A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning
Bibliographic Data
| ID | 20283183 |
|---|---|
| Authors | David Magis (University of Liège, Liège, Belgium), Paul De Boeck (0000-0002-0884-2582, K. U. Leuven, Leuven, Belgium) |
| Year | 2012 |
| Volume | 72 |
| Issue | 2 |
| Pages | 291-311 |
| Publication date | 2012-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164411416975 |
| OpenAlex | W2155647249 |
| Language | EN |
| Citations received | 4 |
| References cited | 41 |
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel–Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source of potential Type I error inflation, not only in the presence of DIF but also when DIF is absent and in the presence of impact. The purpose of this article is to present an alternative statistical inference approach based on the same measure of DIF but such that the Type I error inflation is prevented. The key notion is that for DIF items, the measure has an outlying value that can be identified as such with inference tools from robust statistics. Although we use the MH log odds ratio as a statistic, the inference is different. A simulation study is performed to compare the robust statistical inference with the classical inference method, both based on the MH statistic. As expected, the Type I error rate inflation is avoided with the robust approach, although the power of the two methods is similar
Bayesian inference · Bayesian probability · Data mining · Differential item functioning · Econometrics · Frequentist inference · Inference · Item response theory · Measure (data warehouse) · Outlier · Psychometrics · Statistic · Statistical hypothesis testing · Statistical inference · Statistics · Type I and type II errors · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics · Optimal Experimental Design Methods · Psychometric Methodologies and Testing
Item Purification Does Not Always Improve DIF Detection
Type I Error Inflation in DIF Identification With Mantel–Haenszel
Exploring the Evidence to Interpret Differential Item Functioning via Response Process Data
A Regression Discontinuity Design Framework for Controlling Selection Bias in Evaluations of Differential Item Functioning
Robust Regression and Outlier Detection
Detecting Differential Item Functioning Using Logistic Regression Procedures
A Model-Based Standardization Approach that Separates True Bias/DIF from Group Ability Differences and Detects Test Bias/DTF as well as Item Bias/DIF
Identification of Nonuniform Differential Item Functioning Using a Variation of the Mantel-Haenszel Procedure
Type I Error Inflation for Detecting DIF in the Presence of Impact
On The Robustness Of Factor Analysis Against Crude Classification Of The Observations
Asymptotic Robust Inferences in the Analysis of Mean and Covariance Structures
Applying the Breslow-Day Test of Trend in Odds Ratio Heterogeneity to the Analysis of Nonuniform DIF
Structural Equation Modeling with Robust Covariances
| Citations per year | 0.29 |
|---|---|
| Citation span | 2012 - 2025 (14) |
| Highly cited | No |