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Pituch, Keenan A.; Whittaker, Tiffany A.; Stapleton, Laura M. – Multivariate Behavioral Research, 2005
A Monte Carlo study extended the research of MacKinnon, Lockwood, Hoffman, West, and Sheets (2002) for single-level designs by examining the statistical performance of four methods to test for mediation in a multilevel experimental design. The design studied was a two-group experiment that was replicated across several sites, included a single…
Descriptors: Research Design, Intervals, Monte Carlo Methods, Hypothesis Testing

Lunneborg, Clifford E.; Tousignant, James P. – Multivariate Behavioral Research, 1985
This paper illustrates an application of Efron's bootstrap to the repeated measures design. While this approach does not require parametric assumptions, it does utilize distributional information in the sample. By appropriately resampling from study data, the bootstrap may determine accurate sampling distributions for estimators, effects, or…
Descriptors: Hypothesis Testing, Research Design, Research Methodology, Sampling

Keselman, H. J. – Multivariate Behavioral Research, 1982
The need for multiple comparison procedures for repeated measures means employing a pooled estimate of error variance to conform to the sphericity assumptions of the design in order to provide a valid test is discussed. An alternative approach which does not require this assumption is presented. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design

Revelle, William; Rocklin, Thomas – Multivariate Behavioral Research, 1979
A new procedure for determining the optimal number of interpretable factors to extract from a correlation matrix is introduced and compared to more conventional procedures. The new method evaluates the magnitude of the very simple structure index of goodness of fit for factor solutions of increasing rank. (Author/CTM)
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Research Design

Mendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1974
Descriptors: Comparative Analysis, Hypothesis Testing, Monte Carlo Methods, Research Design

Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1984
Several general correlation patterns are shown which give exact F tests in an analysis of variance (ANOVA) procedure. They are the most general correlation patterns one can assume in a one-way and two-way layout and still have the F tests be valid. (Author/BW)
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Data Interpretation

Algina, James – Multivariate Behavioral Research, 1994
Alternative tests are presented for the between-by-within interaction null hypothesis and for two within-subjects main effects null hypothesis in a split plot design. Estimated Type I error rates for the interaction tests and for several tests of the second null hypothesis are reported. (SLD)
Descriptors: Equations (Mathematics), Error of Measurement, Estimation (Mathematics), Hypothesis Testing

Eiting, Mindert H.; Mellenbergh, Gideon J. – Multivariate Behavioral Research, 1980
Using reasonable values for the parameters in both null and alternative hypotheses about covariance matrices, an optimal and feasible combination of number of subjects, significance level, and power of the test were determined for an empirical study of the measurement of musical ability. (Author/BW)
Descriptors: Education Majors, Foreign Countries, Higher Education, Hypothesis Testing

Green, Samuel B. – Multivariate Behavioral Research, 1991
An evaluation of the rules-of-thumb used to determine the minimum number of subjects required to conduct multiple regression analyses suggests that researchers who use a rule of thumb rather than power analyses trade simplicity of use for accuracy and specificity of response. Insufficient power is likely to result. (SLD)
Descriptors: Correlation, Effect Size, Equations (Mathematics), Estimation (Mathematics)