bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incomplete Contigency Tables
Authors: Hershberger Scott L. 
Published: September 2014

AbstractIncomplete contingency tables have cells with no observations. When empty cells are predicted on the basis of theory or characteristics of the population, the cells have structural zeros. When empty cells occur from deficiencies in sampling, the cells have sampling zeros. While the analytic consequences of structural zeros are usually benign, the consequences arising from sampling zeros are often not so. The primary problem is deterioration in the accuracy of theG2and χ2approximations for testing the fit of loglinear models. Under these conditions, various strategies can be used to improve model testing.
Incidence
Authors: Grietens Hans 
Published: September 2014

AbstractIncidence is determined as the number of new cases of a disease in a population at a specified interval of time. Incidence rates provide information about the number of new cases or cases added to the sick population. Incidence studies are part of epidemiological research. They are conducted in medicine, psychiatry, and behavioral science. By means of an incidence study, one can truly identify individuals at risk for a disease, or risk factors. There is a dynamic relationship between prevalence and incidence, since prevalence rates are a function of both the incidence and the duration of a disease.
Yates' Correction
Authors: Stefanescu Catalina , Berger Vance W. , Hershberger Scott L. 
Published: September 2014

AbstractYates' correction for continuity adjusts the formula for Pearson's chi‐square test by subtracting 0.5 from each observed value in a 2 × 2 contingency table. This formula is used when at least one cell of the table has an expected frequency less than 5, although some sources say that this correction should be used when the expected frequency is less than 10.
Two by Two Contingency Tables
Authors: Liu Li , Berger Vance W. 
Published: September 2014

Abstract

Two by two contingency tables are generated by cross classifying subjects by two binary variables. In such tables, one may be interested in testing for an association between the two variables, and, if it is found, then also in estimating the strength of the association. The χ
2
test of independence and Fisher's exact test are illustrated to test independence. The difference in proportions, relative risk, and odds ratio are shown to measure the association. Sensitivity, specificity, the false positive rate, and the false negative rate are used to evaluate a test method. Simpson's paradox is introduced to illustrate the difference between marginal tables and partial tables.
Rater Agreement – Weighted Kappa
Authors: Mun Eun Young 
Published: September 2014

Abstract

One of the characteristics of Cohen's kappa (κ) is that any discrepancy between raters is equally weighted as zero. On the other hand, any agreement means
absolute agreement
between raters, and is equally weighted as one. Thus, the distinction between agreement and disagreement is categorical. For example, Table 1 from the Cohen's kappa section shows three agreement cells and six disagreement cells. In deriving Cohen's kappa, all six disagreement cells are treated equally. However, there could be situations in which the discrepancy between adjacent categories such as Categories 1 and 2 can be considered as partial agreement as opposed to complete disagreement. In those situations, Cohen's weighted kappa can be used to reflect the magnitude of agreement.
Trend Tests for Counts and Proportions
Authors: Liu Li , Berger Vance W. , Hershberger Scott L. 
Published: September 2014

Abstract
In a contingency table, one may be interested in whether there is a trend along the row variable or the column variable. In an R by 2 or 2 by C contingency table, where one variable is binary and the other is ordinal, the Cochran–Armitage test for trend can be used to test whether the binomial proportions have a linear trend across the ordinal variable. In an R by C contingency table, where one variable is ordinal and the other variable is nominal, the Jonckheere–Terpstra trend test can used to determine whether, within each level of the nominal variable, the proportions are distributed identically.
Chi‐Square Decomposition
Authors: Rindskopf David 
Published: September 2014

Abstract
When analyzing a contingency table, the most frequently used procedure is to test the association between variables using a chi‐square test of independence. If the variables are not independent, no further testing is typically done. However, the chi‐square can be decomposed, or partitioned, into components to test hypotheses concerning the nature of the differences among categories of the variables. One can determine which categories of one variable are (and are not) the same in their relation to the other variable.
Marginal Independence
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2014

Abstract

Two variables, say
X
and
Y
, are marginally independent if their joint distribution is the product of the two marginal distributions. In the discrete case, inspecting only the marginal table of
X
‐by‐
Y
may lead to false conclusions if the association between
X
and
Y
is not the same across different levels of another covariate, say
Z
. To prevent such misleading conclusions, partial tables of
X
and
Y
at each level of the confounding covariate
Z
should be displayed.
Sampling Issues in Categorical Data
Authors: Hershberger Scott L. 
Published: September 2014

Abstract
The sampling method used to collect response on categorical variables determines both the probability of the specific configuration of a contingency table, and, more importantly, determines the interpretation of the data. Sampling methods leading to three probability distributions; the hypergeometric, the binomial, and the negative binomial, are discussed. The three sampling methods indicate the same probability of observing an example contingency table, but lead to different interpretations of the data.
Models for Matched Pairs
Authors: Berger Vance W. , Zhang Jialu 
Published: September 2014

Abstract

When randomization is impractical or impossible, matched studies are often used to enhance the comparability of comparison groups, so as to facilitate the assessment of the association between an exposure and an event while minimizing confounding. There are several approaches to the analysis of matched data, including the matched pairs
t
Test, stratified analyses, the McNemar test, and conditional logistic regression. Each approach may be best in a given situation.
M
onte
C
arlo Simulation
Authors: Gentle James E. 
Published: September 2014

Abstract
Monte Carlo simulation is one of the most important tools in all fields of science. Monte Carlo methods use samples of numbers that appear to be random, although the numbers are generated by deterministic computer algorithms. These “pseudorandom” samples can be used in solving a wide range of problems, including both deterministic problems such as evaluating a definite integral and problems in which there is a random component such as studying and comparing the performance of statistical procedures under various assumptions about the underlying distribution.
Regression Discontinuity Design
Authors: Shadish William R. , Luellen Jason K. 
Published: September 2014

Abstract
The regression discontinuity design is a quasi‐experimental design in which the experimenter assigns participants to treatment conditions on the basis of a cutoff score on an assignment variable – usually a measure of need or merit. This design yields unbiased estimates of treatment effects when certain assumptions are met. Just as in a randomized experiment, the selection process is both completely known and perfectly measured in the regression discontinuity design. Numerous variations of the design are possible, including combinations with randomized experiments.
Causal‐Comparative Study
Authors: Busk Patricia L. 
Published: September 2014

Abstract

A nonexperimental design that is employed to explore cause‐and‐effect relationships is called
ex post facto
or causal‐comparative. The title
ex post facto
is applied to studies in which data are gathered retrospectively, whereas causal‐comparative is used when data are gathered from groups and the independent variable is not manipulated as in an experiment.
Matching‐Models and Examples
Authors: Berger Vance W. , Liu Li , Thach Chau 
Published: September 2014

Abstract
Evaluation is generally inherently comparative. For example, a treatment is generally neither ‘good’ nor ‘bad’ in absolute terms, but rather better than or worse than another treatment. The studies that allow for such comparative evaluations must then also be comparative. To isolate the effects of the treatments under study, avoid confounding treatment effects with unit effects, and possibly increase the power of the study, the units under study must be carefully matched across treatment groups. There are many matching techniques in practice, including crossover designs and case‐control studies. While randomization is often considered to be a competitor to matching, it actually may be considered to be a type of matching, except that here the matching is group‐to‐group.
Counterbalancing
Authors: DePuy Venita , Berger Vance W. 
Published: September 2014

AbstractWhen subjects are consecutively given more than one treatment in an experiment, including for crossover design, earlier treatments may affect the results observed during later treatments. To counteract this, the treatment order may be varied between different subjects, or counterbalanced. While complete counterbalancing often requires large numbers of participants, partial counterbalancing, often through the use of a Latin squares design, can provide a reasonable alternative.
Counternull Value of an Effect Size
Authors: Rosenthal Robert 
Published: September 2014

Abstract

The counternull value of an effect size is the nonnull magnitude of effect size that is supported by exactly the same amount of evidence as supports the null value of the effect size. That is, if the counternull value were taken as the null hypothesis, the resulting
P
value would be the same as the obtained
P
value for the actual null hypothesis. Reporting the counternull, in addition to the
P
value, virtually eliminates the common error of equating failure to reject the null with the estimation of the effect size as equal to zero.
M
axwell,
A
lbert
E
rnest
Authors: Bartholomew David J. 
Published: September 2014

Abstract

A.E. Maxwell (1916–1996) spent most of his career at the Institute of Psychiatry, University of London, latterly as head of the Biometrics Unit. He is best known for his co‐authorship (with D.N. Lawley) of the book
Factor Analysis as a Statistical Method
. This established factor analysis as a branch of statistical methodology. His other publications include several books on psychological statistics and he also engaged in extensive consulting and advisory work.
History of Mathematical Learning Theory
Authors: Lovie Sandy 
Published: September 2014

Abstract
The article outlines the story of modern mathematical learning theory from 1950. In particular, the account provides a brief comparison of the earlier work by Hull with the newer approaches by Estes, Bush and Mosteller, and Luce. It is noted that the latter were thoroughgoing probabilists in that they argued that all learning could only be modeled by stochastic mechanisms.
History of the Control Group
Authors: Dehue Trudy 
Published: September 2014

AbstractIn contemporary behavioral and social research, scientific experimentation means comparing experimental groups to control groups and establishing the statistical significance of the resulting averages. Yet, this design is of a rather recent date. From the early nineteenth century, scientific experimentation with human beings has been a much debated topic. The rule of comparing groups, however, did not appear in methodological manuals until the second decade of the twentieth century. This article presents a history of the control group. It first discusses nineteenth‐century methodological views on human science experimentation, and explains why the notion of the control group could not be conceived at the time. Next, it describes and explains the introduction of control groups in the course of the twentieth century. By analyzing the nineteenth‐century values that excluded group comparison, it aims to identify the twentieth‐century principles endorsing it.
Graphical Methods Pre‐20th Century
Authors: Smith Laurence D. 
Published: September 2014

AbstractDespite their utility for analyzing and presenting data, the use of graphs in science is a relatively late development and their emergence through history has been uneven and contested. Modern statistical graphics date largely to the pioneering work of William Playfair in the late eighteenth century. The gradual spread of graphical methods in the early nineteenth century was slowed by various antigraph prejudices, but eventually culminated in their enthusiastic reception during the second half of that century. During the Golden Age of graphics, graphs were used extensively in fields ranging from demography to laboratory physiology. Hailed as the universal language of science, graphs underwent a proliferation of novel formats, and they figured crucially in a number of scientific discoveries. The behavioral scientists of that period – including many of psychology's founding figures – made sophisticated applications of graphical methods, but such methods would be eclipsed by the rise of inferential statistics in the behavioral sciences after 1900.
History of Surveys of Sexual Behaviour
Authors: Everitt Brian S. 
Published: September 2014

AbstractAccording to psychiatrist Anthony Storr, sexual intercourse is one of the most natural and certainly the most rewarding and the most life‐enhancing of all human experiences'. Few of us would argue with that, but although sex plays a vital part in our lives, it has for long periods been associated with guilt and shame, rather than enjoyment. Perhaps this explains why it is only in the last hundred years or so that psychologists, biologists, and others have tried to discover the truth about human sexual behavior in a scientific and objective manner. One of the main tools used in such investigations has been the sample survey, and this paper presents a brief (and therefore necessarily incomplete) history of surveys about sex.
J
onckheere–Terpstra Test
Authors: Lunneborg Clifford E. 
Published: September 2014

Abstract
For the one‐way design, the Jonckheere‐Terpstra procedure tests the null hypothesis of no difference in response magnitude across treatments against an ordered alternative, that response magnitude increases over a pre‐specified ordering of the
treatments. Where the treatment levels can be ordered according to expected response magnitude (e.g., drug dosages, stimulus intensities, and hours of practice), the Jonckheere–Terpstra test (1954) employs a more coherent, directional alternative hypothesis, that is, the response magnitude does not decrease for any treatment level increase and actually increases for one or more treatment level increases.
Confidence Intervals
Authors: Dracup Chris 
Published: September 2014

AbstractIn estimating a population parameter from sample data, it is more informative to provide a range of ‘plausible’ parameter values rather than a one‐point estimate. A 95% confidence interval contains those values of the population parameter that would not be rejected at the 0.05 significance level by a statistical test applied to the sample data. In the long run, such intervals should provide 95% ‘coverage,’ with 95% of them including their true population value (assuming the data meet any distribution assumptions). Topics covered include the construction of confidence intervals for means, proportions, and mean differences; the effects of discrete and nonsymmetrical sampling distributions; central and noncentral intervals; bootstrapped intervals and the Bayesian approach.
Sphericity Test
Authors: Keselman H. J. 
Published: September 2014

Abstract

Classical test statistics, such as the usual
F
test, for the analysis of repeated measures or longitudinal treatment effects, will provide accurate assessments (i.e., correct Type I error probabilities) of null hypotheses when their assumptions of independence, normality, and homogeneity hold. Homogeneity in this context is referred to as sphericity or circularity. This paper enumerates the validity assumptions for simple‐order and higher‐order repeated measures designs, as well as discussing why for many behavioral science applications sphericity will not be satisfied, and concludes by noting procedures that researchers can adopt to circumvent the biasing effects of nonsphericity.
Power
Authors: Howell David C. 
Published: September 2014

AbstractThe power of a test refers to the probability with which that test will reject the null hypothesis in those situations in which it is false. This article focuses on the variables that influence power and discusses the different conceptions of power.A prioripower is computed before the experiment is conducted and is critical to selecting an appropriate sample size. Retrospective power is calculated on the basis of the completed study. This article outlines a number of concerns with retrospective power. Compromise power is based on the relative seriousness of Type I and Type II errors, and should be considered seriously for exploratory research. Finally, the article discusses the importance of considering confidence intervals on effect sizes, in addition to power.
T
ype
I
,
T
ype
II
and
T
ype
III
Sums of Squares
Authors: Hershberger Scott L. 
Published: September 2014

Abstract
Whenever factorial designs have equal cell frequencies, the factors are orthogonal. Although orthogonal factors are not required, orthogonal factors permit a unique decomposition of the variance in the dependent variable. If cell frequencies vary, or there are empty cells, the factors are nonorthogonal: The variance decomposition of the dependent variable differs depending on which of Type I, II, III, or IV sums of squares is used for the analysis.
Sensitivity Analysis in Observational Studies
Authors: Rosenbaum Paul R. 
Published: September 2014

AbstractIn an observational study, subjects are not randomly assigned to treatment or control, as they would be in a controlled clinical trial. As a consequence, differing outcomes in treated and control groups may not reflect effects of the treatment, but rather some way the groups were not comparable prior to treatment, that is, some hidden bias. A sensitivity analysis appraises how the conclusions of a study might be altered by hidden biases of various magnitudes.
Additivity Tests
Authors: Karabatsos George 
Published: September 2014

AbstractAdditive linear models are ubiquitous in applied areas of behavioral statistics, and so it is often important to test whether data fit the assumption of additivity. In a test of additivity, the null hypothesis is that a dependent variable is an additive, noninteractive function of two (or more) independent variables, while the alternative hypothesis of nonadditivity is characterized by one or more interactions between the independent variables. It is possible to test for additivity in the context of Analysis of Variance (ANOVA), while the theory of Additive Conjoint Measurement provides a more general basis for testing additivity.
Directed Alternatives in Testing
Authors: Cohen Arthur 
Published: September 2014

Abstract

Consider a hypothesis‐testing problem concerned with a one‐dimensional parameter θ. In testing a null hypothesis
H
: θ = θ

o

, a two‐sided alternative is
K
: θ ≠ θ

o

. A one‐sided alternative is
K
: θ > θ

o

. This latter alternative is appropriate when it is reasonably certain that θ cannot be less then θ

o

. The advantage of posing a one‐sided alternative is that the appropriate one‐sided test is much more powerful than the corresponding two‐sided test. The idea of one‐sided and two‐sided alternatives extends to the case of testing a
k
‐dimensional parameter
θ
. If the null hypothesis is
H
:
θ
=
θ
o
, then the analogue to the two‐sided alternative is
K
:
θ
≠
θ
o
. This latter alternative is said to be unrestricted or not directed. An example of the analogue to the one‐sided alternative is
K
: θ

i

≥ θ

io

, for
i
= 1, …,
k
, with strict inequality for at least one index
i
. Here
θ
= (θ
1
, …θ

k

). Such an alternative is called a
directed alternative
.

We introduce the notion of directed alternatives for multidimensional parameters. Among the special cases treated are the simple order alternative in which parameters are nondecreasing, the tree order alternative in which case one mean parameter is assumed to be less than or equal to each of the other mean parameters, and the stochastic order alternative in which one multinomial distribution is more concentrated at higher categories than another distribution. We indicate appropriate tests for these directed alternatives.
Simple V Composite Tests
Authors: Macdonald Ranald R. 
Published: September 2014

Abstract

If a null hypothesis takes the form θ =
c
, where c is a constant and θ is the only unknown parameter in the supposed chance probability distribution, then this hypothesis is called a simple hypothesis. Where the chance distribution has more than one unknown parameter, the null hypothesis is said to be composite. Often, but not always, composite hypotheses are phrased in terms of several parameters. Examples include θ
1
=
c
1
, θ
2
=
c
2
, …θ

k

=
c

k

or θ
1
= θ
2
= θ
3
= ⋯ = θ

k

. Where this is the case, the overall null hypothesis can be split into separate testable component hypotheses and this raises multiple testing issues.
Test Dimensionality: Assessment of
Authors: Gessaroli Marc E. , Champlain Andre F. 
Published: September 2014

Abstract
The ability to properly assess the dimensionality of a set of item responses is essential in psychometrics. Many popular psychometric methods using item response theory (IRT) such as item calibration and test equating assume that there is a single latent trait underlying the responses. As well, the popularity of multidimensional models has increased. Furthermore, test dimensionality is directly linked to the structural aspect of the construct validity of a test. History has shown some confusion in the definition of dimensionality and in the methods to assess it. Recent literature relates both the definition of dimensionality and its methods of assessment to the concepts of local independence and essential independence. This entry provides an overview of these definitions and of the more current methods of dimensionality assessment.
Computer‐Based Test Designs
Authors: Zenisky April L. 
Published: September 2014

Abstract
Among the decisions to be made in implementing a computer‐based test, the choice of test design is one of the most important. A test design is the manner by which individual items are selected for presentation to examinees during administration. The importance of evaluating test design options is critical because it not only concerns test administration but also basic activities in the test development process and the quality and precision of scores to be obtained for a specified testing purpose. Essentially, conceptualizing the test design options for computerized testing first hinges on the property of being adaptive or not, and then there are varied approaches within each of those two groupings. The purpose of this entry is to provide an overview of selected test designs, adaptive and otherwise, while highlighting certain measurement advantages and concerns associated with each.