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Lamb, Gordon D. – 2003
This paper discusses the basics of repeated measures designs. Within-subjects designs are compared to between-subjects designs, discussing the advantages and disadvantages of each. Further discussion compares a univariate one-way analysis of variance (ANOVA) with the between-subjects ANOVA and multivariate repeated measures ANOVA. Limitations of…
Descriptors: Analysis of Variance, Comparative Analysis, Research Design
Benton, Roberta L. – 1989
Analyses of data are presented to illustrate the advantages of using a priori or planned comparisons rather than omnibus analysis of variance (ANOVA) tests followed by post hoc or posteriori testing. The two types of planned comparisons considered are planned orthogonal non-trend coding contrasts and orthogonal polynomial or trend contrast coding.…
Descriptors: Analysis of Variance, Comparative Analysis, Research Design, Statistical Analysis
Zwick, Rebecca – 1991
Research in the behavioral and health sciences frequently involves the application of one-factor analysis of variance models. The goal may be to compare several independent groups of subjects on a quantitative dependent variable or to compare measurements made on a single group of subjects on different occasions or under different conditions. In…
Descriptors: Analysis of Variance, Comparative Analysis, Factor Structure, Power (Statistics)

Gaito, John – Educational and Psychological Measurement, 1978
The conduct of multiple post hoc comparison procedures following an analysis of variance is discussed. Various procedures are contrasted in terms of appropriateness, power, and other features. Octhogonal and nonorthogonal comparisons are discussed. (JKS)
Descriptors: Analysis of Variance, Comparative Analysis, Hypothesis Testing, Research Design

O'Brien, Ralph G. – Psychometrika, 1978
Several ways of using traditional analysis of variance to test the homogeneity of variance in factorial designs with equal or unequal cell sizes are compared using theoretical and Monte Carlo results. (Author/JKS)
Descriptors: Analysis of Variance, Comparative Analysis, Hypothesis Testing, Research Design
Benton, Roberta L. – 1990
Basic principles underlying statistical power are examined; and issues pertaining to effect size, sample size, error variance, and significance level are highlighted via the use of specific hypothetical examples. Analysis of variance (ANOVA) and related methods remain popular, although other procedures sometimes have more statistical power against…
Descriptors: Analysis of Variance, Comparative Analysis, Factor Analysis, Power (Statistics)
Wu, Yi-Cheng; McLean, James E. – 1994
By employing a concomitant variable, block designs and analysis of covariance (ANCOVA) can be used to improve the power of traditional analysis of variance (ANOVA) by reducing error. If subjects are randomly assigned to treatments without considering the concomitant variable, an experiment uses a post-hoc approach. Otherwise, an a priori approach…
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Power (Statistics)
Wiley, David E. – 1969
The terms evaluation, assessment, and appraisal are often used interchangeably in research on schools and pupils. Guidelines for their use and some of the similarities and differences in their meanings are explicated. The concept of evaluation is narrowed to refer to use of information on pupil behavior. Four separate components of…
Descriptors: Analysis of Variance, Comparative Analysis, Data Analysis, Evaluation Methods

Werts, Charles E.; Linn, Robert L. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Mathematical Models
Wang, Lin – 1993
The literature is reviewed regarding the difference between planned contrasts, OVA and unplanned contrasts. The relationship between statistical power of a test method and Type I, Type II error rates is first explored to provide a framework for the discussion. The concepts and formulation of contrast, orthogonal and non-orthogonal contrasts are…
Descriptors: Analysis of Variance, Comparative Analysis, Hypothesis Testing, Literature Reviews

Levin, Joel R. – Journal of Educational Measurement, 1975
A set procedure developed in this study is useful in determining sample size, based on specification of linear contrasts involving certain formula treatments. (Author/DEP)
Descriptors: Analysis of Variance, Comparative Analysis, Mathematical Models, Measurement Techniques
Kuehne, Carolyn C. – 1993
There are advantages to using a priori or planned comparisons rather than omnibus multivariate analysis of variance (MANOVA) tests followed by post hoc or a posteriori testing. A small heuristic data set is used to illustrate these advantages. An omnibus MANOVA test was performed on the data followed by a post hoc test (discriminant analysis). A…
Descriptors: Analysis of Variance, Comparative Analysis, Discriminant Analysis, Heuristics

Little, Roderick J. A.; Pullum, Thomas W. – Sociological Methods and Research, 1979
Two methods of analyzing nonorthogonal (uneven cell sizes) cross-classified data sets are compared. The methods are direct standardization and the general linear model. The authors illustrate when direct standardization may be a desirable method of analysis. (JKS)
Descriptors: Analysis of Variance, Comparative Analysis, Mathematical Models, Multiple Regression Analysis

Nelson, Larry R. – Journal of Experimental Education, 1979
The authors state that multiple regression is a powerful method of statistical analysis, provides a strength of relationship index, and should replace analysis of variance (ANOVA) in educational research. They also discuss the coding of categorical variables and available computer programs for multiple regression. (Author/MH)
Descriptors: Analysis of Variance, Classification, Comparative Analysis, Computer Programs

Bray, James H.; And Others – Educational and Psychological Measurement, 1984
The purpose of this study was to determise the relative loss in statistical power of traditional methods of analysis when response-shift bias is present. Five methods of analysis (posttest scores only; postminus pretests; postminus retrospective pretest; postcovaried by pretest; postcovaried by retrospective pretest) were compared. (Author/BW)
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Mathematical Models
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