Journal
PSYCHOMETRIKA
Volume 87, Issue 4, Pages 1318-1342Publisher
SPRINGER
DOI: 10.1007/s11336-022-09847-9
Keywords
Omega; Bayesian; reliability
Categories
Funding
- National Institute On Aging of the National Institutes of Health [R01AG050720]
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This study proposes a novel method for estimating and modeling case-specific reliability without repeated measurements or parallel tests. By using a reliability factor model and Gaussian process modeling, the study provides insights into the variability of reliability and its relationship with latent factors.
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.
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