 9(50,755) — (675)(675) V9(52, 125) — (675)2 /5O,685) — (675)2 456,795 — 455,625 \/469,125 — 455,625 V456165 — 455,625 1170 - \/i /ö — 1170 (116.19)(23.24) 1170 2700.26 Rank Order Correlation (p) A particular form of the Pearson product-moment correlation that can be used with ordinal data is known as the Spearman rank order coefficient of correlation.
The symbol p (rho) is used to represent this correlation coef- ficient.

The paired variables are expressed as ordinal values (ranked) rather than as interval or ratio values.

It lends itself to an interesting graphic demonstration.

In the following example, the students ranking highest in IQ rank highest in mathematics, and those lowest in IQ, lowest in mathematics.
Pupil IQ Rank Achievement in Mathematics Rank A B C D E Perfect positive coefficient of correlation p= +1.00 In the following example, the students ranking highest in time spent in practice rank lowest in number of errors.
306 Part Three Data Analysts Time Spent in Practice Rank Number of Typing Errors Rank Pupil Perfect negative coefficient of correlation p = —1.00 In the following example, there is probably little more than a pure chance relationship (due to sampling error) between height and inteffi- gence.
Pupil Height Rank IQ RankII Very low coefficient of correlation p = +.10 To compute the Spearman rank order coefficient of correlation, this rather simple formula is used: — — 1 — 6D2 N(N2 — 1) where D = the difference between paired ranks = the sum of the squared differences between ranks I N = number of paired ranks If we converted the previously used data to ranks and calculated Spearman's p, it would look like this: Chapter 10 Descriptive Data Analysis 307 Pupil Arthur Betty John Katherine Charles Larry Donna Edward Mary Rank in Reading Rank in Arithmetic D —3 0 0 —5 0 5 0 0 3 IY 9 0 0 25 0 25 0 0 9 D2=68 — — = 1 — 6(68) 9(81 — .1) = 1 — —1— — 408 9(80) .567 = +.433 As we have just demonstrated, Spearman's p and Pearson's r yielded the same result.
This is the case when there are no ties.

When there are ties, the results will not be identical, but the difference wifi be insignificant.

The Spearman rank order coefficient of correlation computation is quick and easy.

It is an acceptable method if data are available only in ordinal form.

Teachers may find this computation method useful when conducting studies using a single class of students as subjects.
Phi Correlation Coefficient (4)) The data are considered dichotomous when there are only two choices for scoring a variable (e.g., pass—fail or female—male).

In these cases, each person's score usually would be represented by a 0 or 1, although some- times 1 and 2 are used instead.

The Pearson product-moment correlation, when both variables are dichotomous, is known as the phi (4)) coefficient.

identical.
identical.

Because we rarely have two dichotomous variables of interest of which we want to know the relationship, we will not present the formula here.

This brief mention of 4) is to make the reader aware of it.

Those wishing more detail should refer to one of the many statistics texts available (e.g., Ferguson, 1981; Glass & Hopkins, 1984).

308 Part Three Data Analysis Interpretation of a Correlation Coefficient Two circumstances can cause a higher or lower correlation than usual.
First, when one person or relatively few people have a pair of scores that differ markedly from the rest of the sample's scores, the resulting r may be spuriously high.

When this occurs, the researcher needs to decide whether to remove this individual's pair of scores (known as an outlier) from the data analyzed.

Second, when all other things are equal, the more homogeneous a group of scores, the lower their correlation will be.

That is, the smaller the range of scores, the smaller r will be.
Researchers need to consider this potential problem when selecting samples that may be highly homogeneous.

However, if the researcher knows the standard deviation of the heterogeneous group from which the homogeneous group was selected, Glass and Hopkins (1984) and others describe a formula that corrects for the restricted range and provides the correlation for the het- erogeneous group.
There are a number of ways to interpret a correlation coefficient or adjusted correlation coefficient depending upon the researcher's purpose and the circumstances that may influence the correlation's magnitude.
One method that is frequently presented is to use a crude criterion for evaluating the magnitude of a correlation: Coefficient (r) Relationship .00 to .20 .20 to .40 .40 to .60 .60 to .80 .80 to 1.00 Negligible Low Moderate Substantial High to very high Another interpretative approach is a test of statistical significance of the correlation, based upon the concepts of sampling error and tests of significance described in Chapter 11.
Another way of interpreting a correlation coefficient is in terms of variance.

The variance of the measure that we want to predict can be divided into the part that is explained by, or due to, the predictor variable and the part that is explained by other factors (generally unknown) in- cluding sampling error.

We find this percentage of explained variance by calculating r2, known as the coefficient of determination.

The percentage of variance not explained by the predictor variable is then 1 — r2.
An example may help the reader understand this important concept.

In combining studies using IQ to predict general academic achievement, Walberg (1984) found the overall correlation between these variables to be .71.

We can use this correlation to find r2 = .50.

This means that 50 Capter '10 Descrptve Data Analysis 309 percent of the variance in academic achievemeflt (bow w11 ot pooT\y dIfferent students do) is predictable from the variance of IQ.
This also obviously means that 50 percent of the variance of academic achievement is due to factors other than IQ, such as motivation, home environment, school attended, and test error.

Walberg also found that the correlation of IQ with science achievement was .48.

This means that only 23 percent (r2) of variance in science achievement is predictable by IQ and that 77 percent is due to other factors, some known and some unknown.
There are additional techniques, too advanced for this introductory text, that allow researchers to use more than one variable.

Thus it is possible, for example, to use a combination of IQ, self-concept scores, a measure of motivation, and a socioeconomic scale to predict academic achievement.

This multiple correlation would increase the correlation, which would in turn increase the percent of variance of academic achieve- ment that is explained by known factors.
Misinterpretation of the Coefficient of Correlation Several fallacies and limitations should be considered in interpreting the meaning of a coefficient of correlation.

The coefficient does not imply a cause-and-effect relationship between variables.

High positive correlations have been observed between the number of storks' nests and the number of human births in northwestern Europe, and between the number of ordinations of ministers in the New England colonies and the consumption of gallons of rum.
These high correlations obviously do not imply caus- ality.

As population increases, both good and bad things are likely to increase in frequency.

Similarly, zero (Or even negative) correlation does not necessarily mean that no causation is possible.

Glass and Hopkins (1984) point out, "some studies with college students have found no correlation between hours of study for an examination and test performance.

.

.

.
.

.

.

EThis is likely due to the fact thatj some bright students study little and still achieve average scores, whereas some of their less gifted classmates study dili- gently but still achieve an average performance.

A controlled experimental study would almost certainly show some causal relationship" (p. 106).

Prediction An important use of the coefficient of correlation and the Y on X regression line is for prediction of unknown Y values from known X values.
Because it is a method for estimating future performance of individuals on the basis of past performance of a sample, prediction is an inferential appli- cation of correlational analysis.

It has been included in this chapter to illustrate one of the most useful applications of correlation.

310 Part Three Data Analysis Let us assume that a college's admissions officers wish to predict the likely academic performance of students considered for admission or for scholarship grants.
They have built up a body of data based upon the past records of a substantial number of admitted college students over a period of several years.

They have calculated the coefficient of correlation between their high school grade-point averages and their college freshman grade-point averages.

They can now construct a regression line and predict the future college freshman GPA for any prospective student, based upon his or her high school CPA.
Let us assume that the admissions officers found the coefficient of correlation to be + .52.

The slope of the line could be used to determine any Y values for any X value.

This process would be quite inconvenient, however, for all grade-point averages would have to be entered as sigma (z) values.

A more practicable procedue would be to construct a regression line with a slope of b so that any college grade-point average (Y) could be predicted directly from any high school grade-point average.
The b regres- sion line and a carefully drawn graph would provide a quick method for prediction.

For example: - If r = + .52, then b = r S=.50 b=+.52 b=+.43 XA is student A's high school CPA, YAhis predicted college CPA.

XB is student B's high school GPA, YB her predicted college CPA.

Figure 10—11 uses these data to predict college CPA from high school CPA.

Another, and perhaps more accurate, alternative for predicting un- known Is from known Xs is to use the regression equation rather than the graph.
The formula for predicting 'I from X is: =a+bX where = the predicted score (e.g., college freshman CPA) X = the predictor score (e.g., high school GPA) b = slope a = constant, or Y intercept We have already seen that b = We can find a by a = V — b.

Chapter 10 Descriptive Data Analysis 311 FIGURE 10—11 A Regression Line Used to Predict College Freshman GPA from High School GPA.

Given the following data, we can then find the most likely freshman GPA for two students.
b = .43 (found earlier) X = 2.10 Y = 2.40 a = 2.40 — 2.10(.43) = 2.40 — .90 = 1.50 X (student A's high school GPA) = 2.00 Xb (student B's high school GI'A) = 3.10 = 1.50 + .43(Xa) = 1.50 + .43(2.00) = 1.50 + .86 = 2.36 = 1.50 + .43(Xb) = 1.50 + .43(3.10) = 1.50 + 1.33 =2.83 312 Part Three Data Analysis For student A, whose high school GPA was below the mean, the predicted college GPA was also below the mean.
For student B, whose high school GPA was well above the mean, the predicted GPA wassub- stañtially above the mean.

These results are consistent with a positive coefficient of correlation in general: high in X, high in Y; low in X, low in Y.

Standard Error of Estimate When the coefficient of correlation based upon a sufficient body of data has been determined as ± 1.00, there wifi be no error of prediction.
Perfect correlation indicates that for every increase in X, there is a proportional increase (when +) or proportional decrease (when —) in Y.

There are no exceptions.

But when the magnitude of r is less than + 1.00 or —1.00, error of prediction is inherent because there have been exceptions to a consistent, orderly relationship.

The regression line does not coincide or pass through all of the coordinate values used in determining the slope.
A measure for estimating this prediction error is known as the stand- ard error of estimate (Sest).

Sesty = Sy Vii — As the coefficient of correlation increases, the prediction error de- creases.

When r = ±1.00 Sesty = S Vi — r2 = S Vf (1)2 = S(0) = 0 When r = 0 — C C "esty — J' V L /iirn2 — C a1\ — C U) — .Jy,1J — When r = 0 (or when the coefficient of correlation is unknown), the best blind prediction of any Y from any X is the mean of Y.
This is true because we know that most of the scores in a normal distribution cluster around the mean and that about 68 percent of them would probably fail within one standard deviation from the mean.

In this situation the stand- ard deviation of Y may be thought of as the standard error of estimate.

When r = 0, 5estr = S.1.

If the coefficient of correlation is more than zero, this blind prediction can be improved upon in these ways: 1.
By plotting Y from a particular X from the regression line (see Figure 10—12) Chapter 10 Descriptive Data Analysis 313 2.

By reducing the error of prediction of Y by calculating how much S is reduced by the coefficient of correlation For example, when r = ± .60 Sesty = S/1 — (r)2 = Sv'1 — (.60)2 = S-v'1 — .36 = S v' = .8OS Thus the estimate error of Y has been reduced from S to .80S.

Interpretation of the standard error of estimate is similar to the interpre- tation of the standard deviation.
If r = ± .60S, the standard error of estimate of Y wifi be .80S.

An actual performance score of Y would probably fall within a band of ± .80S from the predicted Y in about 68 of 100 predictions.

In other words, the probability is that the predicted score would not be more than one standard error of estimate from the actual score in about 68 percent of the predictions.
In addition to the applications described, the coefficient of correlation is indispensable to psychologists who construct and standardize psycho- logical tests and inventories.

A few of the basic procedures are briefly described.

Computing the coefficient of correlation is the usual procedure used to evaluate the degree of validity and reliability of psychological tests and FIGURE 10—12 A Predicted V Score from a Given X Score, Showing the Standard Error of Estimate.
314 Part Three Data Analysis inventories (see Chapter 9 for a more detailed description of these con- cepts).

The Coefficient of Validity.

A test is said to be valid to the degree that it measures what it claims to measure, or, in the case of predictive validity, to the extent that it predicts accurately such types of behavior as academic success or failure, job success or failure, or stability or instability under stress.
Tests are usually validated by correlating test scores against some outside criteria, which may be scores on tests of accepted validity, suc- cessful performance or behavior, or the expert judgment of recognized authorities.

The Coefficient of Reliability.

A test is said to be reliable to the degree that it measures accurately and consistently, yielding comparable results when administered a number of times.

There are a number of ways of using the process of correlation to evaluate reliability: 1.
Test—retest—correlating the scores on two or more successive administrations of the test (administration number 1 versus admin- istration number 2) 2.

Equivalent forms—correlating the scores when groups of individuals take equivalent forms of the test (form L versus form N) 3.

Split halves—correlating the scores on the odd items of the test (numbers 1, 3, 5, 7, etc.)

against the even items (numbers 2; 4, 6, 8, etc.).


This method yields lower correlations because of the reduction in size to two tests of half the number of items.

This may be corrected by the application of the Spearman—Brown prophecy formula.

2r r= 1+r Ifr=±.60, 1.20 =+.75 1 + .60 A Note of Caution Statistics is an important tool of the research worker, and an understand- ing of statistical terminology, methodology, and logic is important for the consumer of research.
A number of limitations, however, should be rec- Chapter 10 Descriptive Data Analysis 315 ognized in using statistical processes and in drawing conclusions from statistical evidence.

1.

Statistical process, a servant of logic, has value only if it verifies, clarifies, and measures relationships that have been established by clear, logical analysis.

Statistics is a means, never an end,of research.

2.
2.

A statistical process should not bE employed in the analysis of data unless it adds clarity or meaning to the analysis of data.

It should not be used as wmdow dressing to impress the reader.

3.

The conclusions derived from statistical analysis will be no more accurate or valid than the original data.

To use an analogy, no matter how elaborate the mixer, a cake made of poor ingredients will be a poor cake.
All the refinement of elaborate statistical manipulation will not yield significant truths if the data result from crude or inexact measurement.

In computer terminology, this is known as Cl—GO, "garbage in—garbage out."

4.

All treatment of data must be checked and double-checked frequently to minimize the likelthood of errors in measurement, recording, tab- ulation, and analysis.

5.

There is a constant margin of error wherever measurementof human beings is involved.
The error is increased when qualities orcharac- teristics of human personality are subjected to measurement or when inferences about the population are made from measurements de- rived from statistical samples.

When comparisons or contrasts are made, a mere number differ- ence is not in itself a valid basis for any conclusion.

A testof statistical significance should be employed to weigh the possibility that chance in sample selection could have yielded the apparent difference.
To apply these measures of statistical significance is to remove someof the doubt from the condusions.

6.

Statisticians and liars are often equated in humorous quips.

There is little doubt that statistical processes can be used to prove nearly anything that one sets out to prove if the procedures used are in- appropriate.

Starting with false assumptions, using inappropriate procedures, or omitting relevant data, the biased investigator can arrive at false conclusions.
These conclusions are often particularly dangerous because of the authenticity that the statistical treatment seems to confer.

Of course, intentionally using inappropriate pro- cedures or omitting relevant data constitutes unethical behavior and is quite rare.

Distortion may be deliberate or unintentional.

In research, omitting certain facts or choosing only those facts favorable to one's position is as culpable as actual distortion, which has no place in research.
The reader must always try to evaluate the manipulation of data, particularly when the report seems to be persuasive.

316 Part Three Data Analysis Summary This chapter deals with only the most elementary descriptive statistical concepts.

For a more complete treatment the reader is urged to consult one or more of the references Hated.
Statistical analysis is the mathematical process of gathering, organizing, ana- lyzing, and interpreting numerical data, and is one of thébásic phases of the research process.

Descriptive statistical analysis involves the description of a particular group.

Inferential statistical analysis leads to judgments about the whole population, to which the sample at hand is presumed to be related.

Data are often organized in arrays in ascending or descending numerical order.
Data are often grouped into class intervals so that analysis is simplified and char- acteristics more readily noted.

Measures of central tendency (mean, median, and mode) describe data in terms of some sort of average.

Measures of position, spread, or dispersion describe data in terms of relationship to a point of central tendency.

The range, deviation, variances, standard deviation, percentUe, and sigma score are useful measures of position, spread, or dispersion.
Measures of relationship describe the relationship of paired variables, quantified by a coefficient of correlation.

The coefficient is useful in educational research in standardizing tests and in making predictions when only some of the data are avail- able.

Note that a high coefficient does not imply a cause-and-effect relationship but merely quantifies a relationship that has been logically established prior to its meas- urement.
Statistics is the servant, not the master, of logic; it is a means rather than an end of research.

Unless basic assumptions are valid, unless the right data are carefully gathered, recorded, and tabulated, and unless the analysis and interpretations are logical, statistics can make no contribution to the search for truth.

Exercises (Answers in Appendix H) 1.

More than half the families in a community can have an annual income that is lower than the mean income for that community.
Do you agree or disagree?

Why?

2.

The median is the midpoint between the highest and the lowest scores in a distribution.

Do you agree or disagree?

Why?

3.

Compute the mean and the median of this distribution: 74 72 70 65 63 61 56 Chapter 10 Descriptive Data Analysis 317 51 42 40 37 33 4.

Determine the mean, the median, and the range of this distribution: 88 86 85 80 80 77 75 71 65 60 58 5.
Compute the variance (ok) and the standard deviation (a) for this set of scores: 27 27 25 24 20 18 16 16 14 12 10 7 6.

The distribution with the larger range is the distribution with the larger standard deviation.

Do you agree or disagree?

Why?

7.

If five points were added to each score in a distribution, how wouldthis change each of the following: a. the range b. the mean c. the median d.. the mode e. the variance f. the standard deviation 318 Part Three Data Analysis 8.
Joan Brown ranked twenty-seventh in a graduating class of 367.

What was her percentile rank?

9.

In a coin-tossing experiment where N = 144 and P (probability) = .50, draw the curve depicting the distribution of probable outcomes of heads appearing for an infinite number of repetitions of this experiment.

Indicate the number of heads for the mean, and at 1,2, and 3 standard deviations from the mean, both positive and negative.

10.
10.

Assuming the distribution to be normal with a mean of 61 and a standard deviation of 5, calculate the following standard score equivalents: x z T X 66 58 70 61 52 11.

Using the normal probability table in Appendix B, calculate the following values: a. below —1 .25z b. above —1.25z c. between —1.40z and +1.67z d. between +1 .50z and +2.50z e. 65th percentile rank f. 43rd percentile rank g. top 1%of scores h. middle 50% of scores i. not included between —1 .OOz and +1 .OOz j.
50th percentile rank _____ % _____ % ____ % _____ % _____ z _____ z ____ z _____ z to _____ z _____ % _____ z 12.

Assuming a normal distribution of scores, a test has a mean score of 100 and a standard deviation of 15.

Compute the following scores: a. score that cuts off the top 10% b. score that cuts off the lower 40% c. percentage of scores above 90 d. score that occupies the 68th percentile rank ______ e. score limits of the middle 68% _____ _____ _____ % _____ to _____ 13.
Consider the following table showing the performance of three students in algebra and history: Mean Algebra History 90 20 a 30 4 Tom Donna Harry 60 25 100 22 85 19 Chapter 10 Descriptive Data Analysis 319 Who had: a. the poorest score on either test?

b. the best score on either test?

c. the most consistent scores on both tests?

d. the least consistent scores on both tests?

e. the best mean score on both tests?

f. the poorest mean score on both tests?

14.


The coefficient of correlation measures the magnitude of the cause-and- effect relationship between paired variables.

Do you agree or disagree?

Why?

15.

Using the Spearman rank order coefficient of correlation method, com- pute p. Mary Peter Paul Helen Ruth Edward John X Variable Y Variable 16.

Two sets of paired variables are expressed in sigma (z) scores.

Compute the coefficient of correlation between them.
zx +.70 —.20 +1.50 +1.33 —.88 +.32 —1.00 +.67 —.30 +1.25 +.50 zy +.55 —.32 +2.00 +1.20 —1.06 —.40 +.50 +.80 —.10 +1.10 —.20 320 Part Three Data Analysis 17.

Using the Pearson product-moment raw score method, compute the coefficient of correlation between these paired variables: X2 Y2 XY • x 66 50 43 8 12 35 24 20 16 54 Y 42 55 60 24 30 18 48 35 22 38 18.

A class took a statistics test.

The students completed all of the questions.


The coefficient of correlation between the number of correct and the number of incorrect responses for the class was 19.

There is a significant difference between the slope of the regression line r and that of the regression line b.

Do you agree?

Why?

20.

Compute the standard error of estimate of V from X when: Si,.

= 6.20 r=+.60 21.
= 6.20 r=+.60 21.

Given the following information, predict the V score from the given X, when X = 90, and: a. r = +.60 X=80 S=12 V=40 S= 8 b. r = — .60 Ref erences FERGUSON, G. A.

(1981).

Statistical analysis in psychology and education (5th ed.).

New York: McGraw-Hill.

GLASs, C. V. & HoPKINs, K. D. (1984).

Statistical methods in education and psychology (2nd ed.).

Englewood Cliffs, NJ: Prentice-Hall.

GLASs, G. V., PEcKHAM, P. D., & SANDERS, J. R. (1972).
Consequences of failure to meet assumptions underlying the fixed effects analysis of variance and covariance.

Review of Educational Research, 42, 237—288.

Chapter 10 Descriptive Data Analysis 321 GUILFORD, J. P. & FRUCHTER, B.

(1978).

Fundamental statistics in psychology and ed- ucation (6th ed.).

New York: McGraw-Hill.

HAYS, W. L. (1981).

Statistics (3rd ed.).

New York: Holt, Rinehart & Winston.

Kiix, R. E. (1982).

Experimental design: Procedures for the behavioral sciences (2nd ed.).
Belmont, CA: Brooks/Cole.

LUNNEY, G. H. (1970).

Using analysis of variance with a dichotomous dependent variable: An empirical study.

Journal of Educational Measurement, 7, 263—269.

MANDEVILLE, C. K. (1972).

A new look at treatment differences.

American Educa- tional Research Journal, 9, 311—321.

SIEGEL, S. (1956).

Non paramet nc statistics for the behavioral sciences.

New York: McGraw- Hill.

WALBERG, H. J.

(1984).

Improving the productivity of America's schools.

Educational WINER, B. J.


(1971).

Statistical principles in experimental design (2nd ed.

).New York: Leadership, 41, 19—30.

McGraw-Hill.

•11 Inferential Data Analysis IN CHAFFER 1 WE DESCRIBED the ultimate purpose of research as the dis- covery of general principles based upon observed relationships between variables.

If it were necessary to observe all of the individuals in the population about which one wished to generalize, the process would be never-ending and prohibitively expensive.
The practical solution is to select samples that are representative of the population of interest; then, through observations and analysis of the sample data, the researcher may infer characteristics of the population.

(The reader may wish to refer to the discussion of types of samples and sampling procedures presented in Chapter 1.)

I.
I.

Statistical Inference Many laypersons share the misconception that an adequate sample must be a miniature carbon copy, or have the identical characteristics, of the population under study.

If a large number of researchers selected random samples of 100 teachers from the population of all teachers in California, the mean weight of the samples would not be identical.

A few would be relatively high, a few relatively low, but most would tend to cluster around the population mean.
This variation of sample means is due to what is known as sampling error.

The term does not suggest any fault or mistake in the sampling process but merely describes the cI'ance variations that are inevitable when a number of randomly selectl sample means are computed.

Estimating or inferring a population characteristic (parameter) from a random sample (statistic) is not an exact process.
It has been noted that 323 324 Part Three Data Analysis successive means of randomly selected samples from the same population are not identical.

Thus, if these means are not identical, it would be logical to assume that any one of them probably differs from the population mean.

This would seem to present an insurmountable obstacle to statis- ticians, for they have only a sample to use as a basis for generalizations about a population.
Fortunately, an advantage of random selection is that the sample statistic will be an unbiased estimate of the population param- eter.

Because the nature of the variations of random sample means is known, it is possible to estimate the degree or variation of sample means on a probability basis.

The Central Limit Theorem An important principle, known as the central limit theorem, describes the characteristics of sample means.
If a large number of equal-sized samples (greater than 30 subjects) is selected at random from an infinite population: 1.

The means of the samples will be normally distributed.

2.

The mean value of the sample means will be the same as the mean of the population.

3.

The distribution of sample means will have its own standard devia- tion.

This is in actuality the distribution of the expected sampling error.
Known as the standard error of the mean, it is computed from this formula: S s=—v where S = the standard deviation of individual scores N = the size of the sample = the standard error of the mean To illustrate the operation of the central limit theorem, let us assume that the mean of a sample is 180 and the standard deviation is 12.

Figure 11—1 illustrates the relationship between the distribution of individual scores and the distribution of sample means when the sample size is 36.
If X = 180, N = 36, and S = 12: S = S 12 = = -- = 2 12 The standard error of the mean has a smaller value than the standard deviation of individual scores.

This is understandable, because in com- Chapter 11 Inferential Data Analysis 325 FIGURE 11—1 Normal Distribution of Individual Scores and of Sample Means when N = 36. puting the means of samples, the extreme scores are not represented; means are middle score values.
Note the difference between the range and standard deviation of individual scores and those of the sample means.

From the formula S= it is apparent that as the size of the sample increases, the standard error of the mean decreases.

To cite extreme cases as illustrations, as the sample N approaches infinity, the mean approaches the population mean and the standard error of the mean approaches zero.
= S S = = 0 As the sample is reduced in size and approaches one, the standard error of the mean approaches the standard deviation of the individual scores.

326 Part Three Data Analysis s = S = = S S As sample size increases, the magnitude of the error decreases.

Sample size and sampling error are negatively correlated -(see Figure 11—2).
It may be generalized that, as the number of independent observa- tions increases, the error involved in generalizing from sample values to population values decreases and accuracy of prediction increases.

To the statisticians who must estimate the population mean from a sample mean, their obtained sample mean would not be too far away from the unknown population mean.
One might say that the population mean is "known only to God," but a particular mean calculated from a randomly selected sample can be related to the population mean in the same way as an individual's score is related to the mean, by using the normal curve table in Appendix B.
The chances or probabilities are approximately i& that the sample mean will not be farther than 1 S from the population mean i that the sample mean will not be farther than 1.96 S from the population mean that the sample mean will not be farther than 2.58 S from the population mean Thus the value of a population mean, inferred from a randomly selected sample mean, can be estimated on a probability basis.
In the example presented in Figure 11—1, since S = 2 points, there is approx- FIGURE 11—2 The Relationship between Sample Size and the Magnitude of Sampling Error.

Chapter 11 Inferential Data Analysis 327 imately a h probability that the mean of any randomly selected sample of N = 36 and S = 12 would not be more than 2 points away from the probability that the sample mean would not be population mean, and more than 3.92 points away (± 1.96S).
Knowing the mean and the standard error of the mean of a sample, we can easily determine the confidence interval, within which the "true" mean of the population most likely will be.

To find the 95 percent con- fidence interval, the standard error of the mean is multiplied by 1.96 and the result is added to and subtracted from the mean.

To find the 99 percent confidence interval, the standard error of the mean is multiplied by 2.58 and the result is added to and subtracted from the mean.
Thus if we had a sample with a mean of 93, and a standard error of the mean (S) of 3.2, the 95 percent confidence interval would be .L95% (the population mean) = 93 ±.
(1.96) S = 93 ± (1.96) 3.2 = 93 ± 6.27 = between 86.73 and 99.27 The 99 percent confidence interval would be = 93 ± (2.58) S = 93 ± (2.58) 3.2 = 93 ± 8.26 = between 84.74 and 101.26 We could then say that 95 times out of 100 we would probably be correct in stating that the mean of the population is between 86.73 and 99.27; and correct 99 times out of 100 in stating that the mean of the population is between 84.74 and 101.86.
Parametric Tests Parametric tests are considered to be the most powerful tests and should be used if their basic assumptions can be met.

These assumptions are based on the nature of the population distribution and on the way the type of scale is used to quantify the data observations.
However, as we mentioned in Chapter 10, some parametric tests (the t test and analysis of variance, in particular) are quite robust and are appropriate even when some assumptions are violated (see Glass & Hopkins, 1984, for a more complete explanation).

The assumptions for most parametric tests are the following: 1.

The observations are independent.

The selection of one case is not dependent upon the selection of any other case (there are specific parametric tests for nonindependent samples).
328 Part Three Data Analysis 2.

The samples have equal or nearly equal variances.

This condition is particularly important to determine when samples are small.

3.

The variables described are expressed in interval or ratio scales.

Nominal measures (frequency counts) and ordinal measures (rank- ing) do not qualify for parametric treatment.
Testing Statistical Significance The Significance of the Difference between the Means of Two Independent Groups Because a mean is probably the most satisfactory measure for character- izing a group, researchers find it important to determine whether the difference between means of samples is significant.

To illustrate the point, an example might be helpful.

Let us assume that an experiment is set up to compare the relative effectiveness of two methods of teaching reading.
A sample is randomly selected and the subjects are randomly assigned to either the experimental group or the control group.

The experimental group is taught by the initial teaching alphabet method and the control group by the traditional alphabet.

At the end of a year a standardized reading test is administered and the mean score of each group is computed.
The effectiveness of the experimental group method as compared to the effectiveness of the control group method is the issue, with the end-of-year mean scores of each group the basis for comparison.

A mere quantitative superiority of the experimental group mean score over the control group mean score is not conclusive proof of its superiority.
Because we know that the means of two groups randomly drawn from the same population are not necessarily identical, any difference that appeared at the end of the experimental cycle could possibly be attributed to sampling error or chance.

To be statistically significant, the difference must be greater than that reasonably attributed to sampling error.

-Deter- mining whether a difference is significant always involves discrediting a sampling error explanation.
The test of the sigttificance of the difference between two means is known as a t test.

It involves the computation of the ratio between experimental variance (observed difference between two sample means) and error variance (the sampling error factor).
t- x1-x2 — /s s + JN1 N2 Chapter 11 Inferential Data Analysis 329 where 3 = mean of experimental sample X2 = mean of control sample N1 = number of cases in experimental sample N2 = number of cases in control sample = variance of experimental sample S = variance of control sample If the value of the numerator in this ratio is not significantly greater than the denominator, it is likely that sampling error—not the effect of the treatment or experimental variable—is indicated.
But before we dis- cuss the quantitative criteria that determines the statistical significance of the difference between means, two additional concepts should be consid- ered: 1.

The null hypothesis (H0) 2.

The level of significance The Null Hypothesis (H0) A null hypothesis states that there is no significant difference or relation- ship between two or more parameters.
It concerns a judgment as to whether apparent differences or relationships are true differences or re- lationships or whether they merely result from sampling error.

The ex- perimenter formulates for statistical purposes a null hypothesis, a no- difference or relationship hypothesis.
The experimenter hypothesizes that any apparent difference between the mean achievement of the experi- mental and control sample groups at the end of the experimental cycle is simply the result of sampling error, as explained by the operation of the central limit theorem.

It should be noted that, although the null hypothesis is needed for statistical purposes, most actual hypotheses are alternatives to the null; that is, hypotheses that propose that differences will exist.
The use of the null hypothesis is not restricted to experimental stud- ies.

It may be used when generalizations are inferred about populations from sample data in descriptive research studies.

Students have complained that the statement of a null hypothesis sounds like double-talk.

They are understandably puzzled about the rea- sons for the negative statement that the researcher attempts to reject.

The explanation is somewhat involved, but the logic is sound.
Verification of one consequence of a positive hypothesis does not prove it to be true.

Observed consequences that may be consistent with a positive hypothesis may also be compatible with equally plausible but competing hypotheses.

Verifying a positive hypothesis provides a rather inconclusive test.

330 Part Three Data Analysis Rejecting a null or negative hypothesis provides a stronger test of logic.
Evidence that is inconsistent with a particular negative hypothesis provides a strong basis for its rejection.

Before a court of law, a defendant is assumed to be not guilty until the not-guilty assumption is discredited or rejected.

In a sense, the not-guilty assumption is comparable to the null hypothesis.
If the difference between the mean achievement of the experimental and the control groups is too great to attribute to the normal fluctuations that result from sampling error, the experimenter may reject the null hypothesis, saying in effect that it is probably not true that the difference is merely the result of sampling error.

The means no longer behave as random sample means from the same population.
Something has hap- pened to, or affected, the experimental group in such a way that it behaves like a random sample from a different or changed population.

Thus the researcher may conclude that the experimental variable or treatment prob- ably accounted for the difference in performance, as measured by the mean test scores.

The experimenter is using a statistical test to discount chance or sampling error as an explanation for the difference.
If the difference between means was not great enough to reject the null hypothesis, the researcher fails to reject it.

He or she concludes that there was no significant difference and that chance or sampling error may have accounted for any observed difference.

The Level of Significance The rejection or acceptance of a null hypothesis is based upon some level of significance (alpha level) as a criterion.
In psychological and educational circles, the 5 percent (.05) alpha (a) level of significance is often used as a standard for rejection.

Rejecting a null hypothesis at the .05 level in- dicates that a difference in means as large as that found between the experimental and control groups would have resulted from sampling error in less than 5 out of 100 replications of the experiment.
This suggests a 95 percent probability that the difference was due to the experimental treatment rather than to sampling error.

A more rigorous test of significance is the 1 percent (.01) a level.

Rejecting a null hypothesis at the .01 level would suggest that a difference in means as large as that found between the experimental and control groups would have resulted from sampling error in less than 1 in 100 replications of the experiment.
When samples are large (more than 30 in size) the t critical value approaches the z (sigma) score.

In these cases, if the z value equals or exceeds 1.96, we may conclude that the difference between means is significant at the .05 level.

If the z value equals or exceeds 2.58, we may conclude that the difference between means is significant at the .01 level.

Determining the exact t critical value is discussed later in this chapter.
Chapter 11 Inferential Data Analysis 331 Using the example of the reading experiment previously described, let us supply the data and test the null hypothesis that there was no significant difference between the mean reading achievement of the initial- teaching alphabet experimental group and the traditional alphabet control group.
39.40 Experimental ITA Group 32X1 = 87.43 —t['JN1N2 — — 4.85 \71.23 + 1.20 Control Traditional Alphabet Group N2=34 = 82.58 S = 40.80 = 87.43 — 82.58 /4o480 V32 34 4.85 = VT 4.85 t = 3.11 Because a t value of 3.11 exceeds 2.58, the null hypothesis may be rejected at the .01 level of significance.
If this experiment were replicated with random sampls from the same population, the probability is that a difference between mean performance as great as that observed would result from sampling error in fewer than 1 out of 100 replications.

This test would indicate rather strong evidence that the treatment would prob- ably make a difference in the teaching of reading when applied to similar populations of pupils.
Decision Making Statistical decisions about parameters based upon evidence observed in samples always involve the possibility of error.

Statisticians do not deal with decisions based upon certainty.

They merely estimate the probability or improbability of occurrences of events.

Rejection of a null hypothesis when it is really true is known as a Type I error.

The level of significance (alpha) selected determines the prob- ability of a Type I error.
For example, when the researcher rejects a null hypothesis at the .05 level, he or she is taking a 5 percent risk of rejecting what should be a sampling error explanation when it is probably true.

Not rejecting a null hypothesis when it is really false is known as a Type IL error.

This decision errs in accepting a sampling error explanation when it is probably false.

332 Part Three Data Analysis Setting a level of significance as high as the .01 level minimizes the risk of a Type I error.
But this high level of significance is more conserv- ative and increases the risk of a Type II error.

The researcher sets the level of significance based upon the relative seriousness of making a Type I or a Type II error.
Two-Tailed and One-Tailed Tests of Significance If a null hypothesis was proposed that there was no difference (other than in sampling error) between the mean IQs of athletes and nonathletes, we would be concerned only with a difference and not with the superiority or inferiority of either group.

There is no difference between the mean IQs of athletes and non- athletes.

In this situation we apply a two-tailed test.


If we changed the null hypothesis to indicate the superiority or inferiority of either group it might be stated: Athletes do not have higher IQs than nonathietes.

or Athletes do not have lower IQs than nonathietes.

Each of these hypotheses indicates a direction of difference.

When researchers are hypothesizing a direction of difference, rather than the mere existence of a difference, they can sometimes use a one-tailed test.
For a large sample two-tailed test, the 5 percent area of rejection is divided between the upper and lower tails of the curve (2.5 percent at each end), and it is necessary to go out to ± 1.96 on the sigma (z) scale to reach the area of rejection (Figure 11—3).
For a one-tailed test, since the 5 percent area of rejection is either at the upper tail or at the lower tail of the curve, the t critical value is lower, for it is not necessary to go as far out on the sigma scale to reach the area of rejection (Figure 11—4).

The t critical value in such a case is ±1.645.


Large Sample t Critical Values for Rejection of the Null Hypothesis .05 level .01 level Two-tailed One-tailed test test 1.96 1.64 2.58 2.33 Chapter 11 Inferential Data Analysis 333 FIGURE 11—3 A Two-Tailed Test at the .05 Level (2.5 Percent at Each End).

FIGURE 11—4 A One-Tailed Test at the .05 Level (5 Percent at One End or 5 Percent at the Other End).
A similar pair of curves would illustrate the difference between t critical areas of rejection at the 1 percent level of significance.

The t values must equal or exceed these t critical values for the rejection of a null hypothesis.

Because the t values needed to reject a null hypothesis are smaller for a one-tailed test, and because most researchers would like to reject the null hypothesis, it is tempting always to propose a directional hy- pothesis so as to be able to use a one-tailed test.
However, a one-tailed test should be used only when a directional hypothesis is actually pro- posed for logical andlor theoretical reasons prior to the collection of even preliminary data.

If a reasonable alternative hypothesis could be proposed ii the opposite direction, then, even if a directional hypothesis is to be tested, a two-tailed test should be used.
Hypotheses that athletes would have higher or lower IQs than nonathletes are probably inappropriate for 334 Part Three Data Analysis one-tailed tests.

A better example of a directional hypothesis, for which a one-tailed test would be appropriate, would be: Children will score higher on a reading achievement test after first grade than they did prior to first grade.
In this case, while no difference might be found, it is very unlikely that findings would be in the opposite direction, reading being lower after first grade.

The test of the significance of the difference between two independ- ent means to this point has concerned large samples, and the critical t values for rejection of the null hypothesis have been found in the normal probability table.

When small samples are used to infer population differences, a dif- ferent set of t critical values is used.
But before discussing small sample tests, an important concept known as degrees of freedom should be con- sidered.

Degrees of Freedom The number of degrees of freedom in a distribution is the number of observations or values that are independent of each other, that cannot be deduced from each other.

Although this concept has been puzzling to students of statistics, several analogies and their application to estimation or prediction may help to clarify it.

1.
1.

Let us assume that a coin is tossed in the air.

The statistician predicts that a head will turn up.

If a head comes up, he or she has made one correct, independent prediction.

But if the statistician predicted that a head would turn up and a tail would face down, he or she has made two predictions.

Only one prediction, however, is an in- dependent prediction, for the other can be deduced from the first.

The second added no new information.

In this case there was one degree of freedom, not two.
The strength of a prediction is increased as the number of independent observations or degrees of freedom is increased.

2.

When a mean is computed from a number of terms in a distribution, the sum is calculated and divided by N. N But in computing a mean, 1 degree of freedom is used up or lost, and subsequent calculations of the variance and the standard deviation will Chapter 11 Inferential Data Analysis 335 be based on N — 1 independent observations or N — 1 degrees of freedom.
An example of the loss of a degree of freedom follows.

A Original Distribution B Altered Distribution +5 + 4 + 3 +2 +1 X = +15 N = 5 X=+3 15) 8 5 ( 7J —20 X = +15 N = 5 X=+3 These four terms can be altere4 in any way.

This term is dependent on, or .

determined by, the other four terms.

In the altered distribution, the fifth term must have a value of —20 for the sum to equal +15, the mean to be + 3, and the sum of the.

.deviations from the mean to equal zero.


Thus, four terms are independent and can be altered, but one is dependent or fixed and is deduced from the other four.

There are N — 1 (5 — 1) or 4 degrees of freedom.

Standard Deviation for Samples (5).

In Chapter.

10 we describedthe var- iance and standard deviation for a population.

Because most of the time we use samples selected from the population, itis necessary to introduce the formulas for the variance S2 and the standard deviation (S) of a sample.
The sample formulas differ only slightly from the population formulas.

As we will see, instead of dividing by N in the deviation formula and by N2 in the raw score formula, the sample formulas divide by n — 1 and n(n — 1), respectively.1 This is done to correct for the probability that the smaller the sample, the less likely it is that extreme scores will be included.

Thus the formula for a, if used with a sample, would underestimate the standard deviation of the population.
Dividing by n — 1 or n(n — 1) corrects for this bias more or less depending upon the sample's size.

This makes the standard deviation of the sample more representative of the population.

In a small sample, say n = 5, the correction is rather large, dividing by 4 instead of 5—a reduction of 20 percent in the denominator.

In a large sample, say n = 100, the cOrrection is insignificant, dividing by 99 instead of 100—a reduction of 1 percent in the denominator.
The two formulas for sample standard devition with the deviation and the raw score methods of computation, respctive1y, are: 'N represents the number of subjects in the population; n represents the number of subjects a amp1e.

336 Part Three Data Analysis s = — n — 1 or X n — 1 and S = 'V X2 — n(n — 1) (X)2 No doubt the reader can see that the only changes are in the denominator.
Thus, if we substitute n(n — 1) for N2 and calculate S2 and S using the data from Chapter 10, we would find the following: 52 — 9(52,125) — (675)2 — 469,125 — 455,625 — — 9(8) 72 = 13,500 = 187.50 S = V187.50 = 13.69 These results are quite a change from a = 166.67 (change of + 20.83) and a = 12.91 (change of + .78).

These relatively large differences from the population formula to the sample formula are due to the small sample size (n = 9), which made a relatively large correction necessary.
The correction for calculating the variance and standard deviation is important because unless the loss of a degree of freedom is considered, the calculated sample variance or standard deviation is likely to underestimate the pop- ulation variance or standard deviation.

This is true because the mean of the squared deviations from the mean of any distribution is the smallest possible value, and probably would be smaller than the mean of the squared deviation from any other point in the distribution.
Because the mean of the sample is not likely to be identical to the population mean (because of sampling error), the use of the number of degrees of freedom, rather than N in the denominator, tends to correct for this underestimation of the population variance or standard deviation.

The strength of a prediction or the accuracy of an inferred value increases as the number of independent observations (sample size) is increased.
Because large samples may be biased, sample size is not the only important determinant, but if unbiased samples are selected ran- domly from a population, large samples will provide a more accurate basis than will smaller samples for inferring population values.

Student's Distribution (t) When small samples are involved, the t table is used to determine statis- tical significance, rather than the normal probability table.
This concept of small sample size was developed around 1915 by William Sealy Gosset, a consulting statistician for Guinness Breweries of Dublin, Ireland.

Because Chapter 11 Inferential Data Analysis 337 his employer's rules prohibited publication under the researcher'sname, he signed the name "Student" when he published his findings.

Gosset determined that the distribution curves of small sample means were somewhat different from the normal curve.
Small sample distribu- tions were observed to be lower at the means and higher at the tails or ends of the distributions.

Gosset's t critical values, carefully calculated for small samples, are reproduced in the t distribution table in Appendix D. The t critical values necessary for rejection of a null hypothesis are higher for small samples at a given level of significance (see Figure 11—5).

Each t critical value for rejection is based upon the appropriate number of degrees of freedom.
As the sample sizes increase, the t critical values necessary for re- jection of a null hypothesis diminish and approach the z values of the normal probability table.

Significance of the Difference between Two Small Sample independent Means When the samples are small and their variances are equal or nearly equal, the method of pooled variances provides the appropriate test of the sig- nificance of the difference between two independent means.
The formula is a bit more involved than the one previously illus- trated, but it provides a more precise test of significance.

The appropriate t critical value for rejection of the null hypothesis would be found forN + N — 2 degrees of freedom, using the t distribution table.

x1-x2 +(N2—1)S(1 N2—2 — 1)S N1 + FIGURE 11—5 Distribution of Large and Small Sample Means.
338 Pail Three Data Analysis For example, in comparing the significance of the mean IQ difference between samples of 8 athletes and 10 nonathietes, the number of degrees of freedom would be N + N — 2 = 8 + 10 — 2 = 16.
From the t distribution table at 16 degrees of freedom, the t critical values necessary for the rejection of the null hypothesis would be 16 Degrees of Freedom Two-tailed test One-tailed test Homogeneity of Variances Level of Si .05 gnificance .01 2.921 2.583 2.120 1.746 In t tests for small samples, one condition must be met to justify the method of pooi variances.

This condition is known as equality or homo- geneity of variance.
It does not literally mean that the variances of the samples to be compared must be identical, but only that they do not differ by an amount that is statistically significant.

Differences that would be attributed to sampling error do not impair the validity of the process.

To determine whether the samples meet the criterion of equality of variances an 'max test is used.
— S2 (largest variance) — 52 (smallest-variance) This F ratio is never less than one, for the largest variance is always divided by the smallest.

To test for homogeneity Of variance, an F distri- bution table is used in much the same way as the t distribution table.

F critical values are presented for determining the statistical significance of the calculated F critical ratio, based upon the appropriate rows and col- umns, each at N — I degrees of freedom.
A few .05-level-of-significance values from the Fmax distribution table are presented in Table 11—i.

The degrees of freedom for the largest group is used if the samples differ in size.

With t tests, there will be only two variances.

For analysis of variance (discussed later in this chapter), there usually will be more than two variances.

Unless the calculated F equals or exceeds the appropriate F critical value, it may be assumed that the variances are homogeneous and the difference is not significant.
For example, if two samples with 10 degrees of freedom (greater variance 38.40) and 12 degrees of freedom (smaller variance 18.06) were subjected to the test of homogeneity: Chapter 11 Inferential Data Analysis 339 TABLE 11—1 Distribution of F (.05 level) Number of Variances Degrees of freedom for largest group (N — 1) 9 10 12 15 2 4.03 3.72 2.86 3 5.34 4.85 4.16 3.54 4 6.31 5.67 4.79 4.01 F 38.40 18.06 2.13 An F critical value of 3.28 must be equaled or exceeded to determine that the difference between variances is significant at the .05 level.
In this example, since 2.13 < 3.28, the researcher would conclude that the vari- ances fulfilled the condition of homogeneity and that the method of pooled variances is appropriate.

An example using small samples illustrates the process of calculating the F ratio to test homogeneity of variance and then calculating the appropriate t test.

The mean score of 10 delinquent boys on a personal adjustment invetitory -was compared with the mean score of 12 nondelinquent boys, both groups selected at random.
Test the null hypothesisthat there is no statistically significant difference between the mean test scores at the .01 level of significance.
Delinquent Boys Nondelinquent Boys X2—9 = 20.44 N2=10 = 19.60 N1=12 = 1.04 (the vanances are homogeneous) 20.44 F = df = 10 + 12 — 2 = 20 xl - x2 — 1)S (i + \N1 N2 — 1)S + (N2 N1+N2—2 14 — 9 fiii9.6o) + 9(20.44) (i 12 + 10— 2 \12 + 10 340 Part Three Data Analysis 5 /215.60 + 183.96 (ii \6o 20 5 _5 '/19.98 (M) — = 2.62 — — = Because this is a two-tailed test, the t critical value for rejection of the null hypothesis at the .01 level of significance for 20 degrees of freedom is 2.845.
Because the calculated value is 2.62, it does not equal or exceed the t critical value necessary for rejection of the nill hypothesis at the .01 level for 20 degrees of freedom; the hypothesis is not rejected, and we conclude that there is no significant difference.
Had we used the .05 level of significance for 20 degrees of freedom, the t critical value necessary for rejection would be 2.086, and we could have rejected the null hypothesis, for our calculated t critical ratio of 2.62 exceeds the 2.086 t table value.
By using the data from a previous example, comparing reading achievement of a group using the ITA reading method with that of the control group, we can see that this formula gives us the same resultas did the formula used in that example: ITA Control N1=32 Z = 87.43 S = 39.40 40.80 F = N2=34 = 82.58.
S = 40.80 = 1.04 (the vanances are homogeneous) df=32+ 34—2=64 x1 - x2 /(N, — 1)S + (N2 — 1)S (i + N1+N2—2 \N, N2 87.43 — 82.58 /31(39.4) + 33 (40.8) (1 64 1 3234 Chapter 11 InferentIal Data Analysis 341 4.85 /1221.4 + 1346.4 ( 34 32 \1O881O88 64 4.85 - /4O.12(j) 4.85 4.85 V1.56 = 3.11 Thus the two formulas presented in this chapter for- comparing the means of two independent samples are actually equivalent.
Significance of the Difference between the Means of Two Matched or Correlated Groups (Nonindependent Samples) The two previous examples of testing the significance of the difference between two independent means assume that the individuals were ran- domly assigned to the control and experimental groups.

There are situ- ations when it is appropriate to determine the significance of the difference between means of groups that are not randomly assigned.

Two such situations are 1.


When the pairs of individuals who make up the groups have been matched on one or more characteristics—IQ, reading achievement, identical twins, or on some other basis for equating the individuals.

2.

When the same group of individuals takes a pretest, is expvsed to a treatment, and then is retested to determine whether the influence of the treatment has been statistically significant, as determined by mean gain scores.
Because the groups are not independent samples, it is necessary to calculate the coefficient of correlation between: 1. the posttest scores of the matched pairs sample; or 2. the pretest and posttest scores of the participants in the experiment.

If the coefficient of correlation is used, the appropriate t test would be based upon this formula: 342 Part Three Data Analysis X1X2 /s s (s1\(s2 \IN1N2 The number of degrees of freedom would be the number of pairs minus one.
Two examples illustrate situations A and B: Example A.

Two groups, each made up of 20 fifth-grade students, were matched on the basis of IQs.

Filmstrips were used to teach the experi- mental group; the control group was exposed to a conventional "read and discuss" method.

The researcher wished to test the null hypothesis that there was no difference between the mean achievement of the two groups (a two-tailed test) at the .05 level.
x N1=20 S = 54.76 Z = 53.20 r=+.60 C N2=20 = 42.25 Z = 49.80 df19 54.76 F = = 1.30 (vanances are homogeneous) 53.20 — 49.80 /54.76 20 + 20 2 (7.4o\ (6.50 (+.)

4.47) \4.47 3.40 2.74 + 2.14 — 1.20(1.66)(i.45) — 3.40 'V484 — 2.89 3.40 3.40 =_=j-2.43 Because the t value of 2.43 exceeds the t critical value of 2.093 for a two-tailed test at the .05 level at 19 degrees of freedom, the null hypothesis may be rejected.

Example B.
Example B.

A typing teacher wished to determine the effectivenessof 10 minutes of transcendental meditation upon the speed and accuracy of Chapter ii Inferential Data Analysis 343 his class of 30 students.

He administered a timed speed/accuracy test and recorded the score for each student.

The next day, after 10 minutes of dass participation in a TM exercise, he administered a similar timed speedJ accuracy test.
He computed the mean scores for the pretest and the scores obtained calculated the coefficient of correlation be- He then tested the null hypothesis that the TM experience would speed and accuracy of typing of his class.

after the TM experience and tween the pairs of scores to be + .84. not improve the proficiency in He chose the .01 level of significance, using a one-tailed test.
Pretest N2=30 S = 37.21 "2 = 44.80 r=+.84 Test after TM N1=30 S = 36.10 Z = 49.10 df=29 F = = 1.03 (variances are homogeneous) /3721 + 49.10 — 44.80 2(+.84)()() V1.24 + 1.20 — 1.68(1.11)(1.I 4.30 V2.44 — 2.c 4.30 = =6.94 Because the t value of 6.94 exceeds the t critical value of 2462 for a one-tailed test at the .01 level for 29 degrees of freedom, he rejected the null hypothesis, concluding that the meditation experience did improve performance proficiency.
Statistical Significance of a Coefficient of Correlation Throughout this chapter on inferential dataanalysis, the idea of statistical significance and its relationship to the null hypothesis have been empha- sized.

An observed coefficient of correlation may result from chance or 344 Part Three Data Analysis sampling error, and a test to determine its statistical significance is ap- propriate.

In small sample correlations, chance could yield what might appear to be evidence of a genuine relationship.
The null hypothesis (H0) states that the coefficient of correlation is zero.

Only when chance or sampling error has been discredited on a probability basis can a coefficient of correlation be accepted as statistically significant.

One test of the significance of r is determined by the use of the formula:-1\rVN2] With N — 2 degrees of freedom, a coefficient of correlation is judged as statistically significant when the t value equals or exceeds the t critical value in the t distribution table.
If r = .40 N = 25 .4OVZ = ________ = — = 2.09 1.92 .92 — (4Q)2 Using a two-tailed test at the .05 level with 23 degrees of freedom, the null hypothesis is rejected, exceeding the t critical value of 2.07.

As sample size is decreased, the probability of sampling error increases.

For a smaller sample, the coefficient must be larger to be statistically signifi- cant.
If r = .40 N = 18 = .40\/i \/I— (.40)2 — 1.60174 — At 16 degrees of freedom the observed value of 1.74 fails to equal or exceed the t critical value of 2.12 at the .05 level of significance, and the null hypothesis would not be rejected.

Thus, with a sample N of 18, a coefficient of correlation of .40 would not be large enough for the re- jection of the null hypothesis, a sampling error explanation.
There is a more direct and simple way to evaluate the statistical significance of the coefficient of correlation.

Instead of computing the t Chapter 11 Inferential Data Analysis 345 value and using the table in Appendix D, critical values of r can be read directly from the table in Appenthx C at the .10, .05, .02, and .01 levels.

Statistical significance merely indicates the probable influence of chance or sampling error upon an observed coefficient of correlation between sample variables.
It is related to sample size.

When sample size decreases, sampling error increases.

When samples are small, what may appear to be a large coefficient of correlation may not pass the test of statistical significance.

When samples are large enough, almost any coefficient, how- ever small, may prove to be significant.

At the .05 level, when N = 18, an r of .468 would be necessary for statistical significance, but when N = 500 an r of .088 would be sufficient.
It is apparent that statistical significance is not a measure of the magnitude of a variable relationship..

It is only an estimate of the probable influence of sampling error in the observed coefficient of orrelation.

Sta- tistical significance may be of great importance to the researcher when conducting small sample studies, but of less importance in large sample research.
The reader should remember from Chapter 10 that the interpretation of a correlation involves more than just its statistical significance.

A low correlation, for example r = .20, is statistically significant if a large sample, 200 subjects, was used.

Despite its statistical significance, this correlation, .20, is still a low correlation with the two variables only having .04 of their variance in common.
The values presented in the table in Chapter 10 provide guidelines for evaluating the magnitude of r, but they should be interpreted cau- tiously in terms of several criteria: 1.

The magnitude and statistical significance of the coefficient of cor- relation 2.

The nature of the variables 3.

The design of the study 4.


The reported findings of other respected investigators in the field of inquiry A1Varjance (ANOVA) We have noted that the t test is employed to determine, after treatment, whether the means of two random samples were too different to attribute to chance or sampling error.

The analysis of variance is an effective way to determine whether the means of more than two samples are too different to attribute to sampling error.
It would be possible to use a number of t tests to determine the significance of the difference between five means, two at a time, but it 346 Part Three Data Analysis would involve ten separate tests.

The number of necessary pair-wise com- parisons of N things is determined by the formula: N(N-1) 2 If N = 5, 5(5 —1) — 20 j- 2 10 Analysis of variance makes it possible to determine whether the five means differ significantly with a single test, rather than ten.
Another advantage lies in the fact that computing a number of separate t tests will increase the overall Type I error rate for the experiment.

For instance, if we calculated ten t tests (for comparing five means) and accepted .05 as our significance level, we would have ten times .05, or .50, as the prob- ability that we would reject at least one null hypothesis when it is really true (Type I error).

Thus we would have an unacceptably high error rate for the total experiment.
Analysis of variance takes care of this by com- paring all five means simultaneously in a single test.

In single classification, or one-way analysis of variance, the relationship between one independent and one dependent variable is examined.

For example: A test of abstract reasoning is administered to three randomly selected groups of students in a large state university majoring in mathematics, philosophy, and chemistry.
Are the mean test scores of each of the three groups significantly different from one another?

The analysis of variance consists of these operations: /Fhe variance of the scores for three groups are combined into one '-" composite group, known as the total groups variance (Vi).

3/4Fhe mean value of the variances of each of the three groups, com- '—' puted separately, is known as the within-groups variance (V).
Z' The difference between the total groups variance and the within- V groups variance is known as the between-groups variance (V — V, = Vb).

4.

The F ratio is computed — — VL, tween-ou;s within-groups variance The logic of the F ratio is asfollO.

The within-groups variance represents the sampling error in the distributions and is also referred to I Chapter 11 Inferential Data Analysis 347 as the error variance or residual.
The between-groups variance represents the influence of the variable of interest or the experimental variable.

If the between-groups variance is not substantiallygreater than the wit hing roups variance, the researcher would conclude that the difference between the means is probably only a reflection of sampling error.
If the F ratio were substantially greater than one, it would seem that the ratio of the between- groups variance and the within-groups variance was probably too great to attribute to sampling error.

The critical values of the F ratio (named for Sir Ronald Fisher) are found in an F table (different from the table referred to earlier), which indicates the critical values necessary to test the null hypothesis at selected levels of significance.
As can be seen in the F table presented in Appendix F, there are two different degrees of freedom, one for Vb (the numerator) and one for (the denominator).

The degrees of freedom for the within-groups var- iance (V) is determined in the same way as it Is for the t test—that is, the sum of the subjects for all of the groups minus the number of groups.

We can use K to represent the number of groups and N1 + N2 + — K to represent the degrees of freedom for the within-groups variance.
In the above example, if we had ten students in each of the three groups, we would have 10 + 10 + 10 — 3, or 27, degrees of freedom for the within-groups variance.

The degrees of freedom for the between-groups variance (Vb) is determined by the number of groups minus one (K — 1).

In the above example, there are three groups, thus, two degrees of free- dom.
The above example then has two degrees of freedom for the nu- merator and 27 for the denominator, for a total of 29, one less than the total number of subjects.

The calculation of F involves finding the mean of the deviations from the mean, squared.

Thus the between-groups variance (Vb) is more com- monly referred to as the mean squared between (MSb), and the within- groups variance (V) is more commonly referred to as the mean squared within (MS).
The formula then becomes MS, MS Given the data in Table 11—2, we would calculate F as follows.

The first step is to find the sum of the squared deviations of each person's score for the mean of all of the subjects.
This is known as the total sum of squares (SS) and can be found by using the following formula: ( X)2 N 348 Part Three Data Analysis TABLE 11 —2 Sample Data for Calculating Analysis of Variance Group I Mat hematics Majors xl 18 22 18 23 19 24 20 21 19 25 Z = 20.9 x1= xl 324 484 324 529 361 576 400 441 361 625 Group 2 Philosophy Majors x2 26 27 18 22 23 19 27 26 24 26 = 23.8 x2= 2 x2 676 729 324 484 529 361 729 676 576 676 Group 3 Chemistry Majo rs x3 18 14 15 14 19 21 17 17 18 19 x3=1 = 17.2 2 x3 324 196 225 196 361 441 289 289 324 361 = 20.63 X2 = +X + = 13,191 In our example this would be (619)2 = 13,191 — 12,772.03 = 418.97 SS = 13,191 — The next step is to divide the total sum of squares into the between- groups sum of squares (SSb) and the within-groups sum of squares (SS).
We determine SSb using the formula: = b ( X)2 ( X2)2 + + n2 — ( )()2 N n = the number of subjects in a group N = the number of subjects for all the groups combined In our example this would be — (209)2 SSb— 10 + (238)2 10 + (172)2 10 (619)2 30 = 4368.1 + 5664.4 + 2958.4 — 12,772.03 = 218.87 Chapter 11 Inferential Data Analysis 349 The within-groups sum of squares (SS) can be calculated in two ways.
First, we can calculate it directly, using the formula: ' X'2 ssw=x—' " ' X'2 j ' X'2 ____ In our example this would be SS, = 4425 — 4368:1 + 5760 — 5664.4 + 3006 — 2958.4 = 200.1 SS, can also be found by subtracting SSb from SS: SS, = SS — SS, = 418.97 — 218.87 = 200.1 By using both methods of calculating SS,, we can check our results for computational errors.
To find the mean square between (MSb) and mean square within (MS), we divide the sum of squares between (SSb) and the sum of squares within (SS) by their respective degrees of freedom (df).

MS — MS, — SS,/dfb — — SS/df — MSb — 218.87/2 — 200.1/27 MS — = = 14.77 Table 11—3 shows what a typical summary table for this analysis of variance would look like.

The F of 14.77 is statistically significant at the .01 level.
That is, there is less than I chance in 100 that the observed •differences among these three group means is due to sampling error.

We can reject the null hypothesis with this degree of confidence.

However, this significant F does not pinpoint exactly where the differences are in a pair-wise way.

That is, the three groups.

differ signif- icantly, but does Group 1 differ from Group 2 and/or Group 3?

Does Group 2 differ from Group 3?


These questions can be answered by still further analysis of the data using one of the several post hoc analyses available (e.g., Scheffe, Tukey, Neuman-Keuls, Duncan).
The reader should consult one of several fine texts (e.g., Glass & Hopkins, 1984; Kirk, '1982; 350 Part Three Data Analysis TABLE 11—3 Summary of Three Group Analysis of Variance Source of Variance Between groups (major) Within groups (error) Total < .01 SS 218.87 200.10 418.97 df 2 27 MS 109.44 7.41 F 14.77* Winer, 1971) for more information regarding the use and calculation of these post hoc tests.
In multiple classification or factorial analysis of variance, both the inde- pendent and interactive effects of two or more independent variables on one de- pendent variable may be analyzed.

Not only may the effect of several inde- pendent variables be tested, but their interaction (how they may combine in a significant way) may be examined.
Because human behavior and the factors influencing it are complex and can rarely be explained by single independent variable influences, this method of analysis is a powerful statistical tool of the behavioral researcher.

With computers so readily available, it is rarely necessary to calculate a factorial analysis of variance by hand.

An example of a computer printout from such an analysis is included in Chapter .12.

In factorial designs, the total variance is divided into more than two parts.
It is divided into one part for each independent variable (main effect), one part for each interaction of two or more independent variables, and one part for the residual, or within-group, variance.

Thus, in a design with two independent variables, the variance is divided into four parts.

For example, in our previous example comparing the performance of mathematics, philosophy, and chemistry majors on a test of abstract rea- soning, we could also divide each of the three groups into males and females.
We then have a factorial design with two independent variables, student's major and sex.

Because there are three conditions of student major and two conditions of sex, this is a 3 x 2 design.

As shown in Chapter 12, this results in the variance being divided into four parts: the main effect of student's major, the main effect of student's sex, the in- teraction effect of student's major with sex, and residual.
From this, three separate Fs are derived: one to test the difference among the three majors, one to test the difference between males and females, and one to test the interaction of students' sex and major.

An example of a significant inter- • action was presented in Chapter 6 (see Figure 6—3).

With the aid of computers, analysis of variance can be used with any number of independent variables.
The only limitations are in con- trolling for potentially confounding variables and interpreting complex Chapter 11 Inferential bath Analysis 351 interaction effects.

Glass and Hopkins (1984), Kirk (1982), and Winer (1971) are excellent resources for the student wanting more information about analysis of variance designs and computation.
Analysis of Covariance (ANCOVA) and Partial Correlation Analysis of covariance and partial correlation are statistical techniques that can remove the effect of a confounding variab1e'sinfluence from a study.

Partial correlation is used to remove the effect of ne variable on the cor- relation between two other variables.
For example, if a correlation is de- sired between IQ and academic achievement and the subjects have a range of ages, we would not want the variable of chronological age to affect the correlation.

Thus, we would partial out its effect on the other two vari- ables, IQ and academic achievement.

This is symbolized by r123, the correlation of variables 1 and 2 with 3 removed.
We can calculate the partial correlation using the following formula: r12 — — VI — (r13)(r) r3) (1 — r3) An example from Glass and Hopkins (1984) may help to further clarify this concept.

In this example a correlation between visual perception (X1) and reading performance (X2) is found to be .64 for children ranging in age (X3) from six to fifteen years.
Because of the wide age range, and because children's reading and visual perception both generally improve with age, it seems appropriate to partial out the effect of age.

Given the following correlation coefficients, we can calculate the partial correlation of visual perception and reading performance with age removed.
r12 (correlation of visual perception (X1) and reading performance (X2) = .64 r13 [correlation of X1 and age (X3)1 = .80 r23 (correlation of X2 and X3) = .80 — r123 — .64 — (.80) (.80) VU — .802) (1 — .802) — — .64 — .64 — .36 — As Glass ax'd Hopkins (1984) point out: 352 Part Three Data Analysis one would estimate the value of r12 for children of the same chronological age to be zero.

If enough children of the same chrono- logical age were available, r could be calculated for them alone to c\Iec\.
tine pTe'VlOUS tesult.

The pa*iaX cotte%a%Xor coe1vieit series the purpose of estimating r12 for a single level of chronological age even when there is an insufficient number of persons at any single conological age to do the estimating by direct calculation.

(p. 131) lysis of covariance (ANCOVA) uses the principles of partial cor- relation with analysis of variance.

It is particularly appropriate when the subjects in two or more groups are found to differ on a pretest or other initial variable.
In this case, the effects of the pretest and/or other relevant variables are partialed out and the resulting adjusted means of the posttest scores are compared.

Analysis of covariance is a method of analysis that enables the researcher to equate the preexperimental status of the groups in terms of relevant known variables.
The initial status of the groups may be determined by pretest scores in a pretest.-posttest study, or in posttest- only studies, by such measures as intelligence, reading scores, grade-point average, or previous knowledge of subject matter.

Differences in the initial status of the groups can be removed statistically so that they can be compared as though their initial status had been equated.
The scores that have been corrected by this procedure are known as residuals, for they are what remain after the inequalities• have been removed.

Analysis of covanance, used with one or more independent variables and one dependent variable, is an important method of analyzing exper- iments carried on under conditions that otherwise would be unacceptable.

The mathematical procedures are rather complicated, and there are many steps in computing their values.
However, with the use of standard com- puter programs, the analysis of complex studies can be processed almost instantaneously.

It should be noted that analysis of covariance is not as robust as analysis of variance.

That is, violation of the assumptions on which anal- ysis of covariance is based may make its use inappropriate.

In addition, as Glass and Hopkins (1984) point out, ANCOVA does not transform a quasi-experiment into a true (randomized) experiment.

There is no sub- stitute for randomization.


Multiple Regression and Correlation In Chapter 10, we discussed correlation and linear regression when only two variables are involved.

We demonstrated the prediction (regression) equation for estimating the value of one variable from another: c = a + bX.

Chapter 11 Inferential Data Analysis 353 In many cases, it is better to use more than one predictor variable to predict an outcome, or dependent, variable.
Forexample, a university may use a number of variables to predict college CPA in its admission process.

High school grades and SAT or ACT scores are usually used.

Ranks in high school graduating class could also be included.

Multiple regression is the term used for predicting Y (in the example above, college GPA) from two or more independent variables combined.
The formula for multiple regression is just an extension ofthat for linear regression: = a + b1X1 + L2X2 + where = the variable to be predicted a = the constant or intercept b1 = the slope of the first predictor = the slope of the second predictor X1 = the score on the first predictor X2 = the score on the second predictor An example may further clarify this procedure.

In Chapter 10 we gave an example of predicting college CPA from high school CPA.
We will use the data given in that example and add SAT score (combined.

verbal, and quantitative).

= college GPA X1 = high school CPA X2 = SAT score r12 = correlation of high school CPA and SAT score .
= correlation of college CPA and high school CPA r2 = correlation of college CPA and SAT score The data are as follows: V= 2.40 S = 0.50 = 2.10 354 Part Three Data Analysis Si = 0.60 X2=930.00 5x2 = 80.00 r12 = 0.22 = 0.52 0.66 r2 = The first step in finding b is to calculate the standardized beta weight (13): — r1 13 — i_ 2 (r2) (r12) Ti2 13z (r,2 — (r1) (ri2) i_ 2 Ti2 - — .52 — (.66) (.22) — .375 — 394 1(.22)2 952 132 — .66 — (.52) (.22) 1 — (.22)2 — .546 .952 — Using the standardized beta weights and the standard deviations, we can calculate the raw score beta weights (b): (Sr) bi13is (Sw) xi = .394 () = .328 b2 = .574 (Ii) = .004 The next step is to calculate the intercept, a, from the formula: V — — b22 — .
In this case: a = 2.4 — .328 (2.1) — .004 (930) = 2.4 — .689 — 3.72 = —2.01 Chapter 11 Inferential Data Analysis 355 Finally, we can calculate the predicted college GPA for the two stu- dents in the example from Chapter 10: Xai (student A's high school CPA) = 2.00 Xa2 = (student A's SAT score) = 900.00 Xbl = (student B'shigh school CPA) = 3.10 Xb2 = (student B's SAT score) = 1100.00 = a + b1X1 + b2X2... = —2.01 + .328 (2.00) + .004 (900) = —2.01 + .656 + 3.6 = 2.25 = —2.01 + .328 (3.10) + .004 (1100) = —2.01 + 1.02 + 4.4 = 3.41 Student A, with the below-average high school CPA and SAT score, is predicted to have a below-average college CPA, and student B, with the above-average high school CPA and SAT score, is expected to have an above-average college GPA.
When we compare these findings with the regression results in Chapter 10, we see that the addition of a con- firming score resulted in a prediction further above or below the average.

That is, when we included an SAT score for student A that was to the same side of (below) the means as his or her high school CPA, the result was a predicted college CPA further below the average than the one predicted by high school CPA alone (2.25 versus 2.36).
Conversely, when we included an SAT score for student B that was above the mean, as was his or her high school CPA, the result was a predicted college CPA further above the average than the one predicted by high school GPA alone (3.41 versus 2.83).

If we had added a disconfirming SAT score to high school CPA, it would have the opposite effect described above.
For example, if student A had an SAT of 1080 instead of 900, his or her predicted college CPA would be higher than that predicted with high school CPA alone.

= 2.01 + .328 (2.00) + .004 (1080) = —2.01 + .656 + 4.32 = 2.97 In either case, confirming the addition of related variables to a prediction equation will result in more accurate predictions.
356 Part Three Data Analysis We should point out that when we write of "adding" a variable to the equation, we do not mean that the amount of the prediction from one variable is just added to by the amount of prediction from a second, and!

or subsequent, variable.

The multiple regression equation controls for the overlap (relatedness) of the predictor variables.

The result is that the second variab'e only "adds" the amount of prediction that it has inde- pendent of the first variable.
This is necessary since other attributes (var- iables) may directly influence more than one of the predictor variables.

For instance, in our example, high school grades and SAT scores are probably both influenced by IQ.

Multiple regression controls for that part that SAT sOres and high school grades have in common due to other factors such as IQ.

This has the same effect as partial corrclation.


Multiple correlation (R)is the correlation between the actual scores (Y) and the scores predicted (Y) by two or more independent variables.

It is most useful for determining the percentage of the variance of the predicted scores that can be explained by the predictors.

This was discussed as the coefficient of determination (r2) in relation to the simple, bivariate cor- relation (r) in Chapter 10.
For multiple correlation, R2 is the percentage of the variance of the predicted (dependent) variable that is due to, or explained by, the combined predictor (independent) variables.

Con- versely, 1 — R2 is the percentage of the variance of the predicted variable that is due to factors other than the predictor variables.

In the previous multiple regression example, the multiple correlation (the correlation between actual college GPA and predicted college GPA) is R = .583.
The percentage of college GPA that is due to a combination of high school GPA and SAT scores is then R2 = .340.

Thirty-four percent of the variance of college CPA is explained by high school grades and SAT scores.

Because 1 — R2 is .660, 66 percent of college CPA is due to other factors such as motivation, involvement in extracurricular activities, measurement error, and so on.

This gives a good idea of just how accurate we can expect individual predictions to be.
Given this data, we would expect to be able to predict broad categories such as "probable fail," "borderline," "probable pass," and probable high CPA.

To expect to accurately predict a person's college CPA would be unrealistic.

For greater detail, the reader should consult a more advanced text that specializes on this topic (e.g., Cohen & Cohen, 1975; Neter, Wasser- man, & Kutner, 1985).

A computer analysis of a more intricate multiple regression is presented in Chapter 12.
Nonparametric Tests The parametric tests presented in this chapter are generally quite robust; that is, they are useful even when some of their mathematical assumptions Chapter 11 Inferential Data Analysis 357 are violated.

However, sometimes it is necessary, or preferable, to use a nonparametric or distribution-free test.

Nonparametric tests are appropriate when 1.

The nature of the population distribution from which samples are drawn is not known to be normal.

2.
2.

The variables are expressed in nominal form (classified in categories and represented by frequency counts).

3.

The variables are expressed in ordinal form (ranked in order, ex- pressed as first, second, third, etc.).

Nonparametric tests, because they are based upon cobinted or ranked data rather than on measured values, are less precise, have less power than parametric tests, and are not as likely to reject a null hypothesis when it is false.
Many statisticians suggest that parametric tests be used, if possible, and that nonparametric tests be used only when parametric assumptions cannot be met.

Others argue that nonparametric tests have greater merit than is often attributed to them because their validity is not based upon assumptions about the nature of the population distribution, assumptions that are so frequently ignored or violated by researchers employing par- ametric tests.
Of the many nonparametric tests, two of the most frequently used are described and illustrated here: the chi square (x2) test, and the Mann— Whitney test.

Square Test (x2) The x2 test applies only to discrete dna, counted rather than measured values.

It is a test of independence, the idea that one variable is not affected by, or related to, another variable.

The x2 is not a measure of the degree of relationship.
It is merely used to estimate the likélthood that some factor other than chance (sampling error) accounts for the apparent relationship.

Because the null hypothesis states that there is no relation- ship (the variables are independent), the test merely evaluates the prob- ability that the observed relationship results from chance.

As in other tests of statistical significance, it is assumed that the sample observations have been randomly selected.
The computed x2 value must equal or exceed the appropriate table's (see Appendix E) critical value to justify rejection of the null hypothesis or the assumption of independence at the .05 or the .01 level of signifi- cance.

A finding of a statistically significant x2 value doesn't necessarily indicate a cause-and-effect relationship, a limitation that was observed 358 Part Three Data Ana1ys when we interpreted a coefficient of correlation.
A significant x2 finding indicates that the variables probably do not exhibit the quality of inde- pendence, that they tend to be systematically related, and that the rela- tionship transcends pure chance or sampling error.

There are situations when the theoretical or expected frequencies must be computed from the distribution.

Let us assume that 200 residents of a college dormitory major in business, liberal arts, or engineering.
Is the variable, major, related to the number of cigarettes smoked per day on the average for a 3-week period?

The null hypothesis would state that major is not related to the number of cigarettes smoked; that the variables major and frequency of cigarette smoking are independent; Chi square observations should be organized in crossbreak form.
In each category, the expected frequencies (fe)' as contrasted to the observed frequencies (f0), is the number of cases that would appear if there were no systematic relationship between the variables, a pure chance relation- ship.
Number of Cigarettes Smoked per Day Major Business Liberal Arts Engineering Total f row f column None 6(12) 14(12) 10(6) 30** 1 —15 60(56) 58(56) 22(28) 140** More than 15 14(12) 8(12) 8(6) 30** Total 80' 80* 40* 200 grand total Numbers represent the actual observed frequencies f, Nuñibers in parentheses represent the expected frequencies f, The expected frequency for each of the 9 cells is computed by the formula fe ( f column)( f row) grand total Computation of expected frequencies (fe): (30)(80) 200 (30)(80) - 200 = (12) (140)(80) — 200 (56) (30)(80) — 200 (12) (12) (140)(80) — 200 (56) (30)(80) — 200 (12) (30)(40) - (6) — 200 Chapter 11 InferentIal Data Analysis 359 (140)(40) — 200 (28) (30)(40) - 6 200 Computation of the x2 value: (6 — 12)2 — 12 (14 — 12)2 — 12 (60 — 56)2 — 29 56 (14 — 12)2 — 12 (58 — 56)2 — 0 07 56 (8_12)2 —133 12 (10 — 6)2 = 2.67 (22 — 28)2 — 1 29 28 (8_6)2 —67 6 x2=3+•29•33 .33+0.07+1.33+2.67+1.29+.67=9.98 degrees of freedom = (rows — 1)(columns — 1) = (3 — 1)(3 — 1) = (2)(2) = 4 x2 critical values for 4 degrees of freedom (see Appendix E).
.01 13.28 .05 9.49 x2 = 9.98 The test indicates that there is a significant relationship between major and number of cigarettes smoked at the .05 but not at the .01 level of significance.

If we wished to answer the question, 11JS there a relation- ship between being a business major and number of cigarettes smoked?"

we would combine the liberal arts and engineering categories and use a x2 table with six rather than nine cells.
Number of Cigarettes Smoked per Day Major Business Nonbusiness Total None 6(12) 24(18) 30 1 —15 6(56) 80(84) 140 More than 15 14(12) 16(18) 30 Total 80 120 200 360 Part Three Data Analysis (30)(80) — 12 — 200 (140)(80) — 56 — 200 (30)(80) 200 — — (12) (30)(120) — 18 — 200 (140)(120) — — 200 (612)2300 (60_56)2 29 — — 12 56 (24 — 18)2 18 — 2 00 — (80 — 84)2 — 19 — 84 (30)(120) — — (18) 200 (14— 12)2 12 — (16 — 18)2 — 22 — 18 x2=3.OO+.29+.33+2.00+.19+.22=6.03 at2df: 9.21 .01 .05 - 5.99 The null hypothesis may be rejected at the .05 but not at the .01 level of significance.
In a 2 x 2 table (4 cells) with 1 degree of freedom, there is a simple formula that eliminates the need to calculate the theoretical frequencies for each cell.

2 = X N[IAD — BCI]2 (A + B)(C + D)(A + C)(B + D) Terms in a 2 x 2 table A C B D Let us use an example employing this formula.

A random sample of auto drivers revealed the relationship between experiences of those who had taken a course in driver education and those who had not.
Had driver's education No driver's education Total Reported Accident 44A 81C 125 No Accident lOB 35D 45 Total 54 116 170 This is a 2 x 2 table with one degree of freedom.
2 — X — 170[((44 x 35) — (10 x 81)112 (54 + 10)(81 + 35)(44 + 81)(10 + 35) Chapter ii Inferential Data Analysis 361 = 170 [f0 — 810fl2 (64)(116)ç125)(45) 170(730)2 41,760,000 — 90,593,000 41,760,000 — 2 17 The x2 value does not equal or exceed the critical 2 value (3.84) necessary to reject the null hypothesis at the .05 level of significance.

There seems to be no significant relationship between completing the course in driver education and the number of individuals who had re- corded auto accidents.
Yate's Correction for Continuity.

In computing a chi square value for a 2 x 2 table with one degree of freedom, the formula is modified when any cell has a frequency count of fewer than 10.

This formula differs from the previous formula.

2 X — BC!

— N{!AD NjZ (A+B)(C+D)(A+C)(B+D) Example: A pharmaceutical company wished to evaluate the effec- tiveness of X-40, a recently developed headache relief pill.
Two randomly selected and assigned samples of patients who com- plained of headaches were given pills.

The experimental group was given six X-40 pills daily and the control group was given six placebos (Or sugar pills) daily, although they thought that they were receiving medication.

After a week they repeated their experience.


Headaches relieved Headaches continued Total X-40 X 30,, 34 Placebo C 408 10 50 Total 70 14 A x2 test using a 2 X 2 table at 1 degree of freedom was applied, with Yate's correction.

Was the effectiveness of the X-40 medication sig- nificant at the .05 level?
2 — 84[I(30 x 10) — (40 x 4)f — 4212 — 84[1300 — 1601 — X — (30 + 40)(4 + 10)(30 + 4)(40 + 10) — 4212 (70)(14)(34)(50) 362 Part Three Data AnaIyss 84(98)2 — 84(9604) — 806,736 1,666,0001,666,000 1,666,000 X = 2 806,736 1,666,000 = The computed x2 is far below the x2 critical value (3.84) necessary for the rejection of the null hypothesis at the .05 level.
The researcher concludes that the null hypothesis is not rejected: there is no significant relationship between the use of X-40 pills at this dosage and headache relief.

Any apparent effectiveness was probably the result of sampling error.

The Mann—Whitney Test The Mann—Whitney U test is designed to test the significance of the difference between two populations, using random samples drawn from the same population.

It is a nonparametric equivalent of the parametric t test.
It may be considered a useful alternative to the t test when the parametric assumptions cannot be met and when the observations are expressed in at least ordinal scale values.

The basic computation is U1, and in experiments using small samples, the significance of an observed U may be determined by the U critical values of the Mann—Whitney tables.
When the size of either of the groups is more than 20, the sampling distribution of U rapidly approaches the normal distribution, and the null hypothesis may be tested with the reference to the z critical values of the normal probability table.

The values of the combined samples, N1 and N2, are ranked from the lowest to the highest rank, irrespective of groups, rank 1 to the lowest score, rank 2 to the next lowest, and so fprth.
Then the ranks of each sample group are summed individually and represented as R1 nd R2.

There are two Us calculated for the formulas: a. b. U2 = N1N2 + N2(N2 + 1) — R2 N1 = number in one group N2 = number in second R1 = sum of ranks in one group R2 = sum of ranks in second group group Chapter 11 Inferential Data Analysis 363 Only one U need be calculated, for the other can be easily computed by the formula U1=N1N2-L12 It is the smaller value of U that is used whenconsulting the Mann— Whitney U table.
The z value of U can be determined by the formula z = N1N2 2 /(N1)(N2)(N1 + N2 + 1) 12 It does not matter which U (the larger or the smaller) is used in the computation of z.

The sign of the z will depend on which is used, but the numerical value will be identical.

Ior example, a teacher wishes to evaluate the effect of two methods of teaching reading to two groups of 20 randomly assigned students, drawn from the same population (see Table 11—4).
The null hypothesis proposed is that there is no significant difference between the performance of the students taught by Method A and the students taught by Method B.

After a period of four months' exposure to the two teaching methods, the scores of the students on a standardized achievement test were re- corded.

All scores were ranked from lowest to highest and the Mann— Whitney test was IAsed to test the null hypothesis at the .05 level of significance.
U1 = (20)(0) + 20(21) — 469.50 = 400 + 210 — 469.50 = 140.50 — U2—N1N2+ N2(N2 + 1) 2 —R2 = (20)(20) + 20(21) — 350.50 = 400 + 210 — 350.50 = 259.50 364 Part Three Data Analysis TABLE 11-4 Performance Scores of Students Taught by Method A or by Method B • • B 49 90 88 76 92 81 55 64 84 51 47 70 66 69 87 74 71 Rank 2 36 33.5 21 37 27 7 11 30 4 1 15 12 14 32 19 16 9 6 18 R2 = 350.5 .

A 50 60 89 94 82 75 63 52 97 95 83 80 77 80 88 78 85 .
79 72 68 N1 = 20 Rank 3 8 35 38 28 20 10 5 40 39 29 25.5 22 25.5 33.5 23 31 24 17 13 61 55 73 N2 = 20 R1 = 469.5 LI1 = N1N2 + N1(N1 + 1) — R Check: U1=N1N2-L12 140.50 = 400 — 259.50 140.50 = 140.50 N1N2 2 + N2 + 1) 12 — —59.5 \/'1366.67 — —59.50 36.97 140.50 — = /(2o)(2o)(41) 12 —1.61 Chapter 11 Inferential Data Analysis 365 Because the observed z value of —1.61 did not equal or exceed the z critical value of 1.96 for a two-tailed test at the .05 level, the null hy- pothesis was not rejected.
The difference was not significant, and the apparent superior performance of the Method A group could well have resulted from sampling error.

For further information on these and other nonparametric tests, we recommend Hollander and Wolfe (1973) and Siegel (1956).

Summary Statistics is an indispensable tool for researchers that enables them to make infer- ences or generalizations about populations from their observations of the character- istics of samples.
Although samples do not dupUcate the characteristics of populations, and although samples from the same population will differ from one another, the nature of their variation is reasonably predictable.

The central limit theorem describes the nature of sample means and enables the researcher to make estimates about population means (parameters) with known probabilities of error.
The pioneering contributions of Sir Ronald Fisher and Karl Pearson to statistics and scientific method, and William Sealy Gosset to small-sampling theory, have made practical the analysis of many of the types of problems encountered in psychology and education as well as in agricultural and biological research, where they were first applied.

Parametric statistical treatment of data is based upon certain assumptions about the nature of distributions and the types of measures used.
Nonparametric statistical treatment makes possible useful inferences without assumptions about the nature of data distributions.

Each type makes a significant contribution to the analysis of data relationships.

Statistical decisions are not made with certainty but are based upon probability estimates.

The central limit theorem, sampling error, variance, the null hypothesis, levels of significance, and one-tailed and two-tailed tests have been explained and illustrated.
Although this treatment has been brief and necessarily Incomplete, the presentation of concepts may help the consumer of research to understand many simple research reports.

Students who aspire to significant research activity, or who wish to Interpret complex research studies with understanding, will need additional background in statistics and experimental design.
They will find it helpful to participate in research seminars and to acquire competence through apprenticeship with scholars who are making contributions to knowledge through their own research activities.

Exercises (Answers In Appendix H) 1.

Why is it stronger logic to be able to reject a negative hypothesis than to try to confirm a positive one?

2.

A statistical test of significance would have no useful purpose in a purely descriptive study in which sampling was not involved.

Do you agree?

Why?
Do you agree?

Why?

366 Part Three Data Analysis 3.

When a statistical test determines that a finding is significant at the .05 probability that the relationship, was level, it indicates that there is merely the result of sampling error.

Do you agree?

Why?

4.

Any hypothesis that can be rejected at the .05 level of significance can surely be rejected at the .01 level.

Do you agree?

5.
Do you agree?

5.

The t critical value necessary for the rejection of a null hypothesis (at a given level of significance and for a given number of degrees of freedom) is higher for a one-tailed test than it is for a two-tailed test.

Do you agree?

Why?

6.

A manufacturer guaranteed that a particular type of steel cable had a mean tensile strength of 2000 pounds with a standard deviation of 200 pounds.

In a shipment, 16 lengths of the cable were submitted to a test for breaking strength.
The mean breaking strength was 1900 pounds.

Using a one-tailed test at the .05 level of significance, determine whether the shipment met the manufacturer's specifications.

7.

Two samples of mathematics students took a standardized engineering aptitude test.

Using a two-tailed test at the .05 level of significance, determine whether the two groups were random samples from the same population.

Group A N=25 =8O S= 8 Group B N=30 3=s8 S= 9 8.


An achievement test in spelling was administered to two randomly se- lected fifth-grade groups of students from two schools.

Test the null hypothesis that there was no significant difference in achievement be- tween the two fifth-grade populations from which the samples were selected at the .05 level of significance.

Use the method of separate variances.

School A N=40 =82 S = 12.60 School B N=45 X=86 S = 14.15 Chapter 11 Inferential Data Analysis 367 9.
One groUp of rats was given a vitamin supplement while the other group received a conventional diet.

The rats were randomly assigned.

Test the hypothesis that the vitamin supplement did not result in increased weight gain for the experimental group.

Use a one-tailed test at the .05 level.

x N=12 S = 15.50g X=140g C N=16 S = 12.20g X=120g 10.

A consumer research agency tested two popular makes of automobiles with similar weight and horsepower.
Eleven car As provided a mean miles per gallon of 24.20 with an S of 1.40, while 11 car Bs provided a mean miles per gallon of 26.30 with an S of 1.74.

Using a two-tailed test at the .05 level, test the null hypothesis that there was no significant difference between the mean gasoline mileage of the two makes of cars.

11.

Calculate the number of degrees of freedom when a. computing the statistical significance of a coefficient of correlation.

b. determining the significance between two means.


c. a 2 x 2 x2 table computation is involved.

d. a 3 x 5 x2 table computation is involved.

12.

In a survey to determine high school students' preference for a soft drink, the results were Brand A Brand B Brand C Boys Girls 25 46 30 22 52 28 Was there any relationship between the brand preference and the gender of the consumers?

13.
13.

A group of 50 college freshmen was randomly assigned to experimental and control groups to determine the effectiveness of a counseling pro- gram upon academic averages.

Use the Mann—Whitney test to test the null hypothesis that there was no difference between the academic per- formance of the experimental and control groups at the .05 level of significance.

Use the data in the following table.


368 Part Three Data Analysis Experimental 2.10 3.00 1.96 2.04 3.27 3.60 3.80 2.75 1.98 2.00 2.98 3.10 3.69 2.66 2.56 2.50 3.77 2.40 3.20 1.71 3.04 2.06 2.86 3.02 1.88 Control 2.01 2.69 3.07 2.14 2.82 2.57 3.44 4.00 3.01 2.55 2.77 3.09 2.72 3.34 2.81 3.05 2.67 1.90 1.70 1.57 1.39 2.09 3.68 2.11 2.83 14.

Compute the t value of the coefficient of correlation: r= +.30 N = 18 15.
Calculate 9 given the following information: a = 11.2 .2 b1= b2= .4 b3= .3 Chapter 11 Inferential Data Analysis 369 X1 = 70 X2 = 60 X3 = 82 References behavioral sciences (2nd ed.).

Hiisdale, NJ: Lawrence Eribaum.

COHEN, J.

& COHEN, P. (1983).

Applied multiple regression correlation analysis for the GLASS, G. V. & HOPKINS, K. D. (1984).

Statistical methods in education and psychology statistical methods.

New York: (2nd.

ed.).

Englewood Cliffs, NJ: Prentice-Hall.

HOLLANDER, M. & WoLFE, D. A.


(1973).

NonparametriC Kix, R. E. (1982).

Experimental design: Procedures forthe behavioral sciences (2nd ed.).

NEFER, J., WASSERMAN, W., & KUTNER, M. H. (1985).

Applied linear statistical models Belmont, CA: Brooks/Cole.

John Wiley.

(2nd ed.).

Homewood, IL: Richard D. Irwin, Inc. SIEGEL, S. (1956).

Non paramet nc statistics for the WINER, B. J.

(1971).

Statistical principles in experimental design (2nd ed.).

New York: Hill.

behavioral sciences.


New York: McGraw- McGraw-Hill.

Additional Readings COCHRAN, W. G. (1977).

Sampling techniques (3rd ed.).

New York: John Wiley.

FERGUSON, G. A.

(1981).

Statistical analysis in psychology and education (5th ed.).

New GUILFORD, J. P. & FRUCHTER, B.

(1978).

Fundamental statistics in psychology and ed- York: McGraw-Hill.

ucation.

New York: Holt, McGraw-Hill.

HAYS, W. L. (1981).

StatisticS (3rd ed.).

New HOPKINS, K. D. & GLASS, C. V. (1978).


Basic NUNNALLY, J. C. (1975).

Introduction to statistics for psychology and education.

New York: Holt, Rinehart & Winston.

statistics for the behavioral sciences (6th ed.).

Englewood Cliffs, NJ: Prentice-Hall.

York: McGraw-Hill.

New York: McGraw-Hill.

NUNNALLY, J. C. (1967).

Psychometric theory.

TATSUOKA, M. M. Q971).

Multivariate analysis.

New York: John Wiley.

YAMARE, T. (1967).

Elementary sampling theory.

Englewood Cliffs, NJ: Prentice-Hall.


12 Computer Data Analysis THE PURPOSE OF THIS CHAFFER is to show how computers can be used in analyzing data.

Computers can perform calculations in just a few seconds that human beings would need weeks to do by hand.

Although com- puters, as we know them today, have been in existence only for approx- imately 40 years, all of our daily lives are affected by them.

The microchip has made possible small computers that are within the financial reach of many Amerftans.
As the price of these small com- puters comes down and their capabilities increase, more homes and small businesses will have computers.

Three of the computer programs pre- sented later in this chapter were run using a large university "main frame" computer.

However, comparable programs are already available for mi- crocomputers, and we have included an example using one.

The Computer The electronic digital computer is one of the most versatile and ingenious developments of the technological age.
It is unlikely that complex modem institutions of business, finance, and government would have developed so rapidly without the contributions of the computer.

To the researcher, the use of the computer to analyze complex data has made complicated research designs practical.

Performing calculations almost at the speed of light, the computer has become one of the most useful research tools in the physical and behavioral sciences as well as in the humanities.
An early predecessor of the modern computer was a mechanical device developed by Charles Babbage, a nineteenth-century English math- 371 372 Part Three Data Analysis ematician.

Late in that century Herman Hollerith, a director of the U.S. Census Bureau, devised a hole-punched card to aid in the more efficient processing of census data.

The punched card was a significant develop- ment, for it has been a very important part of modern computer data processing.
In the mid-1940s, an electrical impulse computer was devised with circuits employing thousands of vacuum tubes.

These computers, which were very large and cumbersome, required a great deal of space.

The heat generated by the vacuum tubes required extensive air-conditioning equip- ment to prevent heat damage, and the uncertain life of the vacuum tubes caused frequent malfunction.
With the development of transistorized components, replacement of the vacuum tubes, miniaturization, increased component reliability, elim- ination of heat dissipation problems, and other improvements, the com- puter has become a much more effective device for the storage, processing, and retrieval of information.

The most advanced current models have incorporated microcircuitry of even more compact size, improved storage capacity, and greater pro- cessing speed.
Functions can be processed in nanoseconds, or bihionths of a second.

Computer technology includes four basic functions: input, storage, control, and output.

In put entails entering information or data into the computer.

This is generally done through a cathode ray tube (CRT) ter- minal (similar to a television) that is connected to and interacts with the computer.
Other possible input methods include the use of punched cards, optical scanning readers that translate printed page information, or mag- netic tape.

Once information is inputted, it is stored for eventual use on magnetic cores, tapes, or disks.

Control of stored information, as well as new input, is achieved through programs written in one of several possible computer languages that are translated by the computer's controller to the computer's assembly (basic) language.
FORTRAN, which has gone through several modifications over the years, is the prevailing language for statistical calculations.

Such directions as do, go to, read, call, write, if, and then are FORTRAN language terms.

Computer centers have many "canned" or preprepared programs to perform a variety of statistical pro- cedures, so the researcher usually does not need to write his or her own.
The output or retrieval process transfers the processed information or data from the computer to the researcher, using one of a number of devices to communicate the results.

The output may be displayed on a CRT screen, printed on paper, or recorded on a tape or disk.

The computer can perform many statistical calculations easily and quickly.
Computation of means, standard deviations, correlation coeffi- cients, t tests, analysis of variance, analysis of covariance, multiple regres- sion, factor analysis, and various nonparametric analyses are just a few of the programs and subprograms that are available at computer centers.

Chapter 12 Computer Data Analysis 373 It has been said that the computer makes no mistakes, but program writers do make mistakes, and any directions given to the computer are faithfully executed.
The computer doesn't think; it can only execute the directions of a thinking person.

If poor data or faulty programs are intro- duced into the computer, the data analysis will be meaningless.

The expression "garbage in, garbage out" describes the problem quite well.

It is critical when one is using canned programs to carefully follow the appropriate program syntax.

If a comma or slash is missing, the program may stop processing the data or, worse yet, process the data incorrectly.
With the large "main frame" computers of university and large busi- ness computer centers, hundreds of users at different terminals can com- municate with the computer at a single time.

Computer programs (soft- ware) are available at these centers for many purposes, including statistical analyses.

The canned programs include the Statistical Package for the Social Sciences1 (SPSS), Statistical Analysis System2 (SAS®), and others.
Though the actual programs, input procedures (syntax), and output (printouts) differ for these package programs, they are similar in their capabilities and the variety of statistical analyses that can be performed using them.

Perhaps the most widely used are those programs published by SPSS.

Which set of programs is used, however, depends on the user's needs and prefer- ence.
Examples of programs from the SPSS-X and SAS systems—using the computer facilities of the University of Illinois at Chicago—will be presented later in this chapter.

Microcomputers include a wide range of equipment from small, low- cost computers for games and other purposes to computers that cost several thousands of dollars and can perform a variety of functions.
De- pending on the model and storage capabilities, there are programs avail- able, SPSS and SAS among them, that can calculate any of the statistical analyses presented in this text, and many more.

An example of a program from SPSS-PC—using an IBM personal computer with a hard disk— will be presented later in this chapter.

Data Organization Prior to the input stage of data analysis comes the organizing of data for proper input into the computer system.
Regardless of the type of computer or program to be used, if data are poorly organized the researcher will have trouble analyzing their meaning.

The data must first be coded.

Categorical data, such as a person's sex or occupation, need to be given a number to represent them.

For instance: SPSS, SPSS-X, and SPSS-PC + are trademarks of SPSS, Inc. of Chicago, Ill., for its proprietary computer software.
2 SAS is the registered trademark of SAS Institute, Inc., Cary, N.C. 374 Part Three Data Analysis Sex 1 = Female 2 = Male Occupation 1 = Farmer 2 = Service 3 = Professional The researcher may also want to convert interval or ratio data into cate- gories and code them.
For instance, IQ Level 1 = 120 to 139 2 = 100 to 119 3 = 80 to 99 4 = 60 to 79 Income 40,000 and over 1 2 = 30,000 to 39,999 3 = 20,000 to 29,999 4 = below 20,000 The next step is to assign each variable to the spaces in which it will always be placed.

Most systems call for a maximum of 80 columns per line.

Once the researcher knows how many spaces each variable will occupy, the variables can be assigned to their column numbers (from 1 to 80).
If more than 80 spaces are needed for each subject, then two or more lines will need to be assigned.

The first columns will usually be the individual subject identity (ID) number.

If less than 100 subjects are in- cluded, two spaces starting with 01 will be needed.

Sometimes a researcher may include one or more attributes into the ID number, thereby increasing the number of columns needed.

For instance, in the data set used in the analysis of variance presented in this chapter, we use a four-digit ID.
The first digit represents the student's major, the second the student's sex, and the third and fourth digits the distinctive ID number.

Thus, subject number 2113 was a philosophy major, male, and the thirteenth subject coded.

When a large number of variables are used in a study, separating the variables with spaces will make the data easier to comprehend and easier to use with some programs.
In any case, the researcher needs to have a list that shows which variables are represented in which column numbers.

Figure 12—i shows how data might look when coded on to a form.

Note how the variables are separated from each other by a space left between them.

Table 12—1 is the list used to determine which columns contained the different variables.

Survey researchers frequently have a system for coding and recording their data prior to distributing the questionnaires.
For example, the ques- tionnaire may ask for the sex of the respondent, "1" for male or "2" for — A I :::::: :::: — - — - - ———— - - - h- - (-— - ; ff1 *i .

r i ; ; - !

c cc!

4 'F'!

- : 5 Sc : : — .'
— 3 ç 01 : 2' r- 10 00 1 Ui Ic,U- 375 376 Part Three Data Analysis TABLE 12—1 Variable List for Coded Data Column Number 1—4 6—7 9—10 12—13 15—16 18—19 21—22 24—25 27—28 30—31 33—34 ID Number Expressive Language (EL) Receptive Language (RL) Object Permanence (OP) Means—End (ME) Vocal Imitation (VI) Gestural Imitation (GI) Causality Spatial Relations Responsibility (ABS9) Socialization (ABS1O) Column Number 36—37 39—40 42—43 45—46 48—49 51—53 Independent Functioning (ABSI) Physical Development (ABS2) Language (ABS4) Self-direction (ABS8) Mental Age (MA) Chronological Age (CA) female.
If data are precoded for all questions and the researcher knows into which columns each answer will be placed, the data may be inputted to the computer directly from the returned survey forms, thereby saving the time of transferring the data to coding forms.

Computer Analysis of Data Once the data are coded, they are ready to be stored in the computer.

The researcher then must decide on the descriptive and inferential statis- tics desired and the program(s) that he or she will use to analyze the data.
The selection of appropriate statistics will generally depend on the design of the study, and the specific program(s) to be used will depend upon the researcher's preference.

Some researchers prefer one of the canned program packages and almost always use just that one.

Others use dif- ferent statistical programs from different packages.

In the following we introduce two of the more popular statistical systems, presenting the control cards and output for two analyses from each.
A large main-frame computer at the University of Illinois at Chicago was used in the first three analyses.

For the fourth analysis, the ANOVA example, an IBM personal computer with a hard disk was used.

In each of these analyses, the data may be presented with the control statements or can be retrieved by appropriate statements from anotherlocation (e.g., disk or tape).

In three of the sample programs, the data are imbedded within the control statements.
In the second analysis, we provide an example of control statements that retrieve the data from a disk.

When data are retrieved in this manner, an additional group of control cards, Chapter 12 Computer Data Analysis 377 known as job control language (JCL) cards, are used to inform the com- puter of the location of the data and other parameters.

Example 1: DescriptIve Statistics—SAS:CORR SAS offers a number of descriptive statistics programs.
The one presented here is called CORR because it includes correlations.

The control cards are presented in Figure 12—2 SAS statements are separated from each other by semicolons (;).

Thus, TITLE tells the program that the following words in single quotes (') comprise the title of this program, and DATA tells the program to read the data into an SAS data set created by and for this program.
The INPUT statement tells the program the names of the variables and where they TLE 'SAS CORRELATION EXAMPLE'; ID EL RL OP NE VI CI CAUS SPA ABS9 48510 4851 4852 A8S4 485$ MA CA LABEL EL— EXPRESSIVE LANGUAGE RI.

— RECEPTIVE LANGUAGE OP • OBJECT PERMANENCE ME — MEANS—END VI — VOCAL IMITATION CAUS — CAUSALITY 4851 — INDEPENDENT FUNCTIONING A144 — LANGUAGE AIS8 — SELF-DIRECTION AUtO — SOCIALIZATION CA — CHRONOLOGICAL AGE NA — MENTAL AGE; GI — GESTURAI.
IMITATION 1 1 4 CARDS 111 31 36 14 13 7 9 7 11 112 22 24 14 13 9 9 2 11 C iS ii ii 2 141 14 17 9 5 0 0 7 10 0 142 10 9 14 12 1 2 2 1 2 143 7 9 8 10 .0 Ii C ROC CORR; VAR OP ME VI GI CAUS MA CA HITH EL RI.
ABSI ABS4 ABS8 ABS1C; .175824171521 FIGURE 12—2 Control Cards for Descriptive Statistics Example Using SAS.
378 Part Three Data Analysis are.

Because the variables in the data set are separated by spaces, we inform the program where they are simply by naming them in the order they appear in the file.

When the input statement is used in this way, missing data must be represented by a period (.)

rather than a blank space since a blank space cannot represent a missing data point and a space between variables.

The next statement, LABEL, gives longer labels to the previously listed variables.
CARDS merely informs the programs that the data are imbedded in the program, rather than in a separate file, and follow this statement.

A semicolon follows the last subject's data to inform the program that all of the data have been read.

PROC CORR tells the SAS system that the procedure known as "correlation" is to be used.

The next two statements request correlation coefficients to be calculated and printed for only certain combinations.
The VAR statement lists the vari- ables desired on the top of the printed correlation matrix, and the WITH statement lists the variables desired on the side of the correlation matrix.

Figure 12—3 shows the output produced by this program.

The num- ber of subjects, mean, standard deviation, sum of the scores, the lowest score and the highest score for each of the variables is presented first.
Below that is the correlation matrix—with the significance level below each correlation—for the VAR variables with the WITH variables.

If the VAR and WITH statements had not been included, a correlation matrix consisting of all of the possible combinations (all 13 variables by all 13 variables) would have resulted.

SAS CONRELATION EXAMPLE VAMIASLE N MEAN STD 0EV 5(84 I4INDRJH MAXIRJH ' '111 jI Jijj jIl 1111111 3I !1M!

11P4!
11P4!

lii II 111 PEARSON CORRELATION COEFFICIENTS / PRO8 R UNDER HO:RHO—0 / N 45 OP ME VI GI CAUS NA CA PRESSIVE LANGUAGE CEPTIVE LANGUAGE ARS1 INDEPENDENT FUNCTIONING AS4 LAJKNJAGE A5SI SELF—DIRECTION A51O SOCIALIZATION 0.

0.0017 0.0001 0o18!
Oo3s49 Oo5$ o.4g9 Oö4 ooon: O2 O.434 O.52 0.84 °o?fl O.58O4 0.45439 0.42313 0.63238 0.47839 0.42373 -0.18458 0.0001 0.0009 0.0031 0.2248 0.0001 0.4fl34 0.39894 0.58317 0.53185 0.45032 0.34414 0.10995 O.000o 0.0064 0.0002 0.4721 O.flQ3l 0.33429 0.4153 0.47595 0.37290 0.20341 0.00550 0.0002 0.0248 0.0004 0.0116 0.1802 0.9714 0.0010 O.4443 0.44701 0.43768 0.55503 0.44135 O.2884 —0.14336 0.0021 0.0001 0.3475 0.0021 0.0001 0.0019 0.0134 0.0001 0.0014 0.0546 FIGURE 12—3 Samp$e SAS PROC CORR Output.
Chapter 12 Computer Data Analysis 379 Example 2: Charting—SAS:CHART Both SPSS-X and SAS systems have very sophisticated graphing options including three-dimensional and, if the printer is capable, color graphics.

The present example demonstrates the Chart procedure, a relatively sim- pie one, from SAS.

The first six lines of the control cards presented in Figure 12—4 are the job control language cards.
These cards inform the computer of the computer time to be allocated and memory required, the type and place of printing desired, that the SAS system will be used, and the name (CANON1 .DATA) and location (disk) of the data set to be used.

The next two lines include the first three SAS statements and are similar to the SAS statements used in Example 1.

The next six lines present a series of "IF.

.

.

THEN" statements.

These statements convert two of the variables to categories.
The first four statements deal with chronological age in months (CA).

Those children with CAs from 36 to 59 months are assigned a score of 1; those with CAs from 60 to 83 months are assigned a score of 2; those with CAs from 84 to 107 months are assigned a score of 3; and those with CAs above 107 are assigned a score of 4.

Because the first digit of the three-digit ID number represents the subject's sex, those children with IDs over 200 are female (F) and those with IDs below 200 are male (M).
The next line (PUT AGE SEX) creates the computer space for the variables of age and sex created in the previous six statements.

The PROC FORMAT and VALUE statements assign more meaningful values to the variable age.

Thus, those children assigned a 1 for CAs from 36 to 59 months are 3 to 4 years old; those children assigned a 2 for CAs from 60 to 83 months are 5 to 6 years old; and so on.
The next two lines request the SAS procedure CHART, a vertical bar graph (VBAR) to be created using the variable age, and the number of children of each sex to be included in the bars of the graph.

The next two lines request another graph using the same SAS procedure.

This time a pie or circular graph in sections is to be created using the variable age.

Z!SI,DEST_u1 RT) I .CANON1.OATA, DISP—SHR I J K I N N Q CA; PROC cHART; PROC cHART; FIGURE 12—4 Control Cards for Charting Example Using SAS.
SAS CHART EXAMPLE BAR CHART 21t 20 18 17 iJ 14 13 4 12 1 11 9 8 6 4 4 4 3 4 2 4 1 J #0*04 11*1*4 IDE 1*1*14 1*0114 *0*04 #0004 110114 IDE 400*04 tipi FFFFF FFFFF FFFFF 3to4 FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF StoA AGE - 4*0*4 1*1*04 M#*14 10*114 141*014 #0*04 1414144414 #0004 4*00*4 #0*04 144041414 NI0*4N #0*04 #00*4 FFFFF FFFFF FFFFF FFFFF FFFFF FFFFF 7to8 141440*4 44400*4 FFFFF FFFFF FFFFF FFFFF 9toll 1SAS CHART EXAMPLE SYMBOL SEX SYMBOL SEX F F N N FREQ PIE CHART OF AGE ** ** * * * *5 a a $ • $ • 5 to A a S * • 5* * 55 a a *5 55 *5*5 5*5* *5* *5* *5* ** ** ** *5*a .
*55* 14 29.17% *5* 3to4 ** ** *5 * ** * * * 21 43.75% * * ** *5 555 *5* *55 sass 555$ + .

.

.

.

.

.

.

.

.

.

.

.

.. .. 10 20.83% *5*5 .

.

.

55*5*5 .

*S** *5*555*5*5* * *55* .

.

3 .

6.25% .. .

.

.

.

.

a a * a *5 * *5 *5 *5* *** 7to8 5* * *5 S * * *5 * * S * * a * * *5 9 to 11 * * .5 * S FIGURE 12—5 Sample SAS PROC CHART Output.

380 Chapter 12 Computer Data Analysis 381 Figure 12—5 presents both of these graphs on what would be two pages of output.
The pie chart gives both the frequency (number of subjects) and the percentage of the total sample in each age range.

The bar chart shows the number of males and females and the total number of children in each age group.

Example 3: Multiple Regresslon—SPSS-X SPSS, Inc. has published more than ten versions of SPSS for main frame computers.

The version used in this example is called SPSS-X.

The current example uses data collected by the second author.
The multiple regression presented uses three Piagetian independent variables (object permanence, means-end, and vocal imitation) to predict a receptive language score (presented as an age in months) of 45 profoundly retarded children.

The first SPSS-X control card in Figure 2—6, TITLE, is used to name the program (SPSSX REGRESSION EXAMPLE).
The DATA LIST cards (two lines are used) tell the program that the data are presented in such a way that each variable is always in the same column (FIXED) and gives the short name and column numbers for each variable (e.g., RL is in columns 9 and 10, OP in columns 12 and 13).

The VARIABLE LABELS card assigns more complete labels to the initially short names.

Next comes the REGRESSION command and its options on the next five lines.
The first of these lines instructs SPSS-X to do a regression analysis and iden- tifies the descriptive statistics that are desired (those automatically calculated plus significance levels).

The second and third lines present the variables to be used in the regression equation and the regression statistics to be calculated.

(R requests the multiple correlation and COEF requests the regression coefficients and beta, weights).
The fourth and fifth lines specify the dependent variable (RL) and request that all of the other variables— the remaining variables are the independent variables—be entered si- multaneously into the prediction equation.

Finally, the program is told to read the data (BEGIN DATA), followed by the data set and two statements informing the program to stop reading data (END DATA) and that there are no more statements (FINISH).

Figure 12—7 presents two pages of output produced by the above example.
The first page shows the descriptive statistics requested.

The mean and standard deviation of each variable and a correlation matrix— showing the Pearson r and its significance level for each pair of variables— are presented.

The second page of output shows the multiple correlation coefficient (MULTIPLE R), its square (R SQUARE), the adjusted R square (which corrects R for sampling error), and the standard error of estimate for predicting a score using the regression equation.
This page also con- 382 Part Three Data Analysis &L EL LX REGEESSION 8LE V E WL2 %JJ susz: I I I fli FIGURE 12—6 Control Cards for Regression Example Using SPSS-X.

tains a table that shows B, standard error.

of B, and Beta for each inde- pendent variable.

The table also presents the constant or intercept and a T and its significance level for the importance of each variable to the prediction of the dependent variable.
These t tests show that only object- permanence and vocal imitation add significantly to the prediction of receptive language.

We could use the information regarding the B weights and the con- stant in a multiple regression equation to predict a profoundly retarded person's receptive language age given his or her vocal imitation, object- permanence, and means-end scores.

= a + b1X1 + b2X2 .

'(RL) = 1.411 + 1 .287(VI) + .793(0?)


+ .385(ME) Chapter 12 Computer Data Analysis 383 —LlstwissD.lst$onofHlssth;Data ass.

MULTIPLE REGRESSION S... M..n Std D.v Lab.I :3fl :L,1 Ai TATION N of Ca... — Corr.latten, 1—tstl.d Sig: 43 RL VI RL OP 1:000 :2 :!

': : : 1: .434 .000 .437 .001 VI ME : :$ :!

:S :M .0I .000 1.000 .944 N 2 LAGUAEE 6 N E S S 1 0 N .

* S S Equat,on Nu,ub.r 1 D.scr.ptlv.

Stat.tc.

ar.

prlntsd on Pag.

Osp.nd.nt Var $abl•.. RI B.glnning Block Nu.bsr Varlabl.
(s) Ent.red on St.p Numb.r 1.. 2.. 3.. 1.

Msthod: Ent.r VI OP ME V0CA 1M!TATI1I 05JET PRMANIiCE MEANS—END MultpI.

R N Squar.

Adju.tsd N Squar.

Standard Error .73784 .54441 .51107 5.71240 F • Varabls VI OP ME (Constant) 16.33089 Sgnf F Var,abl.. n hs Equat,o .0000 B 1.86557 .792428 .384680 1.410578 I SE B .377700 .299O6 .537731 4.940245 1st.

.423740 .341478 .102154 T Sg T 3.404 .0015 2.642 .0116 .71 4184 .284 .7774 —End Block Nurnb.r All r.qu.st.d vartabisa .nt.r.d.
FIGURE 12—7 Sample Regression Output.

The resulting prediction of receptive language would have a standard error of estimate of 5.713.

Example 4: AnalysIs of Varlance—SPSS-PC+ The data used in this example are identical to the data used in Chapter 11 on the analysis of variance (ANOVA), with one important difference.

In the current example, the ten students in each college major are evenly divided into males and females.
Thus, instead of the simple one-way analysis of variance presented in Chapter ii, the current example is a two-way analysis of variance.

The SPSS-PC + control statements are quite similar to the SPSS-X statements (see Figure 12—8).

TITLE is again used to name the program, in this case SPSSPC ANOVA EXAMPLE.
The next two lines (DATA LIST) inform the program that each variable is always presented in the same column (FIXED) and gives the name and location of each variable (i.e., MAJOR in column 1, SEX in column 2, and ABSTR in columns 6 and 7).

VALUE LABELS give names to the categories of each variable.

For in- 384 Part Three Data Analysis fflE $PC ANJXAWtE YLSSEX /SEX 1 ' 2'MA N TA.

I hi li t O!A.

FINISH.


A ABSIR BY MAJOR (1 3) SEX 1 2)/STATISTICS 3 FIGURE 12—8 Control Cards for Analysis of Variance Example Using SPSS-PC +.

stance, a subject with a I for major and a 1 for sex is a female mathematics major.

While in SPSS-X the command to execute a particular statistic (e.g., regression or ANOVA) would appear next, with SPSS-PC +, the data precede this command.

Thus, the next several lines consist of the BEGIN DATA statement, the data themselves, and the END DATA statement.
After the data, the ANOVA command, indicating that we want to calculate an analysis of variance, is presented.

This line also specifies abstract rea- soning (ABSTR) as the dependent variable, major as an independent var- iable with three levels, sex as another independent variable with two levels, and that we wish certain statistics produced (STATISTICS = 3)— the means for each category.

Finally, FINISH tells the program that there are no further commands.
Figure 12—9 shows two pages of output from the analysis of variance program just described.

The first page lists the means (and in parentheses the number of subjects) for the total sample ("population" on the print- out), each major, each sex, and the six cells of the major by sex table.

The second page of output presents the analysis of variance table.
The sources of variation include the total for the two main effects, each of the two main effects (major and sex), the interaction effect of the two inde- pendent variables, the total of the main effects and interaction effect (EXPLAINED), the within-groups or error (RESIDUAL), and the total.

The Chapter 12 Computer Data Analysis 385 SPSSPC ANOVA EXAMPLE *5* CELL MEANS *5* AISTR IYThrR TOTAL pce8.ATIoM (2O NA.JOI C SEX C MAJOR 1 1 2 3 2 3 C SPSSPC ANOVA EXAMPLE 2 9/7/87 SEX 1 2 2O.

21.

23.

24.

14.
21.

23.

24.

14.

*5* ANALYSIS OF VARIANCE *** SPSSPC ANOVA EXAMPLE A8STR BY MAJOR SEX Souro.

of V.rlat4on Na&fsct.

SEX 23flt5igtIOn.

Esp.n.d R..Idual Total SPSSPC ANOVA EXAMPLE Sui, of Squars.

24.300 244.947 174.000 418.947 OF 1 5 24 29 30 Ca... vsr.

prQQS*s.d.

0 CASES ( .0 PCI) w.r.

.

*..ng.

SPSSPC ANOVA EXAMPLE M.sn Squar.

S1gnf of F F 24.300 i:2 i:2 :88 : :I 3.352 .080 4.758 48.993 7.250 14.447 .000 9/7/87 9/7/87 FIGURE 12—9 Sample Analysis of Variance Output.
sum of squares, degrees of freedom (df), and mean squared are presented for each of these sources of variation.

E's and the significance of each F are presented for each of the effects.

Of interest are the F's for the three effects: college major, sex, and the major by sex interaction.

The F for major was found to be 15.094.

Significance levels are carried out to three decimal places.
Thus a signif- icance of 0.000 is less than 0.001 —less than one chance in a thousand that the three groups of students with different majors were observed to differ because of sampling error.

The F for sex was found to be 3.352.

The significance level of 0.080 is not low enough (.05 being the highest acceptable error rate) for us to reject the null hypothesis for the main 386 Part Three Data Analysis effect of sex.
That is, any observed differences between females and males should be considered due to sampling error.

Finally, the F for the inter- action of major and sex was found to be 0.124 with a significance level of 0.884.

Obviously, the null hypothesis for the interaction of these variables is also not rejected.

The reader may be interested in comparing the results of this 3 by 2 (major by sex) analysis of variance with the one-way analysis of variance (major) performed in Chapter 11 on the same data.
The reader should note that the total sum of squares and the sum of squares for major are identical.

However, because this example included an additional main effect and an interaction effect, the residual (within-groups) sum of squares is reduced by the amount explained by these two effects.

Similarly, the degrees of freedom of the residual changes from 27 to 24, with sex (1) and the interaction of sex and major (2) taking up these three degrees of freedom.
Finally, due to the changes in the residual, the F for major (the only one thit could be calculated using a one-way ANOVA) was changed slightly from 14.77 to 15.09.

The purpose of this chapter has been to present the reader with an introduction to the use of computers in the analyses of data.

We have presented four examples that are relatively simple and we hope they have helped the reader to understand the capabilities of computer analyses.
These programs required less than 3 seconds each of computer time and approximately 5 minutes each from the command to execute until the printout was ready.

This time difference is primarily a function of the backlog of printouts waiting at the printers and the time required for printing.

We suggest that students wishing to develop skills in computer data analysis consult their university computer center and the suggested read- ings at the end of this chapter.
Summary Technological advances in the past 25 years have made computers an integral part of the functioning of our society.

Computers and sophisticated "canned" computer programs have become widely available.
The steps in using a computer to calculate statistical analyses are: (1) data organization and coding, (2) storing the data in the computer, (3) selection of appro- priate descriptive and inferential statistics, (4) selection of appropriate programs for the desired statistics, (5) writing of control cards, and (6) execution of the computer program.

This chapter has presented four examples of control cards and output from "canned" programs.
The statistics requested in these examples are relatively simple: a two-way analysis of variance, a multiple regression analysis, a descriptive statistics program, and two relatively simple graphics.

Chapter 12 Computer Data Analysis 387 DIXON, W. D., BROWN, M. B., ENGLEMAN, L., & JENNRICH, R. L. (199O) BMDP manual, 1990 revision.

Los Angeles, CA: University of California Press.

NORLJSIS, M. J.

(1983).

SPSS-X introductory statistics guide.

Chicago, IL: SPSS, NoRusIs, M. J.

(1990).


Advanced statistics users guide.

Chicago, IL: SPSS, Inc. NoRusIs, M. J.

(1990).

Base system users guide.

Chicago, IL: SPSS, Inc. NoRusis, M. J.

(1990).

SPSS/PC + 4.0 base manual.

Chicago, IL: SPSS, Inc. NORUSIS, M. J.

(1990).

SPSS/PC + advanced statistics 4.0.

Chicago, IL: SPSS, Inc. SAS language and procedures, version 6 (1st ed.).

(1990).

Cary, NC: SAS Institute.

SAS language guide for personal computers, release 6.03.

(1988).
(1988).

Cary, NC: SAS Institute.

SAS procedures guide, release 6.03.

(1988).

Cary, NC: SAS Institute.

SAS/STAT users guide, release 6.03.

(1988).

Cary, NC: SAS Institute.

SPSS reference guide.

(1990).

Chicago, IL: SPSS, Inc. Inc. ___ APPENDIX A Statistical Formulas and Symbols Statistical Formulas 1.

> is greater than < is less than 2.

Mean X — N 3.

Mode M0 4.

Percentile rank P. Pr — — (100R — 50) N 5.

Variance a Standard deviation a x = (X — ) a2 = (7 = N /•?
(deviation computation) N — ( X)2 N2 /T!x2 -(X)2 N2 (raw score computation) Glossary of Statistical Symbols a > b a is greater than b b < a b is less than a X arithmetic average sumof X,Yscores N number of scores M0 mode: score that occurs most frequently in a distribution P, percentage of scores that fall below a given value, plus the percentage of space oc- cupied by that score R rank from the top of a distri- bution a2 population variance: mean value of the squared devia- tions from the mean a population standard devia- tion: positive square root of the variance x = (X — M) deviation from the mean 389 Appendix A: Statistical Formulas and Symbols Statistical Formulas 6.
Variance S2 Standard deviation S S2 = Yx2 N—i N-i Glossary of Statistical Symbols S2 variance of a population es- timated from a sample standard deviation of population estimated from a sample S - (deviation computation) 52NX2—(X)2 N(N-i) — /Nx2_(x)2 \( N(N—1) (raw score computation) Variance (S) or standard devia- Dichotomous variable tion (SDV) of a dichotomous van- able an outcome is either-or: plus or minus, true or false, heads or tails N number of events P probability of an outcome S,= NP(i—P) = \/NP(1 — P) S2 — DV — (general formula) 5DV = (when P = .50) 7.
Standard error of the mean (S) SS=/ a7 8.

Standard scores z, T, Z, — X — X z= x or— T = 50± 10(X —) or T = 50 ± lOz Zd, = 500 + lOOz z sigma score T standard score Z1, College Board standard score Appendix A: Statistical Formulas and Symbols 391 Statistical Formulas Glossary of Statistical 9.
Coefficient of correlation (r) Symbols r Pearson product-moment coefficient of correlation — ________ N — N>.XY—(>.X)(Y) rX2(X)2\/NY2(Y)2 p (rho) Spearman difference in ranks coefficient of cor- relation D difference between each pair of ranks Test of the statistical significance of a coefficient of correlation p (rho) = — 6 D2 N(N2 — 1) 10.

Statistical significance of rip r'N — 2 ty12 — — pv'N — 2 '/1 — p2 11.
Regression line slope rise — — r = r the slope expressed in sigma b (z) units the slope of the line expressed in raw scores.

— rise_Y run X (Zy b———— b—rt— \Zx = r () Predicting a Y from a known X when the coefficient of corre- lation is known 12.

Regression equations = a + bX = a + b1X1 + b2X2.

13.

Standard error of estimate 5est 5est = S\/1 — r2 392 Appendix A: Statistical Formulas and Symbols Statistical Formulas Glossary of Statistical Symbols 14.
Standard error of the difference between two means (independ- ent variances; when variances are not equal) 2 ± S-;; = (pooled variances; when variances are equal) 12\f - 1)S + (N2 - 1)S (1 N1+N2—2 15.

Significance of the difference between two means = difference between means standard error of the difference x1-x2 1)S + (N2 — 1)S(i.

+ .L'\ N + N2 — 2 \N1 N2J x1-x2 Is+s 2' s1 \!N1 N2 r\p- 52 16.
Analysis of variance F MSb MS (uncorrelated or unmatched groups) MSb = SSb = )()2 + (X2)2• — ()Q2 N x2 1) x2 2) 17.

Partial correlation r123 — — (r13)(r) V'(l — r3)(1 — rL) Appendix A: Statistical Formulas and Symbols 393 Statistical Formulas 18.

Chi square x2 x2 = — fe df = (frows — 1)(f columns — 1) 2 Glossary of Statistical Symbols f° observed frequencies f expected frequencies df degrees of freedom A'B D N[IAD_BCI—] x2 = (A + B)(C + D)(A + C)(B + D) Computation for a 2 x 2 table 19.
Mann—Whitney test (N> 20) + 1) — R1 ________ LI1 = (N1)(N2) + N1(N LI2 = (N1)(N2) + N2(N2 + 1)_ U- (N1)(N2) 2 /N1N2N1 + N2 + 1) 12 Z = N1 number in one group N2 number in second group R sum of ranks of one group R2 R2 sumofranksofsecondgroup The significance of U is read from the U critical table.

When N> 20, the z computation may be used with the normal probabffity table values.

• APPENDIX B Percentage of Area Lying Between the Mean and.
Successive Standard Deviation Units Under the Normal Curve .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 .0 .0000 .0040 .0080 .0120 .0160 .0199 .0239 .0279 .0319 .0359 .0398 .0438 .0478 .0517 .0557 .0596 .0636 .0675 .0714 .0753 .1 .2 .0793 .0332 .0871 .0910 .0948 .0987 .1026 .1064 .1103 .1141 .3 .1179 .1217 .1255 .1293 .1331 .1368 .1406 .1443 .1480 .1517 .4 .1554 .1591 .1628 .1664 .1700 .1736 .1772 .1808 .1844 .1879 .5 .1915 .1950 .1985 .2019 .2054 .2088 .2123 .2157 .2190 .2224 .6 .2257 .2291 .2324 .2357 .2389 .2422 .2454 .2486 .2517 .2549 .7 .2580 .2611 .2642 .2673 .2704 .2734 .2764 .2794 .2823 .2852 .8 .2881 .2910 .2939 .2967 .2995 .3023 .3051 .3078 .3106 .3133 .9 .3159 .3186 .3212 .3238 .3264 .3290 .3315 .3340 .3365 .3389 1.0 .3413 .3438 .3461 .3485 .3508 .353 1 .3554 .3577 .3599 .3621 1.1 .3643 .3665 .3686 .3708 .3729 .3749 .3770 .3790 .3810 .3830 1.2 .3849 .3869 .3888 .3907 .3925 .3944 .3962 .3980 .3997 .4015 1.3 .4032 .4049 .4066 .4082 .4099 .4115 .4131 .4147 .4162 .4177 1.4 .4192 .4207 .4222 .4236 .4251 .4265 .4279 .4292 .4306 .4319 1.5 .4332 .4345 .4357 .4370 .4383 .4394 .4406 .4418 .4429 .4441 1.6 .4452 .4463 .4474 .4484 .4495 .4505 .45 15 .4525 .4535 .4545 1.7 .4554 .4564 .4573 .4582 .4591 .4599 .4608 .4616 .4625 .4633 1.8 .4641 .4649 .4656 .4664 .4671 .4678 .4686 .4693 .4699 .4706 1.9 .4713 .4719 .4726 .4732 .4738 .4744 .4750 .4756 .4761 .4767 2.0 .4772 .4778 .4783 .4788 .4793 .4798 .4803 .4808 .4812 .4817 .4821 .4826 .4830 .4834 .4838 .4842 .4846 .4850 .4854 .4857 2.1 2.2 .4861 .4864 .4868 .4871 .48.75 .4878 .4881 .4884 .4887 .4890 2.3 .4893 .4896 .4898 .4901 .4904 .4906 .4909 .4911 .4913 .4916 2.4 .4918 .4920 .4922 .4925 .4927 .4929 .4931 .4932 .4934 .4936 2.5 .4938 .4940 .4941 .4943 .4945 .4946 .4948 .4949 .4951 .4552 2.6 .4953 .4955 .4956 .4957 .4959 .4960 .4961 .4962 .4963 .4964 2.7 .4965 .4966 .4967 .4968 .4969 .4970 .4971 .4972 .4973 .4974 2.8 .4974 .4975 .4976 .4977 .4977 .4978 .4979 .4979 .4980 .4981 2.9 .4981 .4982 .4982 .4983 .4984 .4984 .4985 .4985 .4986 .4986 3.0 .4987 395 Appendix B: Mean and
 Successive Standard Deviation Units Example: Between the mean and +1.OOz is 34.13% of the area.
Beten the mean and —.SOz is 19.15% of the area.
APPENDIX Ca _____________ Critical Values for Pearson's Product-Moment Correlation (r) n a=.10 a.05 a=.02 a.01 3 .988 4 .900 5 .805 6 .729 7 .669 8 .622 9 .582 10 .549 .521 11 12 .497 13 .476 14 .458 .441 15 16 .426 17 .412 18 .400 19 .389 20 .378 .369 21 22 .360 23 .352 24 .344 25 .337 26 .330 27 .323 28 .317 29 .311 30 .306 35 .282 40 .264 50 .235 60 .214 70 .198 80 .185 90 .174 100 .165 200 .117 500 .074 .052 .9995 .980 .934 .882 .833 .789 .750 .716 .685 .658 .634 .612 .592 .574 .558 .542 .528 .516 .503 .492 .482 .472 .462 .453 .445 .437 .430 .423 .391 .366 .328 .300 .277 .260 .245 .232 .164 .104 .074 .0233 .9999 .990 .959 .917 .874 .834 .798 .765 .735 .708 .684 .661 .641 .623 .606 .590 .575 .561 .549 .537 .526 .515 .505 .496 .487 .479 .471 .463 .428 .402 .361 .330 .305 .286 .270 .256 .182 .115 .081 .0258 .997 .950 .878 .811 .754 .707 .666 .632 .602 .576 .553 .532 .514 .497 .482 .468 .456 .444 .433 .423 .413 .404 .396 .388 .381 .374 .367 .361 .333 .312 .276 .254 .235 .220 .208 .196 .139 .088 .062 .0196 1,000 10,000 .0164 df This table is abridged from Table 13 in Biometrika Tables for Statisticians, vol.
1, 2nd ed.

New York: Cambridge, 1958.

Edited by E. S. Pearson and H. 0.

Hartley.

Reproduced with the kind permission of the editors and the trustees of Biometrika.
397 APPENDIX D Critical Values of Student's Distribution (t) Two-tailed test level of significance One-tailed test level of significance df .05 .01 63.557 9.925 5.841 4.604 4.032 .05 .01 APPENDIXE Abridged Table of Critical Values for Chi Square df Level of sigrnflcance .01 .05 6.64 9.21 11.34 13.28 15.09 3.84 5.99 7.82 9.49 11.07 12.59 14.07 15.51 16.92 18.31 19.68 21.03 22.36 23.68 25.00 26.30 27.59 28.87 30.14 31.41 32.67 33.92 35.17 36.42 37.65.
APPENDIX F Critical Values of the F Distribution NOMI- --' DF FOR NUMERATOR OF FOR DE- - - - NATOR .10 .05 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 - 5.96
.2.90 Appendix F: Critical Values of the F Distribution OF FOR DE- NOMI- NATOR DF FOR NUMERATOR ——'" 3.68
2.94
x .10 .06 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 Appendix F: Critical Values of the F Distribution OF FOR NUMERATOR 1 2 3 4 .5 .2.59 - 2.79
.1.84 2.19 3.03 1.81 2.15.
2.25.
2.18 405 DF FOR 06- NOM NATOR 17 18 19 20 22 24 26 28 30 40 60 120 200 .10 .05 .01 .10 .06 .01 .10 .05 .01 .10 .06 .01 .10 .06 .01 .10 .05 .01 .10 .05 .01 .10 .06 .01 .10 .05 .01 .10 .06 .01 .10 .05 .01 .10 .05 .01 .10 .06 .01 .10 .05 .01 This table is abridged from Table 18 in Biometrika Tables for Statisticians, vol.

1; 2nd ed.

New York: Cambridge, 1958.

Edited by.E.

S. Pearson and Fl.

0.

Hartley.

Reproduced with the kind permission of the editors and the trustees of Biometrika.
406 Appendix F: Critical Values of the F Distribution OF FOR OE- NOMI- NATOR OP FOR NUMERATOR x 15 20 24 30 40 50 60 100 120 200.500 so 17 16 19 20 22 24 26 28 30 40 60 120 200 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 .10 .05 .01 1.
' .35 2.06
2.31 1.53 1.73 2.21 1.50 1.69 2.13 1.48 1.65 2.06 1.46 1.62 2.01 1.38 1.51 1.80 1.29 1.39 1.60 1.19 1.25 1.38 1.14 1.19 1.28 1.00 1.00 1.00 APPENDIX G Research Course Report Evaluation RESEARCH REPORT EVALUATION FORM Name __________________ Date _______________ Grade + adequate TITLE clear and concise PROBLEM AND HYPOTHESES ___________________ clearly stated specific questions raised clear statement of hypothesis testable hypothesis significance recognized properly delimited assumptions stated important terms defined DATA ANALYSIS perceptive recognition of data relationships effective use of tables effective use of figures concise report of findings appropriate statistical treatment logical analysis SUMMARY problem restated ___________________ questions/hypothesis restated ________ procedures described concisely reported supporting data included conclusions based on data analysis — — inadequate REVIEW OF RELATED LITERATURE adequately covered well-organized important findings noted studies critically examined effectively summarized PROCEDURES described in detail ________________ adequate sample appropriate design variables controlled effective data-gathering instruments or procedures FORM AND STYLE typing spacing margins balance table of contents list of tables list of figures headings paiisasu.. citations/quotations footnotes tables figures bibliography appendix spelling punctuation sentence structure proofreading clear and concise style 407 ___ APPENDIX H. Answers to Statistics Exercises Chapter 10 1.
Agree.

The median could be lower than the mean if a large proportionof the families had low incomes.

2.

Disagree.

The median is that point in a distribution above and below which half of the scores fall.

It may not be the midpoint between the highest and the lowest scores.

3.

M = 55.33 Md = 58.50 4.

M = 75 Md=77 Range = 31 5.

Variance = 41.33 Standard deviation = 6.43 6.

Disagree.

The range does not determine the magnitude of the variance or the standard deviation.
These values indicate how all of the scores, not the most extreme, are clustered about the mean.

7. a. no change b.

+5 c. +5 d. +5 e. no change f. no change 8.

Percentile rank = 93.

9.

M = 72 standard deviation = 6 0 +1 +2 +3 90 84 78 72 z heads 1o.x X —3 —2 —1 66 60 54 Z Z +5 66 58 —3 +9 70 0 61 52 +1.00 —.60 +1.80 0 60 44 68 50 32 —9 —1.80 11. a.

11% b.

89% c. 87 d. 6% e.. +39, 12. a.

119 b.

96 c. 75% f. —.18, g. +2.33, h. —.67,to + .67, i.

32% j.
32% j.

0, d. 107 e. 85to115 409 410 Appendix H: Answers to Statistics Exercises 13.

Tom Donna Harry algebraz —1.00 ± 1.25 history z +.33 + .50 —.17 — .25 d. Torn a. Torn b. Tom e. Donna c. Harry f. Harry 14.

Disagree.

The coefficient of correlation is an indication of the magnitude of the relationship, but does not necessarily indicate a cause-and-effect rela- tionship.

15. rho = +.61 16. r = +.65 17. r = +.53 18. r = —1.00 most incorrect least incorrect most correct least correct rise 19.

Agree.
Agree.

r = — expressed in sigma units b = — expressed in raw scores rise run The value of r cannot exceed ±1.00 The value of b can exceed ±1.00 20.

S = 4.96 21. a.

= 44 b.Y=36 Chapter 11 1.

Confirming a positive hypothesis provides a weak argument, for the con- clusion may be true for other reasons.

It does not preclude the validity of alternative or rival hypotheses.

Rejecting a negative hypothesis employs stronger logic.

2.

Agree.
2.

Agree.

A test of statistical significance provides a basis for accepting or rejecting a sampling error explanation on a probability basis.

Only when a sampling process is involved is a test of significance appropriate.

3.

Agree.

The level of significance determines the probability of a sampling error, rather than a treatment variable explanation.

When a researcher finds an observation significant at the .05 level, he or she is admitting that there is a 5/100 chance of a sampling error explanation.
4.

Disagree.

The .01 alpha level is a much more rigorous criterion than the .05 level.

However, any hypothesis that can be rejected at the .01 level can surely be rejected at the .05 level of significance.

Appendix H: Answers to Statistics Exercises 411 5.

Disagree.

The t critical value for a one-tailed test is lower.

The area of rejection is one side of the normal curve and it is not necessary to go out as far to reach it.
Critical Values for Rejection 2t 1.96 2.58 it 1.64 2.33 .05 .01, level level 6. t —2.00 7. t = 3.49 8. t = 1.38 9. t = 3.77 10. t = 3.13 11. a. N — 1 b. N — 2 c.N+N—2 d. 1 e. 8 12. x2 = 14.06 13. z = .28 14. t = 1.26 15. '

= 24.6 Reject the null hypothesis.

The cable did not meet the manufacturer's specifications.

Reject the null hypothesis.

The means do not behave as sample means from the same population.

Do not reject the null hypothesis.


There was no significant difference between the achievement of the two groups.

Reject the null hypothesis.

The weight gain for the exper- imental group was significant.

Reject the null hypothesis.

The difference in gasoline mile- age was significant.

Reject the null hypothesis.

There seems to be a significant relationship between gender and brand preference.

Do not reject the null hypothesis.


The effect of the coun- seling program did not seem to be statistically significant.

Do not reject the null hypothesis.

The coefficient of cor- relation was not statistically significant.

APPENDIX I Selected Indexes, Abstracts, and Reference Materials References about References There are a number of publications that identify specific references that cover particular areas of knowledge.

American Reference Books Annual.

Bodhan S. Wynar, ed.


Littleton, CO.: Libraries Unlimited, 1970—date.

Most reference books published or distributed in the United States are reviewed.

Reviews, written by more than 200 library specialists, vary in length from 75 to 300 words, and are not cumulated from year to year.

This is probably the most complete and up-to-date reference on references available.

A Guide to Reference Books (10th ed.).

Eugene P. Sheehy, compiler.

Chicago: American Library Association, 1986.


This comprehensive work lists, without evaluation, by subject area; by type, and by author or editor, the most important reference books printed in a number of languages.

A section is devoted to education.

Supplements appear every two or three years.

Wynar, Christine L. Guide to Reference Books for School Media Cen- ters.

Littleton, CO.: Libraries Unlimited, 1976.

475 pp.
475 pp.

This guide includes 2575 entries with evaluative comments on ref- erence books and selection tools for use in elementary schools, junior and senior high schools, and community and junior colleges.

It is indexed by author, subject, and title.

Reference Books Review Index.

Ann Arbor, MI.

: Pierian Press, 1978.

This annotated listing of references issues supplements quarterly.

Booklist.

Chicago: American Library Association, 1905—date.


Published biweekly and cumulated every two years, this reference presents an unbiased critical analysis by expert librarians of atlases, en- cyclopedias, biographical works, dictionaries, and other reference mate- rials in terms of their usefulness and reliability for libraries or homes.

413 414 Appendix I: Selected Indexes, Abstracts, and Reference Materials Cumulative Book Index.

New York: H. W. Wilson Co., 1898—date.


This monthly publication, cumulated semiannually and in one- and two-year cumulations, indexes all books published in the English language by author, title, and subject.

It is helpful in assuring the student that all pertinent books have been covered in his or her searches.

Books in Print Subject Guide to Books in Print.

R. R. Bowker Co., 1948—date.

6 vols.

Vols.

1—3 Authors Vols.


4—6 Titles and Publishers These multivolume comprehensive listings of in-print titles list names of publishers and other publication information.

The Standard Periodicals Directory.

New York: Oxbridge Publishing Co., 1964—date.

Published every other year, this directory of over 30,000 entries cov- ers every type of periodical, with the exception of local newspapers.

Periodicals are defined as publications appearing at least once every two years.
Two hundred classifications are arranged by subject.

An alphabet- ical index is provided.

Ulrich's International Periodicals Directory.

New York: R. R. Bowker Co., 1966—date.

2 vols.

This classified list of more than 57,000 foreign and domestic peri- odicals is arranged by subject and title.

Publication information is pro- vided.

Irregular Serials and Annuals.

An International Directory: Excepting Periodicals Issued More Frequently than Once a Year.

R. R. Bowker Co., 1972—date.


Published biennially, this directory includes more than 20,000 pub- lications.

Sources of Inform ation in the Social Sciences (3rd ed.).

Chicago: Amer- ican Library Association, 1986.

Organized by subject area and indexed by author and title, this work contains a comprehensive listing and brief description of reference books, monographs, and scholarly journals.
Indexes Appendix I: Selected Indexes, Abstracts, and Reference Materials 415 Schorr, Alan E. Government Reference Books: A Biennial Guide to United States Government Publications.

Littleton, CO.: Libraries Un- limited, 1968/69—date.

This guide describes more than 1300 publications.

A periodical index serves much the same purpose as the index of a book or the card file of a library.
Usually listing articles alphabetically under subject, title, and author headings, the sources of periodical articles are indicated.

Readers should read the directions for the use of an index before trying to locate references.

Most indexes provide complete directions, as well as a list of the periodicals covered, the issue dates included, and a key to all abbreviations used.

Education Index.

New York: H. W. Wilson Co., 1929—date.

Published monthly (September through June), and cumulated an- nually.
Canadian Education Index.

Ottawa, Ontario: Canadian Council for Educational Research, 1965—date.

Issued quarterly, this publication indexes periodicals, books, pam- phlets, and reports published in Canada.

Current Contents: Education.

Philadelphia: Institute for Scientific Information and Encyclopedia Britannica Educational Corporation, 1969—date.

Issued weekly, this publication reproduces the table of contents of more than 500 foreign and domestic educational periodicals.
It contains an author index and address directory to facilitate writing for reprints of the articles and to identify the author's organization.

Reprints are available directly from the Institute for Scientific Information.

Current Index to Journals in Education.

Phoenix, AZ.

: Oryx Press, 1969—date.


This index is issued monthly and cumulated semiannually and an- ually, and indexes approximately 20,000 articles each year from more than 700 education and education-related journals, a joint venture with the National Institute of Education.

416 Appendix I: Selected Indexes, Abstracts, and Reference Materials Index of Doctoral Dissertations International.

Ann Arbor, MI.

: Xerox University Microfilms, 1956—date.


Published as the issue 13 of Dissertation Abstracts International each year, this work consolidates into one list all dissertations accepted by American, Canadian, and some European universities during the academic year, as well as those available in microfilm.

It Indexes by author and key words selected from dissertation titles.

Readers' Guide to Periodic Literature: New York: H. W. Wilson Co., 1900—date.
Issued twice each month, Readers' Guide indexes by subject and au- thor articles of a popular and general nature.

Prior to 1929, Readers' Guide covered many of the educational periodicals.

By 1929, the number of educational periodicals had become so great that the Education Index was established as a more specialized guide.

Readers' Guide may be helpful to students in education for finding references to articles in areas outside the field of professional education.
Abridged Readers' Guide to Periodic Literature.

New York: H. W. Wilson Co., 1935—date.

Fifty-six selected periodicals most likely to be found in smaller li- braries are indexed here.

New York Times Index.

New York, 1913—date.

This index is published biweekly with annual cumulation, and it classifies material in the New York Times alphabetically and chronologically under subject, title, person, and organization name.
It is also useful in locating materials in other newspapers because it gives a clue to the date of events.

Complete issues of the New York Times are available in microfilm form in many libraries.

Subject Index to the Christian Science Monitor.

Boston: Christian Science Monitor, 1960—date.

This publication is issued monthly with annual cumulations.

Social Sciences Index.

New York: H. W. Wilson Co., 1974—date.

This guide indexes 263 periodicals.

Humanities Index.
Humanities Index.

New York: H. W. Wilson Co., 1974—date.

Formerly published as Social Sciences and Humanities Index (1965— 1973), the Humanities Index lists 260 periodicals.

These two indexes, each issued Appendix I: Selected Indexes, Abstracts, and Reference Materials 417 quarterly and cumulated annually, index alphabetically by subject and title articles from more than 260 periodicals, including many published outside the United States.

Physical Education/Sports Index.


Albany, NY: Marathon Press, 1978— date.

This quarterly covers more than 100 journals.

Since Education Index and Current Index to Journals in Education cover fewer than 10 physical education journals, these indexes provide an important additional source.

Rehabilitation Literature.

Chicago The National Society for Crip- pled Children and Adults, 1940—date.

Published monthly, this index lists material concerning the physically handicapped.
Abstracts Another type of reference guide is the abstract, review, or digest.

In addition to providing a systemized list of reference sources, it includes a summary of the contents.

Usually the summaries are brief, but in some publications they are presented in greater detail.

Dissertation Abstracts International.

Ann Arbor, ML: Xerox Uni- versity Microfilms, 1955—date.
Dissertations accepted by most universities in the United States and Canada and some in foreign countries are indexed by author and key word.

Libraries or individuals may purchase complete xerographic or mi- crofiche copies of any dissertation.

Master's Abstracts International.

Ann Arbor, MI.

: Xerox University Microfilms, 1962—date.

Issued semiannually, this guide abstracts those master's degree theses that are available on microfilm.

Resources in Education.


Washington, D.C.: Superintendent of Doc- uments, Government Printing Office, 1966—date.

This monthly abstract journal prepared by the National Institute of Education reports new and completed research projects gathered by the 16 Educational Research Information Centers (ERIC).

418 Appendix I: Selected Indexes, Abstracts, and Reference Materials Completed Research in Health, Physical Education and Recreation Including International Sources.
Washington, D.C.: American Alli- ance for Health, Physical Education and Recreation, 1958—date.

Issued annually, this work indexes by subject and title abstracts of studies conducted throughout the world.

Child Development Abstracts and Bibliography.

Chicago: University of Chicago Press, 1927—date.

Issued every four months and cumulated every three years, this publication abstracts more than 20 journals.

Exceptional Child Education Resources.


Arlington, VA.: Council for Exceptional Children, 1969—date.

Issued quarterly, this publication indexes and abstracts books, pe- riodicals, and government documents.

Psychological Abstracts.

Washington, D.C.: American Psychological Association, 1927—date.

Issued bimonthly and indexed annually by subject and author, this publication has excellent signed summaries of psychological research re- ports.
The December issue provides annual cumulative author and subject indexes.

Beginning in 1963, each issue is also indexed by both subject and author.

Libraries may also provide a cumulative subject index (1927—1960) and a cumulative author index (1927—1963).

Annual Review of Psychology.

Palo Alto, CA.

: Annual Reviews, 1950— date.

Each issue of this annual volume contains critical reviews of the literature in some 15 topical areas of contemporary psychology.
Each review is written by a recognized authority on the topic.

Although dif- ferent authors writing in different years may vary considerably in their interpretation and handling of the same topic, all aim for comprehensive coverage of new developments.

Psychological Bulletin.

Washington, D.C.: American Psychological Association, 1904—date.

Issued bimonthly, the Bulletin evaluates reviews of research literature and methodology.

Sociological Abstracts.

San Diego, CA.


: Sociological Abstracts, Inc., 1952—date.

Appendix I: Selected Indexes, Abstracts, and Reference Materials 419 Issued five times a year and cumulated annually, the Abstracts cover all areas of sociology, including educational sociology.

The work abstracts articles and presents book reviews from several hundred periodicals, both domestic and foreign.

Social Work Research and Abstracts.

New York: National Association of Social Workers, 1965—date.
Published quarterly, this volume indexes by subject, title, and author.

It combines published research with the previously published journal, Abstracts for Social Workers.

National School Law Reporter.

New London, CT.: Croft Educational Services, 1955—date.

The biweekly publication abstracts court decisions on school law.

Research-Oriented Periodicals There are many publications in education and in closely related areas that report research activity.
Some of these publications are exdusively research- oriented.

Others present both research reports and feature-type articles.

It is possible that beginning researchers may not be familiar with many of the specialized publications that deal with a problem area selected.

Browsing through these periodicals provides an effective introduction to the field.

It is also possible that the student may find recent and current reports that have not yet appeared in the appropriate index.
The following list of periodicals may be helpful to those who are planning a research project.

Education Administrative Science Quarterly Adolescence Adult Education.
Adult Jewish Education Alberta Journal of Educational Research American Association of University Pro- fessors Bulletin American Behavioral Scientist American Biology Teacher American Education American Educational Research Journal American Vocational Journal Arbitration in the Schools Arithmetic Teacher Audio-Visual Communications Review Audio-Visual Language Journal Black Scholar Bulletin of the National Association of Secondary Schools Principals Business Education Forum Business Education Quarterly California Journal of Educational Re- search Catholic Educational Review Character Education Journal Child Care Quarterly Child Development Child Study Journal 420 Appendix I: Selected Indexes, Abstracts, and Reference Matenals Child Welfare Children Today Childhood Education Civil Rights Digest Clearing House College Board Review Colorado Journal of Educational Research Community and Junior College Journal Comparative Education Comparative Education Review Computers and Education Continuing Education Convergence Education and Urban Society Educational Administration Quarterly Educational Forum Educational Leadership Educational Record Educational Researcher Educational Research Quarterly Educational Technology Elementary School Journal Evaluation Quarterly Harvard Educational Review High School Journal History of Education Quarterly Home Economics Research Journal Human Development Illinois School Research Independent School Bulletin Indian Historian Integrated Education International Journal of Aging and Hu- man Development International Journal of Educational Sci- •ence Jewish Education Journal for Research in Mathematics Ed- ucation Journal for the Study of Religion Journal of Afro-American Issues Journal of Alcohol and Drug Education Journal of American Indian Education Journal of Business Education Journal of Communication Journal of Computer-Base1 Instruction Journal of Creative Behavior Journal of Drug Education Journal of Educational Data Processing Journal of Educational Measurement Journal of Educational
 Research Journal of Educational Statistics Journal of Experimental Education Journal of Higher Education Journal of Home Economics Journal of Industrial Teacher Education Journal of Law and Education Journal of Legal Education Journal of Leisure Research Journal of Library Research Journal of Negro Education Journal of Religion Journal of Research and Development in Education Journal of Research in Mathematics Edu- cation Journal of Research in Music Education Journal of Research in Science Teaching Journal of Social Studies Research Journal of Teacher Education Junior College Education Junior College Jiurnal Kappa Delta Pi Record Library Resources and Technical Services Library Quarterly Mathematics Teacher Measurement in Education Merrill Palmer Quarterly Microfilm Review Modern Language Journal Multivariate Behavioral Research National Business Education Quarterly National Catholic Educational Associa- tion Bulletin National Education Association Research Bulletin National Elementary Principal National Society for Programmed In- struction Journal Nejro Educational Review New England Association Quarterly North Central Association Quarterly Outlook Peabody Journal of Education Phi Delta Kappan Phylon Pollution Abstracts Practical Application of Research Programmed Instruction Psychometrika Public Opinion Quarterly Religion Teachers Journal Religious Education Research in Higher Education Research in the Teaching of English Review of Educational Research Appendix I: Selected Indexes, Abstracts, and Reference Materials 421 Review of Religious Research School and Society School Law Journal School Law Reporter School Review School Science and Mathematics Science Science Education Science and Children Science Teacher Social Education Social Science Research Speech Monographs Speech Teacher Teachers College Record Theory and Research in Social Education Theory into Practice Times Educational Supplement UCLA Educator Visual Education Young Children Sociology American
 Anthropologist American Behavioral Scientist American Journal of Sociology American Sociological Review Ethnology Federal Probation Human Relations Journal of American Indian Education Journal of Applied Behavioral Science Journal of Correctional Education Journal of Educational Sociology Journal of Experimental Social Psychology Journal of Marriage and the Family Journal of Research in Crime and Delin- quency Rural Sociology Social Behavior and Personality Social Case Work Social Education Social Forces Social Problems Social Psychology Social Work Sociological Methods and Research Sociological Record Sociology of Education Sociology and Social Research Sociometry Teaching Sociology Urban Education Urban Review Psychology American Journal of Orthopsychiatry American Journal of Psychiatry American Journal of Psychology American Psychologist Applied Psychological Measurement Behavioral Disorders British Journal of Educational Psychology British Journal of Psychology Catholic Psychological Record Cognitive Psychology Contemporary Educational Psychology Educational and Psychological Measure- ment Genetic Psychology Monographs Journal of Abnormal Psychology Journal of Applied Psychology Journal of Autism and Childhood Schizo- phrenia Journal of Clinical Psychology Journal of Comparative and Physiological Psychology Journal of Consulting and Clinical Psychology Journal of Counseling Psychology Journal of Creative Behavior Journal of Educational Psychology Journal of Experimental Child Psychology Journal of General Psychology Journal of Genetic Psychology Journal of Humanistic Psychology Journal of Mental and Nervous Disease Journal of Personality Journal of Personality and Social Psychol- ogy Journal of Personal Assessment Journal of Psychiatric Research Journal of Psychology Journal of Research in Personality Journal of School Psychology Journal of Social Psychology Journal of Verbal Learning and Behavior Learning and Motivation Mental Hygiene Pastoral Psychology 422
 Appendix I: Selected Indexes, Abstracts, and Reference Materials Perceptual and Motor Skills Personnel Psychology Psychiatry Psychoanalytic Quarterly Psychological Abstracts Psychological Bulletin Psychological Monographs Psychological Record Psychological Reports Psychological Review Psychology in the Schools Psychology of Women Quarterly Small Group Behavior Transactional Analysis Journal Health and Physical Education American Journal of Nursing American Journal of Occupational Therapy American Journal of Physical Medicine American Journal of Public Health Athletic Journal Health and Education Journal Health Education Journal of the American Dietetic Associa- tion Journal of the American Medical Associa- tion Journal of the American Physical Therapy Association Journal of Clinical Nutrition Journal of Continuing Education in Nurs- ing Journal of Drug Education Journal of Health and Social Behavior Journal of Health, Physical Education and Recreation - Journal of Medical Education Journal of Mental Health Journal of Nursing Education Journal of Nutrition Journal of Pediatrics Journal of Rehabilitation Journal of School Health Nursing Mirror Nursing Outlook Nursing Times Nutrition Today Quarterly Review of Pediatrics Registered Nurse Research Quarterly of the American Alli- ance for Health, Physical Education and Recreation School Health Review Guidance and Counseling American Vocational Journal British Journal of Guidance and Counsel- ing California Personnel and Guidance Asso- ciation Journal Canadian Counsellor Counselor Education and Supervision Elementary School Guidance and Counsel- ing Focus on Guidance Guidance Clinic Measurement and Evaluation in Guidance Personnel and Guidance Journal School Counselor School Guidance Worker Vocational Guidance Quarterly Special Education Academic Therapy American Annals of the Deaf American Journal of Mental Deficiency Braille Book Review Education and Training of the Mentally Retarded Education of the Visually
 Handicapped Exceptional Children Exceptional Parent Focus on Exceptional Children Gifted Child Quarterly Gifted Pupil Hearing and Speech Action International Journal for the Education of the Blind Journal of Learning Disabilities Journal of Mental Deficiency Research Journal of Special Education Journal of Speech and Hearing Disorders Journal of Speech and Hearing Research Language, Speech and Hearing Services in Schools Learning Disorders Mental Retardation Appendix I: Selected Indexes, Abstracts, and Reference Materials 423 New Outlook for the Blind Sight Saving Review Special Education Teacher of the Blind Teaching Exceptional Children Training School Bulletin Volta Review Reading American Journal of Optometry Elementary English English Journal Initial Teaching Alphabet Bulletin Journal of the Association for the Study of Perception Journal of Reading Journal of Reading Behavior Journalism Quarterly Reading Horizons Reading Improvement Reading Quarterly Reading Research Quarterly Reading Teacher Reading World ___AUTHOR INDEX Alberto, P., 171, 181 Alevios, P., 268 Allen, K., 170, 181 Alverson, L., 268 Anastasia, A., 212, 267 Anderson, C., 131 Anderson, D., 123, 130 Anderson, R., 153, 164 Appel, M., 29 Aydelotte, W., 90, 102 Babbie, E., 110, 130, 242, 268 Backstrom, H., 131 Ballou, S., 80 Barlow, D., 165, 170, 171, 172, 173, 177, 181 Barton, M., 127, 130 Barzun,J., 30, 103 Bellack, A., 59 Berkmans, T., 253, 268 Bernoff, R., 29 Best, J., 85, 86, 102 Bijou, S., 170, 181 Billington, R., 86, 102 Bloom, B., 115, 130 Bogardus, E., 259, 267 Bonney, M., 268 Brickman, W., 86, 89, 99, 103 Broadneck, M., 30 Bromley, D., 193, 205 Brown, M., 387 Brownlee-Conyers, J., 206 Burns, C., 153, 164 Burstein, L., 131 Campbell, D., 140, 141, 144, 146, 151, 164 Campbell, E., 130 Campbell, T., 269 Campbell, W., 80 Cannell, C., 253, 268, 269 Caracelli, V., 132 Carr, H., 103 Cartwright, C., 268 Cartwright, G., 268 Chall, J., 131 Charters, W., 112, 130 Chave, E., 246, 268 Clayton, H., 194,
 205 Clement, P., 175, 181, 222, 227, 268 Cochran, W., 369 Cohe>n, J., 356, 369 Cohen, P., 356, 369 Coleman, J., 125, 130, 132 Combs, J., 119, 130, 132 Conant, J., 30 Cook, T., 140, 146, 151, 164 Cooley, W., 119, 130, 132 Cooper, J., 171, 181 Copperman, P., 132 Crandall, R., 58 Cremin, L., 88, 103 Cronbach, L., 212, 267, Crowson, R., 112, 131, 268 196, 206 115, 116, 131 Darling-Hammond, L., Davies, A., 96, 103 Denny, J., 206 Dewey, J., 6, 29, 30 Diener, E., 58 Diethorn, B., 87, 103 Dillon, H., 118, 130 Dixon, W., 387 Drake, S., 194, 205 Ebel, R., 30, 268 Edgerton, H., 103 Edwards, A., 246, 268 Eisner, E., 206 Elam, S., 110, 130 425 426 Author Index Engleman, L., 387 Ennis, H., 132 Ennis, R., 58 Erickson, F., 183, 205 Evertson, C., 206 Fantuzzo, J., 175, 181, 222, 227, 268 Feigi, H., 30 Fenlason, A., 268 Ferguson, G., 273, 307, 320, 369 Finley, M., 90, 103 Fisher, R., 14, 29 Plannigan, J., 132 Frank, C., 30 Frey, P., 123, 130 Fruchter, 5., 273, 321, 369 Gabel, D., 10, 29 Gage, N., 58 Garraghan, G., 103 Gebhard, P., 131 Gesell, A., 224, 268 Glass, G., 127, 129, 130, 132, 140, 153, 161, 164, 273, 276, 307, 308, 309, 320, 327, 349, 351, 352, 369 Goertwitz, J., 132 Goldberg, M., 132 Gottschalk, L., 86, 90, 103 Graff, H., 103 Green, J., 132, 206 Gronlund, N., 268 Grover, S., 123, 130 Cuba, 5., 184, 205, 206 Guenzel, P, 253, 268 Guilford, J., 273, 321, 369 Hakel, M., 233, 268 Hall, H., 155, 164, 222, 268 Hare, V., 149, 164 Harris, D., 229, 268 Harris, F., 170, 181 Hartshorne, H., 258, 268 Hastings, J., 115, 130 Hawkins, G., 99, 103 Hays, W., 30 3A0, 164, Z73 321, 369 Hechinge 1., 112 130 Hechinger, & 112, 130 Helmstadter, C., 30 Henderson, K., 132 Henry, T., 194, 206 Heron, T., 171, 181 Herriott, R., 268 Herson, M., 165, 170, 171, 172, 173, 177, 181 Hess, R., 132 Heward, W., 171, 181 Hirschi, T., 132 Hobson, C., 130 Hockett, H., 86, 100, 103 Hodges, W., 153, 164 Hollander, M., 365, 369 Hollingshead, A., 194, 205 Hopkins, K., 140, 161, 164, 269, 273, 307, 308, 309, 320, 327,
 349, 351, 352, 369 House, E., 153,164 Howe, A., 29 Howe, H., 58 Hudson, L., 115, 116, 131 Hursh, D., 131 Hurwitz, E., 112, 131, 1%, 206 Hyman, H., 132, 269 Iwamoto, D., 153, 164 Jackson, C., 132 Jacob, E., 188, 205 Jaeger, R., 206 Jennrich, R., 387 Johnston, M., 170, 181 Jones, W., 30 Kahn, J., 10, 12, 29, 63, 80, 108, 131, 136,164 Kahn, R., 269 Kamii, C., 10, 29 Kaplan, A., 30 Karplus, R., 10, 29 Kaufman, R., 114, 115, 131 Kazdin, A., 165, 166, 171, 172, 173, 174, 177, 178, 179, 181, 225, 268 Kenny, K., 246, 268 Kerlinger, F., 140, 164, 269 Kinsey, A., 108, 131 Kirby, S., 115, 116, 131 Kirk, R., 140, 161, 164, 273, 321, 349, 351, 369 Kish, L., 269 Klein, A., 103 Koefod, P., 80 Kohlberg, L., 37, 58 Krantz, P., 177, 181 Kuhn, T., 30 Kutner, M., 356, 369 Lang, R., 108, 131 Lannie, V., 87, 103 Lavatelli, C., 10, 29 Lawson, A., 29 Lesser, S., 132 Levin, S., 123, 130 Lincoln, Y., 184, 205, 206 Lohnes, P., 132 Lomax, R., 149, 164 Lucas, R., 205 Lunney, G., 276, 321 Lunt, P., 194, 206 Lynd, H., 194, 205, 206 Lyñd, R., 194, 205, 206 Maldaus, C., 115, 130 Mandeville, G., 276, 321 Marshall, C., 187, 190, 198, 202, 206 Martin, C., 131 Massey, R., 164, 268 May, M., 258, 268 Mayer, C., 171, 181 Mayo, E., 135, 164 McClannahan, L., 177, 181 McGee, G., 177, 181 McKay, H., 108, 131 McLean, L., 153, 164 McPortland, J., 130 Mead, M., 196, 206 Mees, C., 3, 29 Mill, J., 134, 164 Minton, J., 122, 131 Mitchell, J., 221, 268 Moehlman, A., 103 Mood, A., 130 Mooney, R., 216, 268 Moreno, J., 269 Morris, V., 112, 131, 196, 197, 206 Mosteller, F., 127, 131 Moynihan, D., 127, 131 Myrdal, G., 108, 131 Author Index 427 Neter, J., 356, 369 Nevins, A., 91, 103 Nordin, V., 103 Norrusis, M., 387 Nucci, L., 160, 164 Nucd, M., 160, 164 Nunnaly, J., 269, 369 Oppenheim, A., 269 Osgood, C., 250, 268 Passow, A., 132 Patton, M., 184, 185, 188, 189, 190, 198, 199, 200—201, 203, 206 Peckham, P., 276, 320 Peizer, S., 164, 268 Perrin, P., 80 Peterson, C., 103 Peterson, R., 170, 181 Piaget, J., 10, 29 Poisky,
 R., 132 Pomeroy, W., 131 Popham, W., 132 Porter-Cehrie, C., 112, 131, 196, 206 Price, A., 164, 268 Proper, E., 153, 164 Raph, J., 132 Reagan, M., 30 Rogers, J., 132 Rossman, C., 187, Rusch, J., 29 Ruttman, L., 115, 131 Ryans, D., 132 190, 198, 202, 206 Sanders, J., 132, 276, 320 Schwartz, R., 269 Scott, W., 206 Search, P., 87, 103 Sechrest, L., 269 See, H., 240; 268 Selvin, H., 132 Shaw, C., 108, 131 Sherman, M., 194, 206 Shinedling, M., 164, 268 Siegel, S., 273, 321, 365, 369 Skinner, B., 30, 166, 181 Slavin, R., 30 Smith, M., 127, 130 428 Author Index Smylie, M., 206 Snow, C., 30 Snow, R., 11, 29 St. John, N., 132 St. Pierre, R., 153, 164 Stake, R., 206 Stanley, J., 140, 141, 144, 146, 164, 269 Stayer, J., 10, 29 Stebbins, C., 104 Stebbins, L.,153, 164 Stephenson, W., 254, 268 Strachey, J., 194, 206 Strunk, W., 80 Suci, C., 250, 268 Sullivan, F., 29 Sulzer-Azaroff, B., 171, 181 Summers, G., 269 Tannenbaum, P., 250, 268 Tatsuoko, M., 369 Thomas, S., 114, 115, 131 Thursfield, R., 88, 103 Thurstone, L., 246, 268 Torgeson, W., 269 Torney, J., 132 Torsten, H., 111, 131 Travers, R., 59 Troutntan, A., 171, 181 Turabian, K., 80 Turner, W., 103 Walberg, H., 127, 131, 308, 321 Walker, D., 153, 164 Waples, D., 112, 130 Ward, A., 59 Warner, W., 194, 206 Warren, D., 85, 86, 103 Wasserman, W., 356, 369 Watanabe, P., 149, 164 Webb, E., 269 Weinfeld, F., 130 West, J., 194, 206 Westbury, I., 59 White, E., 80 White, J., 99, 103 Wilson, S., 206 Winer, B., 140, 161, 164, 273, 321, 350, 351, 369 Wisler, C., 153, 164 Wittrock, M., 59 Witty, P. 108, 131 Wolfe, D., 365, 369 Wolinsky, G., 10, 29 Wolman, W., 29 Worthen, B., 132 Wynn, R., 59 Yamare, T., 369 Yates, F., 14, 29 Yin, R., 206 York, R., 130 Zigler, E., 59 Zirkel, P., 104 SUBJECT INDEX A-B-A designs, 173—175 Academic research problem (project), 32—38 list of topics, 35—36 Achievement tests, 213—214 Action research, 24—25 Activity analysis, 112 Alpha level, 330—331 Alternating treatment designs, 177 American Psychological
 Association Committee on Ethical Standards, 44—45 American Psychological Association's PublicatiOn Manual, 62, 69—71 Analysis of covariance (ANCOVA), 140, 351—352 Analysis of variance (ANOVA), 345—351 between group variance, 346—347 computer program, 383—386 within-groups variance, 346—347 Applied behavior analysis, 166 Applied research, 23 Aptitude tests, 214—215 Area or cluster samples, 17—18 Aristotle, 4 Artificiality of the experimental setting (as threat to external experimental validity), 145 Assessment, 25, 105—106 single-subject research, 171—173 Assessment studies, 107—112 Assumptions, 40 Attitude scales (see Opinionnaires) Attribute variable, 137 Bacon, Francis, 4 Baselines, 167—169 Basic research, 23 Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research, 43 Beta weights, 354 Bias experimenter, 143—144 selection, 143 Bimodal distributions, 290 Blind, 144 Boyle's Law, 134 Case study, 193—195 Causal comparative research, 26, 120—123, 127—128 Central limit theorem, 324—327 Charles' Law, 134 Chi square, 357—362 Yate's correction for continuity, 361—362 Coefficient of determination, 308— 309, 356 Coleman Report (Equality of Educational Opportunity Study), 125—127 Computer, 371—373 mainframe, 373 microcomputers, 373 sample programs, 377—386 Confidence interval, 327 Confidentiality, ethics in human experimentation, 46 Confounding variable, 137—138 controlling for the effects of, 138—140 extraneous, 138 intervening, 138 429 430 Subject Index Consent, informed, ethics in human experimentation, 45 Constructs, 8 Construct validity, 219 Control group, 12, 14, 135—136 Correlation, 297—314 interpretation of, 308—309 misinterpretation of, 309 Pearson product-moment, 302—305 phi, 307 prediction using, 309—314 rank order (rho), 305—307 Correlational research, 120 Counterbalanced designs, 158 Darwin, Charles, 5 Data organization, 260—265, 277—278 arrays, 277 computer use, 373—376 ranking and weighting, 264—265 sorting and
 tabulating, 262—263 Deductive-inductive method, 5 Deductive reasoning, 4—5 Degrees of freedom, 334—335 Delimitations, 40 Dependent variable, 137 Descriptive research, 25—26 definition, 106, 120—124 Descriptive statistics, 276, 278—315 caution, 314—315 computer program, 377—378 measures of central tendency, 279—283 (see also Mean, Median; Mode) measures of relationship, 297—314 (see also Correlation) measures of spread or dispersion 283—287 (see also Range; Standard deviation; Variance), standard scores, 293—297 (see also Percentile rank; T score; Z score) Dewey, John, 6, 9 Document or content analysis, 191— 193 Double blind, 144 Educational research, 26—27 Equality of educational opportunity study (Coleman Report), 125—127 Equivalent materials, pretest-posttest design, 155—158 Equivalent time-samples design, 154—155 Error variance, 139 Essay on Population, 5 Ethical Principles in the Conduct of Research with Human Participants (APA), 45 Ethics in human experimentation, 43—47 confidentiality, 46 informed consent, 45 invasion of privacy, 46 knowledge of outcome, 46 protection from stress, harm, or danger, 46 Ethnography, 188, 195—198 Evaluating a research report, 78—79 Evaluation, 25—26 definition, 106 formative, 115 summative, 115 Evaluation research, 115 Evaluation studies, 113—116 Experimental design, 146—162 Experimental group, 12, 14, 135—137 Experimental mortality (as threat to internal experimental validity), 143 Experimental research, defined, 27 Experimental validity, 140—146 external, 140—141, 144—146 internal 140—144 Experimenter bias (as threat to internal experimental validity), 143—144 Ex post facto research, 26, 120—123, 127—128 External experimental validity, 144—146 artificiality of the experimental setting, 145 interaction effect of testing, 145 interaction of selection and treatment, 145 interference of prior treatment, 144 treatment verification, 145 Extraneous variable, 138 control of, 138—140 F ratio, 346—350 Factorial designs, 159—162 'interaction
 effects, 160—161 main effects, 159 Figures, 73—78 Fisher, Sir Ronald, 135, 347 Follow Through Planned Variation study, 151—153 Follow-up study, 118—119 Format of research report (see Research report, format) Fundamental research, 23 Gosset, William Sealy, 19 Hawthorne effect, 136 Historical criticism, 95—99 external, 95—96 internal, 96 Historical research, 26, 85—101 definition, 26 difficulties, 92—93 generalization, 89—91 hypothesis, 91—92 sources of data, 93—95 topics, 99—100 writing the report, 100—101 History (as threat to internal experimental validity), 141—142 Homogeneity of variance, 338—341 Hypothesis, 5, 6, 7, 11, 41—42 historical, 91—92 null, 12—13, 329—330 research 11—12, 42 Independent variable, 137 attribute, 137 organismic, 137 treatment, 137 Inductive reasoning, 4—5 Subject Index 431 Inferential statistics, 276, 323—365 (see also Analysis of covariance; Analysis of variance; Chi square; Mann—Whitney test; Multiple regression; Standard error of the mean; t test) Informed consent, ethics in human experimentation, 45 Inquiry forms, 229—251 (see also Opinionnaire; Questionnaire) Interaction effect of testing (as threat to external experimental validity), 145 Interaction of selection and maturation (as threat to internal experimental validity), 143 Interaction of selection and treatment (as threat to external experimental validity), 145 Interest inventories, 215 Interference of prior treatment (as threat to external experimental validity), 144 Internal experimental validity, 140— 144 experimental mortality, 143 experimenter bias, 143—144 history, 141—142 interaction of selection and matur- ation, 143 maturation, 141 selection bias, 143 statistical regression, 142—143 testing, 142 unstable instrumentation, 142 Interval scale, 209—210, 275 Intervening variable, 138 Interview, 199—202, 251—254 reliability, 253—254 validity, 253—254 Invasion of privacy, ethics in human experimentation, 46 Knowledge of outcome, ethics in human experimentation, 46 432 Subject
 Index • Law of the single variable, 134—135 Library, use of, 47—51 Likert method, 246—250 Limitations, 40 Major premise, 4 Mann—Whitney test, 362—365 Matching, 139—140 Maturation (as threat to internal experimental validity), 141 Mean, 279—280 Median, 280—282 Meta-analysis, 127 Method of difference, 134 Mill, John Stuart, 134 Minor premise, 4 Mode, 282—283 Mortality (as threat to internal experimental validity), 143 Multiple baseline designs, 175—177 Multiple correlation, 356 Multiple regression, 352—356 computer program, 381—383 National Assessment of Educational Progress (NAEP), 25, 111 Nominal scale, 208—209, 275 Nonnormal distributions, 290 bimodal, 290 skewed, 290 Nonparametric data, defined, 275 appropriate test for use with, 356—365 (see also Chi square; Mann— Whitney test) Nonprobability samples, 18—19 Normal distribution, 287—292 interpreting, 291 practical applications, 292 (see also Nonnormal distributions) Note taking, 49—51 Novum Organum, 4—5 Null hypothesis, 12—13, 329—330 Observation, 198—199, 221—229 characteristics of good, 229 rating scale, 227—228 recording, 226 reliability, 225—226 validity, 225 One group, pretest—posttest design, 147—148 One-shot case study, 147 Operational definitions, 10—11 Opinionnaire, 245—251 Likert method, 246—250 semantic differential, 250—251 Thurstone technique, 246 Ordinal scale, 209, 275 Organismic variable, 137 Parameter, 13, 276 Parametric data appropriate tests for use with, 327— 356 defined, 275 Partial correlation, 351—352 Percentile rank, 295—297 Personality inventories, 215—216 Piaget, Jean, theory of cognitive development, 10 Placebo group,, 135—136 Population, 13, 276 Post hoc analyses (following ANOVA), 349-351 Post hoc fallacy, 127—128 Posttest only, equivalent-groups design, 148—149 Pre-experimental designs, 146, 147—148 one group, pretest—poSttest design, 147—148 one-shot case study, 147 static group comparison design, 148 Pretest—posttest, equivalent groups design, 149—150 Pretest—posttest nonequivalent
 groups design, 151—154 Probability sampling, 110 Program evaluation, 114—116 Programmatic research, 6 Projective tests, 216—217 Project Talent, 118—119 Protection from physical and mental stress, ethics in human experimentations 46 Isychological tests and inventories, 212—221 achievement, 213—214 aptitude, 214—215 interest, 215 personality, 215—216 projective, 216—217 qualities of good, 217—221 reliability, 217—218 validity, 218—220 Q methodology, 254—256 Qualitative descriptive research, 27 Qualitative research, 183—204, 211— 212 data analysis, 204 data collection techniques, 198—203 research strategies, 190—198 case study, 193—195 document or content analysis, 191— 193 ethnography, 188, 195—198 sample research questions, 187 themes of, 184—187 context sensitivity, 186 inductive analysis, 186 theoretical traditions, 188—190 Quantification, 208—211 Quantitative descriptive research, 26—27 Quasi-experimental designs, 146, 151 —158 counterbalanced designs, 158 equivalent materials, pretest— post-test design, 155-158 equivalent time-samples design, 154—155 pretest—posttest, nonequivalent- groups design, 151—154 time-series design, 154 Questionnaire, 229—245 characteristics of a, good, 237—240 preparing and administering, 240—242 reliability, 245 samples,.
242, 243, 244 validity, 242—244 Quota sampling, 110 Subject Index 433 Randomness, 13—19, 139 Random numbers, 14—16 Random sample (simple), 14 Range, 284 Ratio scale, 210, 275 References, basic sources, 47—48 (also see Appendix I, pp.
413—423) Current Index to Journals in Education, 48 Dissertation Abstracts International, 48 Education Index, 48 index to Doctoral Dissertations, 48 Psychological Abstracts, 48 Resources in Education, 48 Regression line, linear, 298—302 slope of, 299—302 Regression to the mean, 142—143 Related literature, finding, 47—49 review of, 40—41 Reliability of research tools and methods coefficient of, 314 definition, 208 interview, 253—254 observation, 225—226 questionnaire, 245 test or inventory, 217—218 equivalent or parairel forms, 217—218 internal, 218 interscorer, 218 standard error of measurement, 218 test—retest, 217 Reliability, defined, 208 Replication, 124 Research, 20—27 action, 24—25 applied, 23 basic, 23 causal-comparative, 26 defined, 20—22 descriptive, 25—26 educational, 26—27 experimental, 27 explanatory observational, 26 cx post facto, 26 historical, 26 -- 434 Subject Index Research (cont'd) purposes, 23—25 qualitative descriptive, 27 quantitative descriptive, 26—27 Research problem (project), 32—38 list of topics, 35—3 sources of problems, 33—37 Research proposal, 38—43 data analysis, 42 definitions, assumptions, limita.- tions, delimitations, 40 hypothesis, 41—42 methods, 42 procedures, 42 review of related research, 40—41 significance of the problem, 39—40 statement of the problem, 39 subjects, 42, submitting to a funding agency, 55—56 time schedule, 42—43 Research report, format, 62—78 abstract, 63 discussion section, 66 figures, 73—78 introduction section, 63—65 method section, 65—66 pagination, 71 procedures subsection, 65 references and appendices, 66—67, 69—71 results section, 66 subjects subsection, 65 tables, 71—73 title, 62—63 title page, example, 64 Research topics used by students in a beginning graduate course, 53—54 Review of related research, 40—41 (see also Related literature, finding) Sample, 13, 276 Sample size, 19—20 Sampling, 13—20 area or cluster, 17—18 error, 13, 14, 19, 139 nonprobability samples, 18—19 probability, 110 quota, 110 random, 13—16 sample size, 19—20 stratified
 random, 17 systematic, 17 Scattergram, 299 Science, 7—9 Scientific method, 5—6, 9—10, 20 Secondary analysis, 124—127 Selection and maturation, interaction of, 143 Selection bias (as threat to internal experimental validity), 143 Semantic differential, 250—251 "Sesame street" studies, 122—123 Significance (alpha) level, 330—331 type I error, 331 type II error, 331—332 Single-subject research, 165—179 A-B-A designs, 173—175 alternating treatment designs, 177 assessment in, 171—173 baselines, 167—169 carryover, effect, 170 data collection strategies, 172—173 frequency measure, 172 real-time observation, 173 time sampling, 172—173 evaluating data from, 177—179 length of phases, 169—170 manipulating variables, 169 multiple baseline designs, 175—177 transfer of training and response maintenance, 171 Skewed distributions, 290 Slope of a regression line, 299—302, 310 Social scaling, 256—259 guess-who technique, 258—259 social distance scale, 259 sociogram, 257—258 sociometry, 256—257 Solomon four-group design, 150—151 Sources of historical data, 93—95 primary, 93—94 secondary, 93, 95 Spearman—Brown formula, 314 Standard deviation population (a), 285—287 sample (S), 335—336 Standard error of estimate, 312—314 Standard error of measurement, 218 Standard error of the mean, 324—321 Standard scores, 293—297 (see also Percentile rank; T score; Z score) Statement of the problem defined, 39 evaluating, 37—38 significance, 39—40 sources, 33—37 Static group comparison design, 148 Statistic, 13, 276 Statistical inference, 323—324 Statistical regression (as threat to internal experimental validity), 142— 143 Statistics, definition, 274 (see also Descriptive statistics; Inferential statistics) Stratified random sampling, 17 Student's distribution, 337 (see also test) Style (writing) manuals, 61 Survey, 107—112 public opinion, 109—110 school, 113—114 social, 108—109 Syllogism, 4 Systematic sampling, 17 T score, 295 t test (Student's t), 328—334, 336— 338, 341—345 correlation, of a;
 343—345 distribution for, 337 matched or correlated groups, 341—343 one-tailed, 332—334 small samples, 337—338 two-tailed, 332—334 Tables, 71—73 Testing (as threat to internal experimental validity), 142 Theory, defined, 9—10 Thurstone technique, 246 Time-series design, 154 Title page, example, 64 Treatment variable, 137 Treatment verification (as threat to Subject Index 435 external experimental validity), 145 Trend studies, 112—113 True experimental designs, 146, 148—151 posttest nly, equivalent-groups design, 148—149 pretest—posttest, equivalent-groups design, 149—150 Solomon fcur-group design, 150—151 Typing the report, 68—69 Unstable instrumentation (as threat to internal experimental validity), 142 Validity defined, 208 experimental, 140—146 Validity of research tools and methods coefficient of, 314 dêf1nition 208 experimental, 140—146 interview, 253—254 observation, 225 questionnaire, 242—244 -tet or inventory, 218—220 construct, 219 content, 219 criterion-related, concurrent, 220 criterion related, predictive, 219 Variables, 137—138 confounding, 137—138 extraneous, 138 intervening, 138 dependent, 137 independent, 137 attribute, 137 treatment, 137 operational definitions, 10—11 Variance between groups (ANOVA), 346—347 population parameter (o2), 285 sample statistic (S2), 335 within groups (ANOVA), 346-347 Writing style, 67—68 Z score, 293—294 'iTE F — 1HE MENTA1I' fpt1 ,ECUN0AO iANOICAP9W