A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes

Stephen M. Quintana; Scott E. Maxwell

doi:10.2307/1165177

January 1, 1994Journal of Educational Statistics

A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes

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Key Points

To compare the performance of seven epsilon-adjustment procedures in univariate repeated measures analysis of variance under small sample size conditions.
Conducted Monte Carlo simulations modeling repeated measures designs across various small sample sizes.
Evaluated seven distinct epsilon-adjustment procedures, including standard Greenhouse-Geisser and Huynh-Feldt corrections along with alternative estimators.
Assessed empirical Type I error rates and statistical power across multiple degrees of sphericity violation.
Demonstrated substantial differences in empirical Type I error control across the seven adjustment methods when sample sizes were small.
Identified specific adjustments that effectively control liberal error rates without causing excessive loss of statistical power under severe nonsphericity.

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Implication

Simulation study evaluates seven epsilon adjustments in repeated measures designs with small samples, highlighting optimal procedures for controlling error rates under nonsphericity.

Abstract

Stephen M. Quintana, Scott E. Maxwell, A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes, Journal of Educational Statistics, Vol. 19, No. 1 (Spring, 1994), pp. 57-71

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Quintana et al. (1994) studied this question.

synapsesocial.com/papers/6a9f7f3824ee8000382b6217 https://doi.org/10.2307/1165177

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A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes

Key Points

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Cite This Study

A Monte Carlo Comparison of Seven ε-Adjustment Procedures in Repeated Measures Designs with Small Sample Sizes

Key Points

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Implication

Abstract

Discussion

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