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Item response theory (IRT) is the statistical paradigm underlying a dominantfamily of generative probabilistic models for test responses, used to quantifytraits in individuals relative to target populations. The graded response model(GRM) is a particular IRT model that is used for ordered polytomous testresponses. Both the development and the application of the GRM and other IRTmodels require statistical decisions. For formulating these models(calibration), one needs to decide on methodologies for item selection,inference, and regularization. For applying these models (test scoring), oneneeds to make similar decisions, often prioritizing computational tractabilityand/or interpretability. In many applications, such as in the Work DisabilityFunctional Assessment Battery (WD-FAB), tractability implies approximating anindividual's score distribution using estimates of mean and variance, andobtaining that score conditional on only point estimates of the calibratedmodel. In this manuscript, we evaluate the calibration and scoring of modelsunder this common use-case using Bayesian cross-validation. Applied to theWD-FAB responses collected for the National Institutes of Health, we assess thepredictive power of implementations of the GRM based on their ability to yield,on validation sets of respondents, ability estimates that are most predictiveof patterns of item responses. Our main finding indicates that regularizedBayesian calibration of the GRM outperforms the regularization-free empiricalBayesian procedure of marginal maximum likelihood. We also motivate the use ofcompactly supported priors in test scoring.

Regularized Bayesian calibration and scoring of the WD-FAB IRT model improves predictive performance over marginal maximum likelihood