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Alternatives for Mixed-Effects Meta-Regression Models in the Reliability Generalization Approach

Author

Listed:
  • José Antonio López-López

    (University of Murcia, Spain)

  • Juan Botella

    (Autonomous University of Madrid, Spain)

  • Julio Sánchez-Meca

    (University of Murcia, Spain)

  • Fulgencio Marín-Martínez

Abstract

Since heterogeneity between reliability coefficients is usually found in reliability generalization studies, moderator analyses constitute a crucial step for that meta-analytic approach. In this study, different procedures for conducting mixed-effects meta-regression analyses were compared. Specifically, four transformation methods for the reliability coefficients, two estimators of the residual between-studies variance, and two methods for testing regression coefficients significance were combined in a Monte Carlo simulation study. The different methods were compared in terms of bias and mean square error (MSE) of the slope estimates, and Type I error and statistical power rates for the slope statistical tests. The results of the simulation study did not vary as a function of the residual variance estimator. All transformation methods provided negatively biased estimates, but both bias and MSE were reasonably small in all cases. In contrast, important differences were found regarding statistical tests, with the method proposed by Knapp and Hartung showing a better adjustment to the nominal significance level and higher power rates than the standard method.

Suggested Citation

  • José Antonio López-López & Juan Botella & Julio Sánchez-Meca & Fulgencio Marín-Martínez, 2013. "Alternatives for Mixed-Effects Meta-Regression Models in the Reliability Generalization Approach," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 443-469, October.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:5:p:443-469
    DOI: 10.3102/1076998612466142
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    References listed on IDEAS

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    Cited by:

    1. Peter M. Bentler, 2016. "Covariate-free and Covariate-dependent Reliability," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 907-920, December.

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