ArticlePsychometrika2023
Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT.
Article in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Modeling of Years of Cohabitation, Children Ever Born, and Number of Living Children of Women of Reproductive Age Using Trivariate Poisson Regression.Health science reports · 2026Article
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- Development and validation of a nomogram model to predict postoperative delirium after resection of esophageal cancer.Scientific reports · 2025Article
- Testing measurement invariance in a conditional likelihood framework by considering multiple covariates simultaneously.Behavior research methods · 2025Article
- An analysis of knowledge, attitudes, practice and influencing factors for tuberculosis prevention and control among Hainan University students.Frontiers in public health · 2025Article
- Factors associated with intrinsic capacity impairment in hospitalized older adults: a latent class analysis.BMC geriatrics · 2024Article
- Article
- Occupational protection behavior and its influencing factors of newly recruited nurses.BMC medical education · 2023Article
- To Check or Not to Check? A Comment on the Contemporary Psychometrics (ConPsy) Checklist for the Analysis of Questionnaire Items.European journal of investigation in health, psychology and education · 2023Article
- Confidence in dementia care and care approach toward dementia among the nursing staff in long-term care facilities in China: a cross-sectional survey.Frontiers in public health · 2023Article
- Analysis of Current Situation Regarding Scientific Fitness Literacy of Nurses in Sports Medicine Integration.Risk management and healthcare policy · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.