Logistic Regression Procedure Using Penalized Maximum Likelihood Estimation for Differential Item Functioning
- 주제(기타) Psychology, Educational
- 주제(기타) Psychology, Applied
- 주제(기타) Psychology, Mathematical
- 설명문(일반) [Lee, Sunbok] Univ Houston, Dept Psychol, 3695 Cullen Blvd, Houston, TX 77204 USA
- 등재 SSCI, SCOPUS
- 발행기관 WILEY
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182462
- 본문언어 영어
- Published As http://dx.doi.org/10.1111/jedm.12253
초록/요약
In the logistic regression (LR) procedure for differential item functioning (DIF), the parameters of LR have often been estimated using maximum likelihood (ML) estimation. However, ML estimation suffers from the finite-sample bias. Furthermore, ML estimation for LR can be substantially biased in the presence of rare event data. The bias of ML estimation due to small samples and rare event data can degrade the performance of the LR procedure, especially when testing the DIF of difficult items in small samples. Penalized ML (PML) estimation was originally developed to reduce the finite-sample bias of conventional ML estimation and also was known to reduce the bias in the estimation of LR for the rare events data. The goal of this study is to compare the performances of the LR procedures based on the ML and PML estimation in terms of the statistical power and Type I error. In a simulation study, Swaminathan and Rogers's Wald test based on PML estimation (PSR) showed the highest statistical power in most of the simulation conditions, and LRT based on conventional PML estimation (PLRT) showed the most robust and stable Type I error. The discussion about the trade-off between bias and variance is presented in the discussion section.
more