Literature DB >> 33656627

Differential Item Functioning Analysis Without A Priori Information on Anchor Items: QQ Plots and Graphical Test.

Ke-Hai Yuan1, Hongyun Liu2, Yuting Han3.   

Abstract

Differential item functioning (DIF) analysis is an important step in establishing the validity of measurements. Most traditional methods for DIF analysis use an item-by-item strategy via anchor items that are assumed DIF-free. If anchor items are flawed, these methods will yield misleading results due to biased scales. In this article, based on the fact that the item's relative change of difficulty difference (RCD) does not depend on the mean ability of individual groups, a new DIF detection method (RCD-DIF) is proposed by comparing the observed differences against those with simulated data that are known DIF-free. The RCD-DIF method consists of a D-QQ (quantile quantile) plot that permits the identification of internal references points (similar to anchor items), a RCD-QQ plot that facilitates visual examination of DIF, and a RCD graphical test that synchronizes DIF analysis at the test level with that at the item level via confidence intervals on individual items. The RCD procedure visually reveals the overall pattern of DIF in the test and the size of DIF for each item and is expected to work properly even when the majority of the items possess DIF and the DIF pattern is unbalanced. Results of two simulation studies indicate that the RCD graphical test has Type I error rate comparable to those of existing methods but with greater power.
© 2021. The Psychometric Society.

Entities:  

Keywords:  RCD-QQ plot; confidence interval; differential item functioning; graphical test; relative change of difficulty difference

Year:  2021        PMID: 33656627     DOI: 10.1007/s11336-021-09746-5

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  15 in total

1.  A general framework and an R package for the detection of dichotomous differential item functioning.

Authors:  David Magis; Sébastien Béland; Francis Tuerlinckx; Paul De Boeck
Journal:  Behav Res Methods       Date:  2010-08

2.  lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations.

Authors:  Seung W Choi; Laura E Gibbons; Paul K Crane
Journal:  J Stat Softw       Date:  2011-03-01       Impact factor: 6.440

3.  Looking at DIF From a New Perspective: A Structure-Based Approach Acknowledging Inherent Indefinability.

Authors:  Anna Doebler
Journal:  Appl Psychol Meas       Date:  2018-09-11

4.  A Monte Carlo Study of an Iterative Wald Test Procedure for DIF Analysis.

Authors:  Mengyang Cao; Louis Tay; Yaowu Liu
Journal:  Educ Psychol Meas       Date:  2016-03-07       Impact factor: 2.821

5.  Anchor Selection Strategies for DIF Analysis: Review, Assessment, and New Approaches.

Authors:  Julia Kopf; Achim Zeileis; Carolin Strobl
Journal:  Educ Psychol Meas       Date:  2014-04-21       Impact factor: 2.821

6.  Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications.

Authors:  Hannah Frick; Carolin Strobl; Achim Zeileis
Journal:  Educ Psychol Meas       Date:  2014-06-22       Impact factor: 2.821

7.  A Statistical Test for Differential Item Pair Functioning.

Authors:  Timo M Bechger; Gunter Maris
Journal:  Psychometrika       Date:  2014-09-16       Impact factor: 2.500

8.  Improving the assessment of measurement invariance: Using regularization to select anchor items and identify differential item functioning.

Authors:  William C M Belzak; Daniel J Bauer
Journal:  Psychol Methods       Date:  2020-01-09

9.  Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis.

Authors:  Carl F Falk; Li Cai
Journal:  Psychometrika       Date:  2014-12-09       Impact factor: 2.500

10.  A regularization approach for the detection of differential item functioning in generalized partial credit models.

Authors:  Gunther Schauberger; Patrick Mair
Journal:  Behav Res Methods       Date:  2020-02
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