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Covariate-free and Covariate-dependent Reliability

Published online by Cambridge University Press:  01 January 2025

Peter M. Bentler*
Affiliation:
University of California, Los Angeles
*
Correspondence should be made to Peter M. Bentler, Departments of Psychology and Statistics, University of California, Los Angeles, 4627 Franz Hall, PO Box 951563, Los Angeles, CA 90095-1563 USA.

Abstract

Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.

Type
Article
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

Based on the invited Lifetime Achievement Award Address, International Meeting of the Psychometric Society 2014, Madison WI, July 23, 2014.

References

Bentler, P. M. (1968). Alpha-maximized factor analysis (Alphamax): Its relation to alpha and canonical factor analysis. Psychometrika, 33, 335345.CrossRefGoogle ScholarPubMed
Bentler, P. M. (1972). A lower-bound method for the dimension-free measurement of internal consistency. Social Science Research, 1, 343357.CrossRefGoogle Scholar
Bentler, P. M. Lee, S.- Y. (2007). Covariance structure models for maximal reliability of unit-weighted composites. Handbook of latent variable and related models, Amsterdam: North-Holland. 119.Google Scholar
Bentler, P. M. (2009). Alpha, dimension-free, and model-based internal consistency reliability. Psychometrika, 74, 137143.CrossRefGoogle ScholarPubMed
Bentler, P. M. (2016). Specificity-enhanced reliability coefficients. Psychological Methods. http://dx.doi.org/10.1037/met0000092.CrossRefGoogle Scholar
Bentler, P. M., Weeks, D. G. (1980). Linear structural equations with latent variables. Psychometrika, 45, 289308.CrossRefGoogle Scholar
Bentler, P. M., Woodward, J. A. (1980). (2015). Inequalities among lower bounds to reliability: With applications to test construction and factor analysis. Psychometrika, 45, 249267.CrossRefGoogle Scholar
Bentler, P. M., Wu, EJC EQS 6.3 structural equations program, Temple City, CA: Multivariate SoftwareGoogle Scholar
Beretvas, S. N., Pastor, D. A. (2003). Using mixed-effects models in reliability generalization studies. Educational and Psychological Measurement, 63, 7595.CrossRefGoogle Scholar
Bonett, D. G. (2010). Varying coefficient meta-analytic methods for alpha reliability. Psychological Methods, 15, 368385.CrossRefGoogle ScholarPubMed
Botella, J., Suero, M., Gambara, H. (2010). Psychometric inferences from a meta-analysis of reliability and internal consistency coefficients. Psychological Methods, 15, 386397.CrossRefGoogle ScholarPubMed
Brannick, M. T., Zhang, N. (2013). (2001). Bayesian meta-analysis of coefficient alpha. Research Synthesis Methods, 4, 198207.CrossRefGoogle ScholarPubMed
Brennan, R. L. Generalizability theory, New York: SpringerGoogle Scholar
Byrne, B. M., Shavelson, R. J., Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456466.CrossRefGoogle Scholar
Cheung, G. W., Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233255.CrossRefGoogle Scholar
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297334.CrossRefGoogle Scholar
Deng, L., & Yuan, K. -H. (2015). Multiple-group analysis for structural equation modeling with dependent samples. Structural Equation Modeling. doi:10.1080/10705511.2014.950534.CrossRefGoogle Scholar
Geldhof, G. J., Preacher, K. J., Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19, 7291.CrossRefGoogle Scholar
Guttman, L. A. (1945). A basis for analyzing test–retest reliability. Psychometrika, 10, 255282.CrossRefGoogle ScholarPubMed
Heise, D. R., Bohrnstedt, G. W., Borgatta, E. F., Bohrnstedt, G. W. (1970). Validity, invalidity, and reliability. Sociological methodology, San Francisco: Jossey-Bass. 104129.Google Scholar
Hsu, H.- Y., Kwok, O.- M. Jr, Lin, H., Acosta, S. (2015). Detecting misspecified multilevel structural equation models with common fit indices: A Monte Carlo study. Multivariate Behavioral Research, 50, 197215.CrossRefGoogle ScholarPubMed
Hunt, T. D., Bentler, P. M. (2015). Quantile lower bounds to reliability based on locally optimal splits. Psychometrika, 80, 182195.CrossRefGoogle ScholarPubMed
Jackson, P. H. (1979). A note on the relation between coefficient alpha and Guttman’s "split-half" lower bounds. Psychometrika, 44, 251252.CrossRefGoogle Scholar
Jackson, P. H., Agunwamba, C. C. (1977). Lower bounds for the reliability of the total score on a test composed of non-homogeneous items: I. Algebraic lower bounds. Psychometrika, 42, 567578.CrossRefGoogle Scholar
Jak, S., Oort, F. J., Dolan, C. V. (2013). A test for cluster bias: Detecting violations of measurement invariance across clusters in multilevel data. Structural Equation Modeling, 20, 265282.CrossRefGoogle Scholar
Jamshidian, M., Bentler, P. M. (1998). A quasi-Newton method for minimum trace factor analysis. Journal of Statistical Computation and Simulation, 62, 7389.CrossRefGoogle Scholar
Jöreskog, K. G. (1971). (1996). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109133.CrossRefGoogle Scholar
Jöreskog, K. G., Sörbom, D. G. LISREL 8 user’s guide, Chicago: Scientific Software InternationalGoogle Scholar
Kaiser, H. F., Caffrey, J. (1965). Alpha factor analysis. Psychometrika, 30, 114.CrossRefGoogle ScholarPubMed
Kelley, K., Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21, 6992.CrossRefGoogle ScholarPubMed
Labouvie, E., Ruetsch, C. (1995). Testing for equivalence of measurement scales: Simple structure and metric invariance reconsidered. Multivariate Behavioral Research, 30, 6376.CrossRefGoogle ScholarPubMed
Li, L., Bentler, P. M. (2011). The greatest lower bound to reliability: Corrected and resampling estimators. Modelling and Data Analysis, 1, 87104.Google Scholar
Liang, J., Bentler, P. M. (2004). An EM algorithm for fitting two-level structural equation models. Psychometrika, 69, 101122.CrossRefGoogle Scholar
López-López, J. A., Botella, J., Sánchez-Meca, J., Marín-Martínez, F. (2013). Alternatives for mixed-effects meta-regression models in the reliability generalization approach: A simulation study. Journal of Educational and Behavioral Statistics, 38, 443469.CrossRefGoogle Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33, 337346.CrossRefGoogle Scholar
McDonald, R. P. (1970). (1999). The theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical and Statistical Psychology, 23, 121.CrossRefGoogle Scholar
McDonald, R. P. Test theory: A unified treatment, Mahwah, NJ: ErlbaumCrossRefGoogle Scholar
Meade, A. W., Johnson, E. C., Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568592.CrossRefGoogle ScholarPubMed
Merkle, E. C., Zeileis, A. (2013). (2011). Tests of measurement invariance without subgroups: A generalization of classical methods. Psychometrika, 78, 5982.CrossRefGoogle ScholarPubMed
Millsap, R. E. Statistical approaches to measurement invariance, New York: RoutledgeCrossRefGoogle Scholar
Posthuma, D., Baaré, WFC, Hulshoff Pol, H. E., Kahn, R. S., Boomsma, D. I., De Geus, EJC (2003). (2011). Genetic correlations between brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed. Twin Research, 6, 131139.CrossRefGoogle ScholarPubMed
Raykov, T., Marcoulides, G. A. Introduction to psychometric theory, New York: RoutledgeCrossRefGoogle Scholar
Raykov, T., Marcoulides, G. A. (2013). Meta-analysis of scale reliability using latent variable modeling. Structural Equation Modeling, 20, 338353.CrossRefGoogle Scholar
Raykov, T., Marcoulides, G. A., Millsap, R. E. (2012). Factorial invariance in multiple populations: A multiple testing procedure. Educational and Psychological Measurement, 73, 713727.CrossRefGoogle Scholar
Ryu, E., West, S. G. (2009). Level-specific evaluation of model fit in multilevel structural equation modeling. Structural Equation Modeling, 16, 583601.CrossRefGoogle Scholar
Sawilowsky, S. S. (2000). Psychometrics versus datametrics: Comment on Vacha-Haase’s "reliability generalization" method and some EPM editorial policies. Educational and Psychological Measurement, 60, 157173.CrossRefGoogle Scholar
Schmidt, F. L., Hunter, J. E. (1977). Development of a general solution to the problem of generalization. Journal of Applied Psychology, 62, 529540.CrossRefGoogle Scholar
Schweig, J. (2014). (1991). Multilevel factor analysis by model segregation: New applications for robust test statistics. Journal of Educational and Behavioral Statistics, 39, 394422.CrossRefGoogle Scholar
Shavelson, R. J., Webb, N. Generalizability theory: A primer, Thousand Oaks, CA: SageGoogle Scholar
Sörbom, D. (1974). A general method for studying differences in factor means and factor structures between groups. British Journal of Mathematical and Statistical Psychology, 27, 229239.CrossRefGoogle Scholar
Tarkkonen, L., Vehkalahti, K. (2005). Measurement errors in multivariate measurement scales. Journal of Multivariate Analysis, 96, 172189.CrossRefGoogle Scholar
Thompson, B. (1994). Guidelines for authors. Educational and Psychological Measurement, 54, 837847.Google Scholar
Thompson, B., Vacha-Haase, T. (2000). Psychometrics is datametrics: The test is not reliable. Educational and Psychological Measurement, 60, 174195.CrossRefGoogle Scholar
Vacha-Haase, T. (1998). Reliability generalization: Exploring variance in measurement error affecting score reliability across studies. Educational and Psychological Measurement, 58, 620.CrossRefGoogle Scholar
Vacha-Haase, T., Thompson, B. (2011). Score reliability: A retrospective look back at 12 years of reliability generalization studies. Measurement and Evaluation in Counseling and Development, 44, 159168.CrossRefGoogle Scholar
Van de Schoot, R., Kluytmans, A., Tummers, L., Lugtig, P., Hox, J., Muthén, B. (2013). Facing off with Scylla and Charybdis: A comparison of scalar, partial, and the novel possibility of approximate measurement invariance. Frontiers of Psychology, 4, 770CrossRefGoogle ScholarPubMed
Warrens, M. J. (2014). On Cronbach’s alpha as the mean of all possible k-split alphas. Advances in Statistics, 1–5. doi:10.1155/2014/742863.CrossRefGoogle Scholar
Werts, C. E., Rock, D. R., Linn, R. L., Jöreskog, K. G. (1978). A general method of estimating the reliability of a composite. Educational and Psychological Measurement, 38, 933938.CrossRefGoogle Scholar
Wilkinson, L.APA Task Force on Statistical Inference (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594604.CrossRefGoogle Scholar
Woodhouse, B., Jackson, P. H. (1977). Lower bounds for the reliability of the total score on a test composed of non-homogeneous items. II: A search procedure to locate the greatest lower bound. Psychometrika, 42, 579591.CrossRefGoogle Scholar
Yuan, K.- H., Bentler, P. M. (2003). Eight test statistics for multilevel structural equation models. Computational Statistics & Data Analysis, 44, 89107.CrossRefGoogle Scholar
Yuan, K.- H., Bentler, P. M. (2007). Multilevel covariance structure analysis by fitting multiple single-level models. Sociological Methodology, 37, 5382.CrossRefGoogle Scholar