A Comparative Study of the Bias Correction Methods for Differential Item Functioning Analysis in Logistic Regression with Rare Events Data.
Journal
BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173
Informations de publication
Date de publication:
2020
2020
Historique:
received:
25
09
2019
revised:
19
12
2019
accepted:
20
01
2020
entrez:
19
3
2020
pubmed:
19
3
2020
medline:
5
1
2021
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 32185193
doi: 10.1155/2020/1632350
pmc: PMC7060847
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1632350Informations de copyright
Copyright © 2020 Marjan Faghih et al.
Déclaration de conflit d'intérêts
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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