bluuebunny/crossref_metadata_2025 huggingface dataset:
Rethinking the Allocation of Teaching Resources: Some Lessons From High-Performing Schools
Authors: Miles Karen Hawley , Darling-Hammond Linda
Published: March 2008

Although a great deal of debate surrounds the level and allocation of resources to public schools, very little research addresses how schools might organize teaching resources more effectively at the school level. This article describes case studies of five high-performing public schools that have organized professional resources in innovative ways. The study sought to detail alternative ways of deploying instructional resources in order to provide concrete alternatives to traditional organization of teachers and to quantify objectively the ways in which these schools use resources differently depending on their instructional goals and strategies. Although the schools studied looked very different from one another, they shared six principles of resource allocation that are outlined in this article. The article develops a framework for examining the use of resources and a methodology that may be used to measure the extent to which schools use their resources in focused ways to support teaching and learning.
Notes: A Study of the Utility of Results From the 1992 Trial State Assessment (TSA) in Reading for State-Level Administrators of Assessment
Authors: Bullock Cheryl Davis , DeStefano Lizanne
Published: March 2008

This study investigated the usefulness of the 1992 TSA in reading for state-level administrators of assessment. Data regarding their perceptions about the credibility and orientation of various components of the TSA were collected (using phone interviews) from 26 state directors of assessment, or their designees, and analyzed. Additionally, data about the utility of the manner of reporting TSA results, how results are currently used and could be improved, were collected and analyzed. The following four conclusions were drawn from the study: involve the International Reading Association and more teachers, continue but modify descriptors, disseminate results quicker, and make results more useful.
Do New Teaching Standards Undermine Performance on Old Tests?
Authors: Mayer Daniel P.
Published: March 2008

As almost every state attempts to reform mathematics instruction by implementing new teaching standards, state testing practices remain largely unchanged. Do these new standards undermine student performance on old tests? This question is investigated by examining whether middle and high school algebra students taught in a manner consistent with the National Council for Teachers of Mathematics Professional Standards performed differently on three standardized algebra assessments than students taught in traditional classrooms. The data come from 94 teachers, 2,369 students, and 40 schools in one of the nation’s largest school districts. Results indicate that the Standards do not undermine performance on the old tests. In fact, middle school algebra students whose teachers spent more time using the NCTM teaching approach had higher growth rates than students whose teachers spent less time using the approach. However, students with higher ability levels benefited more. The growth rates of the lowest achieving students, the high school students (who are disproportionately Black and poor), were not helped or hindered by the NCTM teaching approach. If, as other studies indicate, the new standards help students on more novel tests, the finding that students benefit or at least are not hurt on traditional tests strengthens the case for implementing the NCTM reforms.
Does Work Inhibit Cognitive Development During College?
Authors: T.Pascarella Ernest , Edison Marcia I. , Nora Amaury , Hagedorn Linda Serra , T.Terenzini Patrick
Published: March 2008

This longitudinal investigation of 23 colleges and universities sought to estimate the impacts of on- and off-campus work on standardized measures of student cognitive development across three years of college. With controls made for student background characteristics and other experiences of college, there was little evidence to suggest that either form of work inhibited cognitive development in the first year of college. In the second year of college, on-campus work had small negative total and direct influences on science reasoning, but neither form of work inhibited students’ writing skills. Both forms of work had a significant curvilinear relationship with a composite measure of end-of-third-year cognitive development consisting of reading comprehension and critical thinking. Part-time on- or off-campus work had a positive influence, but on-campus work in excess of 15 hours per week or off-campus work in excess of 20 hours per week had a negative impact. Finally, across all years of the study, the cognitive impacts of work appear to be essentially the same, irrespective of student characteristics (e.g., ethnicity, gender, age, precollege ability, full-or part-time enrollment) and whether or not the student attended a two-year or a four-year college.
Shortcut: High School Grades As a Signal of Human Capital
Authors: Miller Shazia Rafiullah
Published: March 2008

Research shows that employers are dissatisfied with their ability to hire good workers out of high school ( Barton, 1990 ; Cappelli & Rogovsky, 1993). This article considers whether employers could benefit from using high school grades to identify workers whom they will value more in the long run. Using the High School and Beyond data on the sophomore cohort, this article examines the effects of high school grades on long-term productivity as measured by earnings. It finds that high school grades do have a strong and significant effect on earnings 9 years after high school for both men and women, those with and without bachelor’s degrees, and controlling for race/ethnicity, SES, region of the country, and whether the school is public or private. Using a fixed-effect model, it also demonstrates that these findings are robust even after controlling for school-level differences. The article further confirms other researchers’ findings of no or negative short-term effects of high school grades on earnings. It argues that this connection between grades and long-run productivity suggests that employers could use high school graduates’ grades to identify workers they will value.
Alternative Certification: A Complicated Research Topic
Authors: Shen Jianping
Published: March 2008

This rejoinder is a response to the three Ballou critiques. It argues that Ballou offered some interesting and creative ideas to check the quality of the data set, but gave no conclusive evidence to demonstrate that the findings are invalid. Further analyses of the data set are discussed.
Improving Higher Education Access and Persistence: New Directions From a “Systems” Perspective
Authors: Stampen Jacob O. , Hansen W. Lee
Published: March 2008

Finding effective ways of increasing college access and persistence requires that proposed solutions be evaluated according to how they individually and collectively interact with the broad goals of higher education. Only then is it possible to estimate the overall impact of any particular solution. This article illustrates a “systems” approach, based on the quality function deployment model, for examining the direct and interaction effects of multiple solutions aimed at improving access and persistence. The results of applying this approach indicate the necessity of quality and access policies that support the entire educational system’s capacity to improve. Rather than simply more student aid, the “high leverage” solutions that emerge argue for improving the academic achievement of K–12 students and enhancing the quality of K–12 schools. The article concludes by outlining how researchers and stakeholder groups can jointly use an approach similar to the one we have illustrated to broaden consensus on how to improve access and persistence.
Social Contracts and the Impact of Matching Fund Requirements on American Research Universities
Authors: Feller Irwin
Published: March 2008

Matching fund and cost-sharing requirements are increasingly pervasive elements in federal support of academic research. Federal agencies perceive universities as receiving benefits in the form of increased reputation and intellectual property rights from the performance of federally funded research. Distributed benefits are seen as justifying distributed costs. This trend challenges the public goods rationale for federal support of academic research. Reinforcing this trend are the increases in the number of research-intensive universities that induce agencies to seek what are, in effect, price discounts and induce universities to voluntarily offer them. The responses of universities to this new bargaining environment and public policy issues are examined.
Notes: Catholic High Schools and Homework
Authors: Sander William
Published: March 2008

This article provides evidence on the effect of Catholic high schools on the total hours of homework students do. Estimates are undertaken for White and minority students, adjusting for selectivity bias. It is shown that Catholic high schools have either no effect or a very modest positive effect on hours of homework done by White students. For minority students, Catholic high schools have a relatively large positive effect on the amount of time spent doing homework. It is also shown that one of the ways that Catholic high schools increase minority test scores is through homework. A Catholic school effect on test scores of White students was not found.
Getting Tough? The Impact of High School Graduation Exams
Authors: Jacob Brian A.
Published: March 2008

The impact of high school graduation exams on student achievement and dropout rates is examined. Using data from the National Educational Longitudinal Survey (NELS), this analysis is able to control for prior student achievement and a variety of other student, school, and state characteristics. It was found that graduation tests have no significant impact on 12th-grade math or reading achievement. These results are robust with a variety of specification checks. Although graduation tests have no appreciable effect on the probability of dropping out for the average student, they increase the probability of dropping out among the lowest ability students. These results suggest that policymakers would be well advised to rethink current graduation test policies.
Female and Male Admission to Graduate School: An Illustrative Inquiry
Authors: Maxwell Scott E. , Jones Lyle V.
Published: March 2008

Female and male admission rates to four graduate programs at the University of North Carolina at Chapel Hill are compared for 1972–73 and 1973–74. To assess possible sex-related bias in admission, rates are adjusted for applicant qualifications by analysis of covariance and by balancing. Guidelines for choosing between these two techniques are developed. The use of adjustment techniques for these data is shown to be justified in terms of Meehl’s analysis of theex post facto design. Other adjustment procedures, direct and indirect standardization, are critically discussed.

The adjusted admission rates reflect, in one case, i.e., for one program and one admission year, a slight advantage for male applicants over females, while in three cases, female applicants were granted a slight advantage over males in admission. In the remaining four cases, there is no evidence that sex of applicant,per se, played a role in admission decisions. Wherever a sex-related advantage is detected, the favored sex is that with the fewer applicants to the program.

While the generalization of these results to other programs and other graduate schools is unwarranted, it is appropriate to emphasize the value of applying balancing and/or analysis of covariance to data that may be collected for the purpose of detecting possible discrimination in the selection or evaluation of identifiable population subgroups.
A Nonparametric Test for the Correspondence Between Two Proximity Matrices
Authors: Schultz James V. , Hubert Lawrence
Published: March 2008

A nonparametric test, developed in the biometrics literature for the study of contagious diseases, may be used more generally to compare the patterning of entries in two proximity matrices. The procedure is a simple extension of the randomization strategies appropriate for the standard bivariate correlation context, and provides a relatively easy alternative to some of the more informal analyses commonly used in education.
Data Analysis by Single-Link and Complete-Link Hierarchical Clustering
Authors: Hubert Lawrence , Baker Frank B.
Published: March 2008

The paper presents an exposition of two data reduction methods widely used in the behavioral sciences and commonly referred to as the single-link and complete-link hierarchical clustering procedures. Major emphasis is placed on several statistical techniques for evaluating the adequacy of a completed partition hierarchy and, in particular, the individual partitions within the sequence. A numerical reanalysis of some previously published data is included as an illustration of the suggested methodology.
Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study
Authors: Games Paul A. , Howell John F.
Published: March 2008

Three different methods for testing all pairs of yȳk, - yȳk’ were contrasted under varying sample size (n) and variance conditions. With unequal n’s of six and up, only the Behrens-Fisher statistic provided satisfactory control of both the familywise rate of Type I errors and Type I error rate on each contrast. Satisfactory control with unequal n’s of three and up is dubious even with this statistic.
Value-Added Analysis: A Dynamic Approach to the Estimation of Treatment Effects
Authors: Bryk Anthony S. , Weisberg Herbert I.
Published: March 2008

Randomized experiments are rarely feasible in large-scale educational and social evaluations. Most evaluations are observational studies in which the investigators have very limited control over the assignment of individuals to treatments. Since the effect of an intervention becomes confounded with those of other influences, clear causal inferences are very difficult to obtain.

A number of statistical “adjustment” strategies have been suggested in an attempt to remove the bias attributable to these confounding factors. These techniques are based on a static model in which the outcome is a relatively simple function of various inputs, one of which is the treatment. In educational studies, one of the principal “inputs” is generally a measure of the outcome variable prior to the program. Statistical adjustments are based primarily on the observed relationship between these pretest scores and the posttest scores measured after the intervention.

An alternative analysis approach is presented in this paper. It focuses explicitly on the fact that an educational treatment typically involves an intervention in a growth process. By modelling this process, it may be possible to estimate expected growth for various treatment groups under “control” conditions. Actual growth can be compared with projected growth to estimate thevalue-added by the program.

A very simple model is developed here as a first step toward implementing this approach. The model is applied to a set of data taken from the Head Start Planned Variation Study. The results are compared with a parallel analysis using the analysis of covariance. Discrepancies between the results of the two analyses are examined, and it is suggested that the value-added results are more accurate in this instance.
Development and Validation of a Simplified m-Group Regression Model
Authors: Shigemasu Kazuo
Published: March 2008

This study is an application and specialization of the Bayesian linear model developed by Lindley and Smith (1972). The context is m-group regression and the application to the prediction of grade point average. The specialization involves the assumption of homogeneity of regression coefficients (but not intercepts) across groups. Empirical cross-validations are conducted to compare the predictive efficiency of this model with that of the full m-group regression and several least squares methods in the context of educational selection.
Models of Environmental Effects on the Development of IQ
Authors: Kadane Joseph B. , McGuire Timothy W. , Sanday Peggy Reeves , Staelin Richard
Published: March 2008

This paper proposes a number of models of the effects of demographic and environmental factors on IQ and its pattern of change over time. The proposed models are concerned with the determinants of an Individual’s true (but unobserved) IQ and the relationship between measured and true IQ’s.

Our analyses are based on data from the school records of a panel of 1, 746 students from the Pittsburgh school system and include demographic and environmental measures as well as IQ test scores at kindergarten, fourth, sixth, and eighth grades. The results indicate that the number of parents living in the household has an important positive effect on IQ at kindergarten that persists over the period of analysis. Number of siblings (measuring both birth order and family size) has an important negative influence on cumulative changes in IQ. Females show faster development until fourth grade; the net difference between the sexes almost completely vanishes by eighth grade. However, the predominant influences on the development of IQ are the socioeconomic status of the student’s parents and peers in school, with SES of peers being the more important. We estimate that roughly half of the difference between the measured eighth grade IQ’s of the average white and black student in our sample may be attributed to differences in their peer and parental SES’s. Finally, we caution that our results may be misleading, since our sample is not from a designed experiment and thus some of the explanatory variables could be correlated with unobserved factors that affect IQ.
Improving Regression Models
Authors: Walberg Herbert J. , Rasher Sue Pinzur
Published: March 2008

This paper illustrates cut-and-try techniques that point to appropriate transformations of variables and to the selection of sets of variables for an equation that may improve understanding of a social process. The substance of the research reported — the relation of mental test results to state population, cultural, and school resource indexes (Walberg and Rasher, 1974) — illustrates typical problems of behavioral data: multi-colinearity, outliers, abnormal distributions, and the lack of a consensually-validated, explicit theoretical model. Despite these problems, data originally collected for purposes other than the investigator’s may yield tentative confirmations or cautions about prior findings and provisional indications for theory or policy; such inferences may be at least partially checked by cross-validation on independent or semi-independent sets of data. After discussing the sequence of analyses and the results, we conclude by mentioning a number of uncertainties and reservations about drawing substantive or policy implications.
Canonical Analysis when Number of Variables is Large Relative to Sample Size
Authors: Weinberg Sharon L. , Darlington Richard B.
Published: March 2008

Canonical Variate Analysis (CVA) has two major problems: sampling error and accumulated rounding error. This paper describes a method designed to minimize these problems. The combined effect of the two problems was assessed for CVA and the new technique in an empirical study. The new technique appears to be superior to CVA when the ratio of variables to sampling units is greater than 1/10.
A Note on the Length and Passing Score of a Mastery Test
Authors: Wilcox Rand R.
Published: March 2008

Using an indifference zone formulation, Fhanér has described a ranking and selection procedure for determining the appropriate length of a mastery test. The purpose of this note is to relate Fhanér’s solution to one given by Millman, to give a criterion for the choice of a passing score, and to describe a search routine which will always yield the appropriate test length and implied passing score.
Determining School Effectiveness Following a Regression Analysis
Authors: Convey John J.
Published: March 2008

Three methods that can be used subsequent to a regression analysis to determine the relative effectiveness of schools are Dyer’s performance indicators (PIs), Scheffé’s hyperbolic confidence bands, and Gafarian’s linear confidence bands. The purpose of this paper is to investigate the relative usefulness of the three methods under various conditions. The three methods were applied to hypothetical data from 54 schools randomly generated from a multivariate normal distribution using parameters from previous studies. Data points having PIs of 1 and 5 generally fell outside of the Scheffé confidence bands. The linear confidence bands were much wider than the Scheffé bands near the mean and slightly narrower at the extremes. The results indicated that extreme PIs seem to be appropriate for identifying schools which are achieving above and below expectation. Fine discriminations using PIs as defined by Dyer may not be warranted. Gafarian bands tend to be so wide as to limit their applicability to most settings. The choice of a particular technique to determine school effectiveness is dictated somewhat by the intent of the investigator. A conservative strategy would dictate employing the Scheffé technique with high confidence levels or adopting a decision rule of a difference of four PIs between two schools. Use of lower confidence levels or less stringent differences in PIs would result in a more liberal strategy.
Partial Ordering of Populations
Authors: Stephens Larry J. , Claypool P. L. , Buchalter Barbara
Published: March 2008

The two-way classification design structure involving ranks has the practical limitation that it may not be possible or practical to rank all p of the objects in ascending (or descending) order of preference. However, in many such instances it may be quite reasonable to expect each observer to identify a subset, say k < p, of the objects as being “most preferred” and exhibit the order of preference among the objects in this subset. In this paper a test statistic is developed for testing the hypothesis of no difference in preference among the p objects. Since the null distribution of this statistic is laborious to tabulate, a chi-square approximation and a beta approximation to this distribution are given with a discussion of their relative merits based on a limited tabulation of the statistic.
Improving the Use of Educational Tests as Selection Tools
Authors: Thomas Jeannie Gouras , Owen D. B. , Gunst R. F.
Published: March 2008

Educational tests are used extensively for predicting future performance of individuals. Multiple regression is the most commonly used approach for this purpose, but occasionally it is desirable to use multiple cutoff scores. This paper provides Taylor-Russell type tables for determining the theoretical proportion of successful students in a selected subpopulation as a function of: the proportion successful in the unselected population; various intercorrelations; and the percentage cut on each of the two variables.
Analyzing Distinctive Features
Authors: Hubert Lawrence J. , Baker Frank B.
Published: March 2008

A statistical technique is proposed for comparing an empirically obtained confusion matrix against a set of distinctive features that supposedly characterize the stimuli on which the given confusion matrix is based. Each distinctive feature corresponds to a partition of the stimulus set, and the term “confusion matrix” refers to the measures of “closeness” collected on all pairs of stimuli. The suggested paradigm can be considered an analysis-of-variance generalization and is dependent on a randomization strategy for evaluating the size of a goodness-of-fit index calculated between the given confusion matrix and a single partition. An example of the inference scheme is carried out on a data set dealing with the 26 Roman capital letters; in addition, an exploratory strategy is illustrated that tries to locate “good” partitionings of a stimulus set in a post-hoc manner.
An Application of the Qualitative Logistic Response Model for Characterizing Developmental Patterns of Aggression
Authors: Schmidt William H. , McSweeney Maryellen , Hapkiewicz Walter G.
Published: March 2008

A method is proposed for modeling developmental data, in general, and, in particular, the developmental relationship between the age of a child and the presence or absence of aggressive (pushing) behavior using a logistic quantal response model. The proposed model was applied to cross-sectional data on 180 first-, second-, and third-grade children from a study by Hapkiewicz (1973) . The results tended to support the hypothesized sex differences and the logistic quantal response model was found to fit the data adequately. An interpretation of the parameter estimates provide interesting conclusions concerning the developmental patterns of aggression for both sexes.
The Unit-of-Analysis Problem in Applications of Simple Correlation Analysis to Educational Research
Authors: Knapp Thomas R.
Published: March 2008

This paper summarizes the interrelationships among within-aggregate, between-aggregate, and total-group correlation coefficients, with artificial and “real-data” examples. It also discusses the relevance of correlation analyses at various levels of aggregation and some of the difficulties encountered in cross-level inference.
Anova Tests for Homogeneity of Variance: Nonnormality and Unequal Samples
Authors: Martin Charles G. , Games Paul A.
Published: March 2008

This paper presents an exposition and an empirical comparison of two potentially useful tests for homogeneity of variance. Control of Type I error rate, P(EI), and power are investigated for three forms of the Box test and for two forms of the jackknife test with equal and unequal n's under conditions of normality and nonnormality. The Box test is shown to be robust to violations of the assumption of normality. The jackknife test is shown not to be robust. When n's are unequal, the problem of heterogeneous within-cell variances of the transformed values and unequal n's affects the jackknife and Box tests. Previously reported suggestions for selecting subsample sizes for the Box test are shown to be inappropriate, producing an inflated P(EI). Two procedures which alleviate this problem are presented for the Box test. Use of the jack-knife test with a reduced alpha is shown to provide power and control of P(EI) at approximately the same level as the Box test. Recommendations for the use of these techniques and computational examples of each are provided.
Inference Procedures for Ordering Theory
Authors: Baker Frank B. , Hubert Lawrence J.
Published: March 2008

Given observations on a set of n dichotomously scored test items representing certain skills or tasks, ordering theory attempts to identify a hierarchical organization among the n items. Using this basic framework, a directed graph representation for the empirically obtained hierarchy is discussed along with several assumptions that are necessary from a substantive point of view to justify the analysis that ordering theory provides. Furthermore, a permutation test procedure is introduced that allows a formal comparison of a postulated hierarchy among the n items to the hierarchy elicited from the available data set.
Indices of Cheating on Multiple-Choice Tests
Authors: Frary Robert B. , Tideman T. Nicolaus , Watts Thomas M.
Published: March 2008

This paper reports the development of indices reflecting the probability that the observed correspondence between the multiple-choice test responses of two examinees was due to chance. Applications of the indices are presented both with respect to apprehending persons who cheat by copying answers and with respect to monitoring the prevalence of this form of cheating in order to evaluate methods of preventing it.
Estimating the Likelihood of False-Positive and False-Negative Decisions in Mastery Testing: An Empirical Bayes Approach
Authors: Wilcox Rand R.
Published: March 2008

False-positive and false-negative decisions are the two possible errors committed with a mastery test; yet the estimation of the likelihood of committing these errors has not been investigated. Accordingly, two methods of estimating the likelihood of committing these errors are described and then investigated using Monte Carlo techniques. Conditions for obtaining accurate estimates are noted.
Educational Production Functions
Authors: Polachek Solomon W. , Kniesner Thomas J. , Harwood Henrick J.
Published: March 2008

This research examines scholastic performance within the context of an individual’s production function. A constant partial elasticity of substitution production function for academic achievement is presented and estimated with non linear maximum likelihood methods. We find that ability and time devoted to various aspects of the learning process are the most important determinants of students’ accomplishments. Our results underscore the potential for students to compensate for relatively “poor” educational backgrounds by spending more time on study and class attendance.
Sensitivity of the Complete-Link Clustering Technique to Missing Individuals
Authors: Baker Frank B.
Published: March 2008

The effect of missing individuals upon the stability of the partition hierarchy yielded by the complete-link clustering technique was investigated. Empirical distributions of indices measuring the goodness-of-fit between partition hierarchies based on full and reduced sets of objects were obtained for data sets in which one, two, or three individuals were missing. The obtained results, based upon an object set obtained from an educational setting and four artificial sets, showed that the goodness-of-fit was a function of the number of missing individuals and the structure of the inter-object proximity matrix. In addition, a “key object” phenomenon was noted where the removal of certain individuals can result in low agreement between the hierarchies based upon the full and reduced set of objects. The results suggest that the complete-link clustering technique should be used with some caution, when less than the full set of objects is analyzed.
Sampling Errors of Variance Components in Small Sample Multifacet Generalizability Studies
Authors: Smith Philip L.
Published: March 2008

The paper describes the small sample stability of least square estimates of variance components within the context of generalizability theory. Monte Carlo methods are used to generate data conforming to some selected multifacet generalizability designs to illustrate the sampling behavior of variance component estimates. Based on the findings, recommendations are made concerning the design of efficient small sample generalizability studies.
Comparison of Means: An F Test Followed by a Modified Multiple Range Procedure
Authors: Shaffer Juliet Popper
Published: March 2008

If used only when a preliminary F test yields significance, the usual multiple range procedures can be modified to increase the probability of detecting differences without changing the control of Type I error. The modification consists of a reduction in the critical value when comparing the largest and smallest means. Equivalence of modified and unmodified procedures in error control is demonstrated. The modified procedure is also compared with the alternative of using the unmodified range test without a preliminary F test, and it is shown that each has advantages over the other under some circumstances.
Using Incidence Sampling to Estimate Covariances
Authors: Knapp Thomas R.
Published: March 2008

This paper is an attempt to illustrate the generality of incidence sampling for estimating a parameter whose estimator preserves the unbiasedness of generalized symmetric means, a property which the sample covariance possesses but which the sample correlation coefficient does not. The problem of missing data is also addressed.
On False-Positive and False-Negative Decisions with a Mastery Test
Authors: Wilcox Rand R.
Published: March 2008

Wilcox (1977) examines two methods of estimating the probability of a false-positive or false-negative decision with a mastery test. Both procedures make assumptions about the form of the true score distribution which may not give good results in all situations. In this paper, upper and lower bounds on the two possible error types are described which make no assumption about the form of the true score distribution. Illustrations are given on how these bounds may be used to determine the length of the test.
Identifying a Migration Effect in Complete-Link Hierarchical Clustering
Authors: Hubert Lawrence J. , Baker Frank B.
Published: March 2008

The migration effect refers to a problem encountered in almost all hierarchical clustering schemes that build up a partition hierarchy in a sequential fashion. Specifically, the least refined partitions constructed by such methods have a tendency to “migrate” away from what could be considered more adequate decompositions of the object set being considered. This phenomenon is illustrated with respect to the well-known complete-link method. In addition, several procedures are suggested for identifying the presence of migration and/or for helping evaluate its severity.
Concordance among Categorized Groups of Judges
Authors: Beckett James , Schucany William R.
Published: March 2008

Analyzing the agreement between and within more than two groups of judges is presented in the form of an ANACONDA (Analysis of Concordance) Table. Also, the consensus ranking (for those groups which exhibit some agreement) of the k objects is determined using existing multiple comparison procedures based on Friedman rank sums.
Comparing Techniques for Selecting Successive Values of the Treatment Variable in Sequential Experimentation
Authors: Hummel Thomas J. , Johnston Charles B.
Published: March 2008

Results are given on an investigation of stochastic approximation procedures of the Robbins-Monro type. Attention is focused on formal methods for selecting successive values of a single treatment variable for a specific case in sequential experimentation. Empirical results obtained by means of Monte Carlo methods are used to compare several formal stochastic approximation techniques and stopping rules. Marked differences were found between the five approximation procedures studied. A procedure using afinite memory performed most effectively. Two procedures suggested in the literature were judged to be inefficient under various conditions.
Statistical Inference for two Reliability Indices in Mastery Testing Based on the Beta-Binomial Model
Authors: Huynh Huynh
Published: March 2008

In mastery testing the raw agreement index and the kappa index may be estimated via one test administration when the test scores follow beta-binomial distributions. This paper reports formulae, tables, and a computer program which facilitate the computation of the standard errors of the estimates. Illustrative applications are provided in the form of confidence intervals, hypothesis testing, and minimum sample sizes in reliability studies for mastery tests.
Bayesian Full Rank MANOVA/MANCOVA: An Intermediate Exposition with Interactive Computer Examples
Authors: Woodworth George G.
Published: March 2008

Computation and interpretation of Bayesian full-rank multivariate analysis of variance and covariance is described and illustrated in an exposition intended for readers familiar with univariate analysis of variance and multiple regression.
A Method for Estimating Treatment Effects When Individuals are Growing
Authors: Bryk Anthony S. , Strenio Judith F. , Weisberg Herbert I.
Published: March 2008

In an earlier paper (Bryk & Weisberg, 1976) , we introduced the value-added analysis. This approach built on the notion that many educational programs are dynamic interventions in natural growth processes. In particular, by modeling the natural growth process, we developed an analytic model and estimation procedure for assessing program impact on these growth processes. The present article extends the theory and applications of value-added analysis in several important directions. We present here for practitioners and researchers a coherent overview of the method that emphasizes intuitive rationale and simple data analysis procedures.
Mastery Assessment with Latent Class and Quasi-Independence Models Representing Homogeneous Item Domains
Authors: Bergan John R. , Cancelli Anthony A. , Luiten John W.
Published: March 2008

This article discusses mastery classification involving the use of latent class and quasi-independence models. Extensions of mastery classification techniques developed by Macready and Dayton are presented. These extensions provide decision rules for assigning individuals to latent classes in complex models involving more than two latent categories. Procedures for identifying the minimally acceptable proportion of misclassified individuals in complex latent class models are also detailed.
Statistical Methods for Summarizing Independent Correlational Results
Authors: Viana Marios A. G.
Published: March 2008

This study discusses the description and application of certain combined tests and estimates based on k independent sets of bivariate normal correlational data. We consider the case in which only the sample correlation coefficients are available and the case in which the original paired data are available. A combined test for the correlation parameter based on Fisher's z-transformation is considered, as well as confidence intervals and a combined estimate for the correlation parameter. A test for the homogeneity assumptions is discussed and a table for the power of the additive test based on Fisher's z-transform is presented. Bayesian single and multiparameter estimation are also considered.
A Two-Stage Conditional Estimation Procedure for Unrestricted Latent Class Models
Authors: Macready George B. , Dayton C. Mitchell
Published: March 2008

A variety of latent class models has been presented during the last 10 years which are restricted forms of a more general class of probability models. Each of these models involves an a priori dependency structure among a set of dichotomously scored tasks that define latent class response patterns across the tasks. In turn, the probabilities related to these latent class patterns along with a set of “Omission” and “intrusion” error rates for each task are the parameters used in defining models within this general class.

One problem in using these models is that the defining parameters for a specific model may not be “identifiable.” To deal with this problem, researchers have considered curtailing the form of the model of interest by placing restrictions on the defining parameters.

The purpose of this paper is to describe a two-stage conditional estimation procedure which results in reasonable estimates of specific models even though they may be nonidentifiable. This procedure involves the following stages: (a) establishment of initial parameter estimates and (b) step-wise maximum likelihood solutions for latent class probabilities and classification errors with iteration of this process until stable parameter estimates across successive iterations are obtained.
On “The Analysis of Ranked Data Derived from Completely Randomized Factorial Designs”
Authors: Toothaker Larry E. , Chang Horng-shing
Published: March 2008

Extensions of the Kruskal-Wallis procedure for a factorial design are reviewed and researched under various degrees and kinds of nonnullity. It was found that the distributions of these test statistics are a Function of effects other than those being tested except under the completely null situation and their use is discouraged.
The Use of Analysis of Covariance in Tests of Attribute-by-Treatment Interactions
Authors: Delaney Harold D. , Maxwell Scott E.
Published: March 2008

The use of analysis of covariance (ANCOVA) is examined for attribute-by-treatment interaction (ATI) research, where participants are randomly assigned to treatments but not to levels of the attribute factor. A survey of the ATI literature revealed that ANCOVA was typically not used and was some-times misunderstood. The current paper demonstrates that, contrary to what some have thought, the use of a covariate does not confound the interpretation of the interaction between attribute and treatment, because the expected value of the interaction effect is independent of the particular covariate chosen. Independence occurs regardless of (1) whether the covariate is measured with perfect or less than perfect reliability, (2) whether the attribute variable is discrete or continuous, and (3) whether sample sizes are equal or proportional. The primary implication of these results is that ANCOVA provides a needed means for researchers to achieve powerful designs for assessing ATI's.
Obstacles in Using Randomization Tests in Single-Case Experimentation
Authors: Kazdin Alan E.
Published: March 2008

Edgington has just proposed the use of randomization tests for single-case experimental designs. The specific tests presented require rapidly alternating treatments over time for a given subject. Although randomization tests may provide viable statistical options, several features of single-case research ordinarily would preclude their use. Characteristics of the interventions and responses that are encompassed by the substantive areas of single-case research are likely to preclude shifting rapidly from one treatment to another on a randomized schedule. Also, the possibility of multiple-treatment interference may be especially acute with randomization tests. Finally, the use of randomization tests may compete with the logic of single-case experimental designs. The present paper discusses both applied and methodological restrictions on the use of randomization tests.
Overcoming Obstacles to Single-Subject Experimentation
Authors: Edgington Eugene S.
Published: March 2008

Kazdin (this issue) discussed two types of problems supposedly associated with the use of randomization tests for single-subject experiments: the random introduction of treatments and the repeated alternation of treatments. Both types of problems represent obstacles to single-subject experimentation, generally, not just to the determination of significance by the randomization test procedure. Ways to reduce the adverse effects associated with the random introduction of treatments and the repeated alternation of treatments are presented.
Pairwise Multiple Comparisons in Repeated Measures Designs
Authors: Maxwell Scott E.
Published: March 2008

Five methods of performing pairwise multiple comparisons in repeated measures designs were investigated. Tukey's Wholly Significant Difference (WSD) test, recommended by most experimental design texts, requires that all differences between pairs of means have a common variance. However, this assumption is equivalent to the sphericity condition that is necessary and sufficient for the validity of the mixed-model approach to the omnibus test. Monte Carlo methods revealed that Tukey's WSD leads to an inflated alpha level when the sphericity assumption is not met. Consideration of both Type I and Type II error rates found in the simulated conditions for the five procedures suggests that a Bonferroni method utilizing a separate error term for each comparison should be employed.
Linear Programming in Exploratory Data Analysis
Authors: Armstrong Ronald D. , Frome Edward L. , Sklar Michael G.
Published: March 2008

It has long been popular to utilize the least Squares estimation procedure for fitting the multiple linear regression model to observed data. In this paper, two useful alternatives to least Squares (L2 norm) estimation in exploratory data analysis are examined: least absolute value estimation (L1 norm) and Chebychev (L∞ norm) estimation. Formulating the L1 norm and L∞ norm problems as linear programming problems offers several advantages, including efficient Solution methods using special-purpose Computer codes. An example is provided in which the three procedures are used to fit a line, both with and without an outlier present in the data.
Exact Type 1 Error Rates for Robustness of Student's t Test with Unequal Variances
Authors: Ramsey Philip H.
Published: March 2008

It is noted that disagreements have arisen in the literature about the robustness of the t test in normal populations with unequal variances. Hsu's procedure is applied to determine exact Type I error rates for t. Employing fairly liberal but objective standards for assessing robustness, it is shown that the t test is not always robust to the assumption of equal population variances even when sample sizes are equal. Several guidelines are suggested including the point that to apply t at α = .05 without regard for unequal variances would require equal sample sizes of at least 15 by one of the standards considered. In many cases, especially those with unequal N's, an alternative such as Welch's procedure is recommended.
A Quasi-Equiprobability Model for Measuring Observer Agreement
Authors: Bergan John R.
Published: March 2008

This article describes the use of a quasi-equiprobaaility model in the measurement of observer agreement involving dichotomous coding categories. A measure of agreement is presented which gives the probability of agreement under the assumption that observation pairs reflecting disagreement will be equally probable.
Log-Linear, Logit-Linear Models: A Didactic
Authors: Baker Frank B.
Published: March 2008

The recently developed log-linear model technique for the analysis of contingency tables has many potential applications within educational research. This paper describes the two major models, log-linear and logit-linear, that are employed under this approach. The basic logic of each is developed and illustrative data analyses presented. In addition, the underlying communality of the two schemes is shown. The intent was to provide the reader with perspective that will facilitate understanding the approach and its application to the analysis of qualitative data.
Theory and Practice for the use of Cut-Scores for Personnel Decisions
Authors: Chuang David T. , Chen James J. , Novick Melvin R.
Published: March 2008

Cut-scores are commonly used in industrial personnel selection, academic selection, minimum competence certification testing, and professional licensing, using simple and multiple-person/multiple-job category decision paradigms. Previous approaches have proposed cut-score solutions in a variety of applications using threshold, normal ogive, linear and discrete utility functions. This paper considers these results by investigating conditions on the posterior, likelihood and utility functions required for setting a cut-score in a Bayesian decision approach. Generalizing and extending results of Lehmann, Karlin, Ferguson and others, it is shown that cut-scores are appropriate in a wide range of applications, but they are less than universally appropriate. Following this, a general paradigm and computational algorithm for cut-score solutions is developed under the assumption that the conditions for a cut-score have been satisfied.
Fitting Additive Models to Unbalanced Two-Way Data
Authors: Rubin Donald B. , Stroud T. W. F. , Thayer Dorothy T.
Published: March 2008

A time-saving and space-saving algorithm is presented for computing the sums of squares and estimated cell means under the additive model in a two-way analysis of variance or co-variance with unequal numbers of observations in the cells. The algorithm uses matrices of order no larger than min{r,c}, where r = number of rows and c = number of columns. A Fortran program is available; the key computational device is a special subroutine, LS2WAY, whose FORTRAN code appears in Rubin, Stroud & Thayer (1978) . The procedure is illustrated using high school and college numerical grade averages for 85 feeder high schools over a period of 6 years.