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Reducing Attenuation Bias in Regression Analyses Involving Rating Scale Data via Psychometric Modeling

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  • Cees A. W. Glas

    (University of Twente)

  • Terrence D. Jorgensen

    (University of Amsterdam)

  • Debby ten Hove

    (Vrije Universiteit Amsterdam)

Abstract

Many studies in fields such as psychology and educational sciences obtain information about attributes of subjects through observational studies, in which raters score subjects using multiple-item rating scales. Error variance due to measurement effects, such as items and raters, attenuate the regression coefficients and lower the power of (hierarchical) linear models. A modeling procedure is discussed to reduce the attenuation. The procedure consists of (1) an item response theory (IRT) model to map the discrete item responses to a continuous latent scale and (2) a generalizability theory (GT) model to separate the variance in the latent measurement into variance components of interest and nuisance variance components. It will be shown how measurements obtained from this mixture of IRT and GT models can be embedded in (hierarchical) linear models, both as predictor or criterion variables, such that error variance due to nuisance effects are partialled out. Using examples from the field of educational measurement, it is shown how general-purpose software can be used to implement the modeling procedure.

Suggested Citation

  • Cees A. W. Glas & Terrence D. Jorgensen & Debby ten Hove, 2024. "Reducing Attenuation Bias in Regression Analyses Involving Rating Scale Data via Psychometric Modeling," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 42-63, March.
    • Handle: RePEc:spr:psycho:v:89:y:2024:i:1:d:10.1007_s11336-024-09967-4
    DOI: 10.1007/s11336-024-09967-4
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    References listed on IDEAS

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    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
    3. A. Béguin & C. Glas, 2001. "MCMC estimation and some model-fit analysis of multidimensional IRT models," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 541-561, December.
    4. Hanneke Geerlings & Cees Glas & Wim Linden, 2011. "Modeling Rule-Based Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 337-359, April.
    5. Jean-Paul Fox & Cees Glas, 2003. "Bayesian modeling of measurement error in predictor variables using item response theory," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 169-191, June.
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