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Peer reviewedFeldt, Leonard S. – Psychometrika, 1980
Procedures are developed for testing the hypothesis that Cronbach's alpha reliability coefficient is equal for two tests given to the same subjects. (Author/JKS)
Descriptors: Error of Measurement, Hypothesis Testing, Measurement, Statistical Significance
Peer reviewedDyer, Frank J. – Educational and Psychological Measurement, 1980
Power analysis is in essence a technique for estimating the probability of obtaining a specific minimum observed effect size. Power analysis techniques are applied to research planning problems in test reliability studies. A table for use in research planning and hypothesis testing is presented. (Author)
Descriptors: Hypothesis Testing, Mathematical Formulas, Power (Statistics), Probability
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 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
Peer reviewedHofmann, Richard J. – Educational and Psychological Measurement, 1979
The Guttman scale is discussed from the viewpoint of errors in response patterns. The errors are assumed to be distributed as a binomial. A double-barreled significance test is suggested having two probabilities: high probability and low probability. (Author)
Descriptors: Error Patterns, Hypothesis Testing, Probability, Psychometrics
PDF pending restorationLam, Tony C. M. – 1981
The objective of this paper is to examine the relationship between the unreliability of difference scores and the power of tests of significance in an attempt to determine the validity of the paradox for the measurement of change presented by Overall and Woodward: that the power of tests of significance is maximum when the reliability of the…
Descriptors: Achievement Gains, Correlation, Error of Measurement, Hypothesis Testing
Raffeld, Paul; Reynolds, William M. – 1977
The pretest-posttest design referred to as Design 2 by Campbell and Stanley (1963) is commonly used in educational research and evaluation. The tenability of the assumption of a zero population difference commonly used with this design is questioned. A nonzero population estimate based on the mean difference observed in test-retest reliability…
Descriptors: Control Groups, Correlation, Experimental Groups, Hypothesis Testing
Samph, Thomas; Sayles, Felton – 1974
The intent of this investigation was to perform a validation study to determine whether RACE (Racial Attitude and Cultural Expression test) differentiates between primary grade students identified as having negative and positive attitudes. Students were categorized by a combination of administrator, teacher and clinical assessment into a negative…
Descriptors: Attitude Measures, Black Youth, Elementary Education, Elementary School Students
Dunivant, Noel – 1979
Eight different methods are reviewed for determining whether two or more tests are equivalent measures. These methods vary in restrictiveness from the Wilks-Votaw test of compound symmetry (which requires that all means, variances, and covariances are equal), to Joreskog's theory of congeneric tests (which requires only that the tests are measures…
Descriptors: Analysis of Variance, Comparative Analysis, Error of Measurement, Evaluation Methods
Olejnik, Stephen F.; Porter, Andrew C. – 1978
The statistical properties of two methods of estimating gain scores for groups in quasi-experiments are compared: (1) gains in scores standardized separately for each group; and (2) analysis of covariance with estimated true pretest scores. The fan spread hypothesis is assumed for groups but not necessarily assumed for members of the groups.…
Descriptors: Academic Achievement, Achievement Gains, Analysis of Covariance, Analysis of Variance


