Testing Latent Variable Distribution Fit in IRT Using Posterior Residuals

Scott Monroe
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Scott Monroe: 14707University of Massachusetts Amherst

Journal of Educational and Behavioral Statistics, 2021, vol. 46, issue 3, 374-398

Abstract: This research proposes a new statistic for testing latent variable distribution fit for unidimensional item response theory (IRT) models. If the typical assumption of normality is violated, then item parameter estimates will be biased, and dependent quantities such as IRT score estimates will be adversely affected. The proposed statistic compares the specified latent variable distribution to the sample average of latent variable posterior distributions commonly used in IRT scoring. Formally, the statistic is an instantiation of a generalized residual and is thus asymptotically distributed as standard normal. Also, the statistic naturally complements residual-based item-fit statistics, as both are conditional on the latent trait, and can be presented with graphical plots. In addition, a corresponding unconditional statistic, which controls for multiple comparisons, is proposed. The statistics are evaluated using a simulation study, and empirical analyses are provided.

Keywords: item response theory; model misspecification; latent variable distribution (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:46:y:2021:i:3:p:374-398

DOI: 10.3102/1076998620953764

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