lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations.
lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations.
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DOI：
10.18637/jss.v039.i08
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发表时间：
2011-03-01
影响因子：
5.8
通讯作者：
Crane PK
中科院分区：
文献类型：
--
作者：
Choi SW;Gibbons LE;Crane PK
逻辑回归提供了一个灵活的框架来检测各种类型的差异项目功能（DIF）。以前的努力扩展的框架，使用项目反应理论（IRT）为基础的特质分数，并通过采用迭代过程中使用组特定的项目参数占DIF的特质分数，类似于其他DIF检测框架中使用的纯化方法。目前的调查先进的技术，通过开发一个计算平台，将统计和IRT程序集成到一个单一的程序。此外，蒙特卡罗模拟方法被纳入到各种DIF统计和效应量的措施，以获得经验标准。为了说明的目的，该程序被应用于焦虑症状问卷的数据，用于检测来自患者报告的结果测量信息系统的与年龄相关的DIF。
Logistic regression provides a flexible framework for detecting various types of differential item functioning (DIF). Previous efforts extended the framework by using item response theory (IRT) based trait scores, and by employing an iterative process using group–specific item parameters to account for DIF in the trait scores, analogous to purification approaches used in other DIF detection frameworks. The current investigation advances the technique by developing a computational platform integrating both statistical and IRT procedures into a single program. Furthermore, a Monte Carlo simulation approach was incorporated to derive empirical criteria for various DIF statistics and effect size measures. For purposes of illustration, the procedure was applied to data from a questionnaire of anxiety symptoms for detecting DIF associated with age from the Patient–Reported Outcomes Measurement Information System.