ArticleBioMed research international2020
A Comparative Study of the Bias Correction Methods for Differential Item Functioning Analysis in Logistic Regression with Rare Events Data.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Association of Mean Arterial Pressure (MAP) with Mortality in Patients with Liver Cirrhosis Awaiting Transplantation.Journal of clinical medicine · 2026Article
- An Investigation of Differential Item Functioning in the Infection Control Contact Isolation Precaution Instrument Among Nurses and Physicians.BioMed research international · 2026Article
- Promise and Peril of Population Genomics for the Development of Genome-First Approaches in Mendelian Cardiovascular Disease.Circulation. Genomic and precision medicine · 2021Review
- A Machine Learning Approach to Assess Differential Item Functioning in Psychometric Questionnaires Using the Elastic Net Regularized Ordinal Logistic Regression in Small Sample Size Groups.BioMed research international · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
The logistic regression (LR) model for assessing differential item functioning (DIF) is highly dependent on the asymptotic sampling distributions. However, for rare events data, the maximum likelihood estimation method may be biased and the asymptotic distributions may not be reliable. In this study, the performance of the regular maximum likelihood (ML) estimation is compared with two bias correction methods including weighted logistic regression (WLR) and Firth's penalized maximum likelihood (PML) to assess DIF for imbalanced or rare events data. The power and type I error rate of the LR model for detecting DIF were investigated under different combinations of sample size, moderate and severe magnitudes of uniform DIF (DIF = 0.4 and 0.8), sample size ratio, number of items, and the imbalanced degree (
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