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

Felix Zimmer (), Clemens Draxler and Rudolf Debelak
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Felix Zimmer: University of Zurich
Clemens Draxler: The Health and Life Sciences University
Rudolf Debelak: University of Zurich

Psychometrika, 2023, vol. 88, issue 4, No 7, 1249-1298

Abstract: Abstract 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 extensive simulation studies in three practically relevant settings, i.e., testing a Rasch model against a 2PL model, testing for differential item functioning, and testing a partial credit model against a generalized partial credit model. The observed distributions of the test statistics and the power of the tests agreed well with the predictions by the proposed methods in sufficiently large samples. We provide an openly accessible R package that implements the methods for user-supplied hypotheses.

Keywords: marginal maximum likelihood; item response theory; power analysis; Wald test; score test; likelihood ratio; gradient test (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-022-09883-5

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