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Peer reviewedFowler, Robert L. – Educational and Psychological Measurement, 1987
This paper develops a general method for comparing treatment magnitudes for research employing multiple treatment fixed effects analysis of variance designs, which may be used for main effects with any number of levels without regard to directionality. (Author/BS)
Descriptors: Analysis of Variance, Comparative Analysis, Effect Size, Hypothesis Testing
Peer reviewedOlejnik, Stephen F.; Algina, James – Journal of Educational Statistics, 1984
Using computer simulation, parametric analysis of covariance (ANCOVA) was compared to ANCOVA with data transformed using ranks, in terms of proportion of Type I errors and statistical power. Results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity, but practiced significant power differences favored…
Descriptors: Analysis of Covariance, Computer Simulation, Hypothesis Testing, Nonparametric Statistics
Peer reviewedBetz, M. Austin; Elliott, Steven D. – Journal of Educational Statistics, 1984
The method of unweighted means in the multivariate analysis of variance with unequal sample sizes was investigated. By approximating the distribution of the hypothesis sums-of-squares-and-cross-products with a Wishart distribution, multivariate test statistics were derived. Monte Carlo methods and a numerical example illustrate the technique.…
Descriptors: Analysis of Variance, Estimation (Mathematics), Hypothesis Testing, Multivariate Analysis
Peer reviewedYancey, Bernard D. – New Directions for Institutional Research, 1988
The ultimate goal of the institutional researcher is not always to test a research hypothesis, but more often simply to find an appropriate model to gain an understanding of the underlying characteristics and interrelationships of the data. Exploratory data analysis provides a means of accomplishing this. (Author)
Descriptors: Data Interpretation, Higher Education, Hypothesis Testing, Institutional Research
Peer reviewedSclove, Stanley L. – Psychometrika, 1987
A review of model-selection criteria is presented, suggesting their similarities. Some problems treated by hypothesis tests may be more expeditiously treated by the application of model-selection criteria. Multivariate analysis, cluster analysis, and factor analysis are considered. (Author/GDC)
Descriptors: Cluster Analysis, Evaluation Criteria, Factor Analysis, Hypothesis Testing
Peer reviewedYen, Wendy M. – Psychometrika, 1985
An approximate relationship is devised between the unidimensional model used in data analysis and a multidimensional model hypothesized to be generating the item responses. Scale shrinkage is successfully predicted for several sets of simulated data. (Author/LMO)
Descriptors: Difficulty Level, Hypothesis Testing, Item Analysis, Latent Trait Theory
Peer reviewedLevin, Joseph – Multivariate Behavioral Research, 1986
The relation between the power of a significance test in a block design with correlated measurements and the reliability of the measuring instrument is analyzed in terms of the components of variance entering the reliability coefficient and the noncentrality parameter. (Author/LMO)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Power (Statistics)
Peer reviewedHertzog, Christopher; Rovine, Michael – Child Development, 1985
Attempts to distill a growing technical literature on repeated-measures analysis of variance into a few simple principles for selecting an analytic technique. Argues that researchers ought not opt for a general analysis strategy when current computer technology makes it possible to select the optimal analysis technique for a given data set. (RH)
Descriptors: Analysis of Variance, Computer Software, Developmental Psychology, Hypothesis Testing
Peer reviewedOlejnik, Stephen F. – Journal of Experimental Education, 1984
This paper discusses the sample size problem and four factors affecting its solution: significance level, statistical power, analysis procedure, and effect size. The interrelationship between these factors is discussed and demonstrated by calculating minimal sample size requirements for a variety of research conditions. (Author)
Descriptors: Effect Size, Error of Measurement, Hypothesis Testing, Research Design
Peer reviewedKatz, Barry M.; McSweeney, Maryellen – Journal of Experimental Education, 1984
This paper developed and illustrated a technique to analyze categorical data when subjects can appear in any number of categories for multigroup designs. Post hoc procedures to be used in conjunction with the presented statistical test are also developed. The technique is a large sample technique whose small sample properties are as yet unknown.…
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Models, Research Methodology
Peer reviewedHuberty, Carl J. – Educational and Psychological Measurement, 1983
The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant, and a trace, yielding a number of alternative multivariate indices of shared variation. Some problems in the interpretation of tests of multivariate hypotheses are reviewed. (Author/BW)
Descriptors: Analysis of Variance, Correlation, Data Analysis, Hypothesis Testing
Peer reviewedBlumberg, Carol Joyce; Porter, Andrew C. – Journal of Experimental Education, 1983
The general class of continuous growth models are described and examples representative of growth models suggested for various types of academic and/or physical growth are given. The fan spread hypothesis is discussed in relationship to natural growth models, as well as differential linear growth. (PN)
Descriptors: Achievement Gains, Data Analysis, Evaluation Methods, Hypothesis Testing
Peer reviewedStavig, Gordon R. – Journal of Experimental Education, 1983
A method is developed for testing a priori multiple regression models. The method allows one to specify in advance as many unstandardized or standardized coefficients as one wants to and allows the remaining slopes to be free to vary. (Author/PN)
Descriptors: Computer Programs, Hypothesis Testing, Mathematical Models, Multiple Regression Analysis
Peer reviewedStrohmer, Douglas C.; Newman, Lisa J. – Journal of Counseling Psychology, 1983
Reports two experiments relevant to the questioning strategies counselors use in testing their hypotheses about clients. Results supported the idea that counselors are able to take a tentative hypothesis about a client and test its accuracy against additional independent, unbiased observations of the client. (LLL)
Descriptors: College Students, Counseling Techniques, Counselor Performance, Data Collection
Peer reviewedHakstian, A. Ralph; Whalen, Thomas E. – Psychometrika, 1976
Details of a reasonably precise normalization technique for coefficient alpha are outlined, along with methods for estimating the variance of the normalized statistic. These procedures lead to the K-sample significance test. (RC)
Descriptors: Analysis of Variance, Comparative Analysis, Error Patterns, Hypothesis Testing


