Back to publications

extended · article · 2022

Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT

Felix Zimmer; Clemens Draxler; Rudolf Debelak; Ivor W. Molenaar

The Wald, likelihood ratio, score, and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This allows the application to a variety of IRT models, which are commonly used in practice, e.g., in large-scale educational assessments. An analytical method utilizes the asymptotic distributions of the statistics under alternative hypotheses. We also provide a sampling-based approach for applications where the analytical approach is computationally infeasible. This can be the case with 20 or more items, since the computational load increases exponentially with the number of items. We performed exte...

Partial Credit ModelDIFCATEducation
APA citation

Felix Zimmer, Clemens Draxler, Rudolf Debelak, & Ivor W. Molenaar (2022). Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT. Psychometrika, 88(4), 1249-1298. https://doi.org/10.1007/s11336-022-09883-5