Bauer Daniel J, Belzak William C M, Cole Veronica
Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill.
Center for Developmental Science, The University of North Carolina at Chapel Hill.
Struct Equ Modeling. 2020;27(1):43-55. doi: 10.1080/10705511.2019.1642754. Epub 2019 Sep 5.
Determining whether measures are equally valid for all individuals is a core component of psychometric analysis. Traditionally, the evaluation of measurement invariance (MI) involves comparing independent groups defined by a single categorical covariate (e.g., men and women) to determine if there are any items that display differential item functioning (DIF). More recently, Moderated Nonlinear Factor Analysis (MNLFA) has been advanced as an approach for evaluating MI/DIF simultaneously over multiple background variables, categorical and continuous. Unfortunately, conventional procedures for detecting DIF do not scale well to the more complex MNLFA. The current manuscript therefore proposes a regularization approach to MNLFA estimation that penalizes the likelihood for DIF parameters (i.e., rewarding sparse DIF). This procedure avoids the pitfalls of sequential inference tests, is automated for end users, and is shown to perform well in both a small-scale simulation and an empirical validation study.
确定测量方法对所有个体是否同样有效是心理测量分析的核心组成部分。传统上，测量不变性（MI）的评估涉及比较由单个分类协变量定义的独立组（例如，男性和女性），以确定是否存在显示差异项目功能（DIF）的项目。最近，调节非线性因子分析（MNLFA）已被提出作为一种同时在多个分类和连续背景变量上评估MI/DIF的方法。不幸的是，检测DIF的传统程序在更复杂的MNLFA上扩展性不佳。因此，当前的手稿提出了一种用于MNLFA估计的正则化方法，该方法对DIF参数的似然性进行惩罚（即奖励稀疏DIF）。此程序避免了顺序推断检验的陷阱，为最终用户实现了自动化，并且在小规模模拟和实证验证研究中均表现良好。
