Type I error rates and statistical power for the univariate F test and the James second-order test were estimated for the two-factor fixed-effects completely randomized design. Results reveal that the F test Type I error rate can exceed the nominal significance level when cell variances differ. (SLD)

Reference-reliability relates variability due to change and error. It indicates whether some suspected change can be reliably differentiated from random fluctuations. A means by which the process of change can be measured at different points in time is outlined, using empirical data. (TJH)

The Shine-Bower single subject ANOVA is extended to a multivariate case, with one example assuming between-variate dependencies among within-subject errors and the second assuming no between-variate dependencies among within-subject errors. Standard and simplified multivariate ANOVA procedures are used, respectively. (Author/CM)

Some results on how the Alexander-Govern heteroscedastic analysis of variance (ANOVA) procedure (R. Alexander and D. Govern, 1994) performs under nonnormality are presented. This method can provide poor control of Type I errors in some cases, and in some situations power decreases as differences among the means get large. (SLD)

Type I error rates and power were estimated for 10 tests of variance equality under various combinations of the following factors: similar and dissimilar distributional forms, equal and unequal means, and equal and unequal sample sizes. (TJH)

This book presents a comprehensive overview of univariate and multivariate generalizability theory, a psychometric model that provides a powerful approach to the analysis of errors of measurement through the use of random-effects and mixed-model analysis of variance. (SLD)

Incidence sampling is a parsimonious method whereby a large number of examinees can be measured on many variables (such as test items) to assess group characteristics. Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. (Author/JKS)

The effects of under and overrotation on common factor loading stability under three levels of common variance and three levels or error are examined. Four representative factor matrices were selected. Results suggested that matrices which account for large amounts of common variance tend to have stable factor loadings. (Author/RL)

This paper describes a BASIC computer program that computes power for any combination of effect size, degrees of freedom for hypothesis, degrees of freedom for error, and alpha level. As a consequence of the algorithm, an approximation to the critical value of the Bonferroni F-test is also computed. (Author/JAZ)

For Y. Yao's and G. S. James' tests, Type I error rates were estimated for various combinations of the number of variables, sample-size and sample-size-to-variables ratios, and heteroscedasticity. These tests are alternatives to Hotelling's T(sup 2) and are intended for use when variance-covariance matrices are unequal for two independent samples.…

This article focuses on steps in conducting empirical-analytic research and the problems of controlling for or estimating three sources of error: the amount of measurement error, research design error, and the amount of statistical or sampling error. (Author/CT)

For the conditions investigated in the study, the parametric ANCOVA was typically the procedure of choice both as a test of equality of conditional means and as a test of equality of conditional distributions. (Author/LMO)

Khuri's and Satterthwaite's methods of obtaining confidence intervals of variance components are compared. The article discusses that Khuri's method may be applied to obtain confidence intervals for the variance components and other linear functions of the expected mean squares used in generalizability theory. (Author/JAZ)

Formulas are provided for estimating statistical power of a test of significance for the difference among means under a variety of conditions. A table for quick power estimates that require no computation for comparing two means in analysis of variance and analysis of covariance is included. (TJH)

The unique conceptual framework and language of generalizability theory are presented. While this chapter is relevant to any area in which generalizability theory is applicable, it emphasizes evaluation research, and most examples come from that area. (Author/PN)