An Optimized Bayesian Hierarchical Two-Parameter Logistic Model for Small-Sample Item Calibration.

ABSTRACT: Accurate item calibration in models of item response theory (IRT) requires rather large samples. For instance, N>500 respondents are typically recommended for the two-parameter logistic (2PL) model. Hence, this model is considered a large-scale application, and its use in small-sample contexts is limited. Hierarchical Bayesian approaches are frequently proposed to reduce the sample size requirements of the 2PL. This study compared the small-sample performance of an optimized Bayesian hierarchical 2PL (H2PL) model to its standard inverse Wishart specification, its nonhierarchical counterpart, and both unweighted and weighted least squares estimators (ULSMV and WLSMV) in terms of sampling efficiency and accuracy of estimation of the item parameters and their variance components. To alleviate

SUBMITTER: Konig C 

PROVIDER: S-EPMC7262992 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature