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The Reliability Factor: Modeling Individual Reliability with Multiple Items from a Single Assessment

Published online by Cambridge University Press:  01 January 2025

Stephen R. Martin Open the ORCID record for Stephen R. Martin [Opens in a new window]  and
Philippe Rast
Stephen R. Martin*
Affiliation:
University Of California, Davis
Philippe Rast
Affiliation:
University Of California, Davis
*
Correspondence should be made to Stephen R. Martin, Department of Psychology, University of California, Davis, 135 Young Hall, 1 Shields Avenue, Davis, CA 95616, USA. Email: stephenSRMMartin@gmail.com
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Abstract

Reliability is a crucial concept in psychometrics. Although it is typically estimated as a single fixed quantity, previous work suggests that reliability can vary across persons, groups, and covariates. We propose a novel method for estimating and modeling case-specific reliability without repeated measurements or parallel tests. The proposed method employs a “Reliability Factor” that models the error variance of each case across multiple indicators, thereby producing case-specific reliability estimates. Additionally, we use Gaussian process modeling to estimate a nonlinear, non-monotonic function between the latent factor itself and the reliability of the measure, providing an analogue to test information functions in item response theory. The reliability factor model is a new tool for examining latent regions with poor conditional reliability, and correlates thereof, in a classical test theory framework.

Keywords

OmegaBayesianreliability

Information

Type
Theory and Methods
Information
Psychometrika , Volume 87 , Issue 4 , December 2022 , pp. 1318 - 1342
Copyright
Copyright © 2022 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Research reported in this publication was supported by the National Institute On Aging of the National Institutes of Health under Award Number R01AG050720 to PR. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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