ArticlePsychological methods2020
Improving the assessment of measurement invariance: Using regularization to select anchor items and identify differential item functioning.
Article in Psychological methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Psychometric properties of the Chinese version of PROMIS-29 in patients receiving kidney replacement therapy.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- An iterative strategy for referent variable selection in MIMIC-interaction modeling.Behavior research methods · 2026Article
- Anchor Detection Strategy in Moderated Non-Linear Factor Analysis for Differential Item Functioning (DIF).Applied psychological measurement · 2025Article
- Score-based tests for parameter instability in ordinal factor models.The British journal of mathematical and statistical psychology · 2025Article
- A Novel Method for Detecting Intersectional DIF: Multilevel Random Item Effects Model with Regularized Gaussian Variational Estimation.Psychometrika · 2025Article
- Detecting Differential Item Functioning across Multiple Groups using Group Pairwise Penalty.Psychometrika · 2025Article
- Assessing Whether Negative Parenting Cognitions Bias Parent Report of Preschoolers' Externalizing Symptoms: A Regularized Moderated Non-Linear Factor Analysis Approach.Research on child and adolescent psychopathology · 2025Article
- Bayesian Adaptive Lasso for Detecting Item-Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models.Psychometrika · 2024Article
- Psychometric evaluation of the study interest questionnaire-short form among Chinese nursing students based on classical test theory and item response theory.BMC nursing · 2024Article
- Using Interpretable Machine Learning for Differential Item Functioning Detection in Psychometric Tests.Applied psychological measurement · 2024Article
- Detecting uniform differential item functioning for continuous response computerized adaptive testing.Applied psychological measurement · 2024Article
- DIF Analysis with Unknown Groups and Anchor Items.Psychometrika · 2024Article
- DIF Statistical Inference Without Knowing Anchoring Items.Psychometrika · 2023Article
- Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Psychometric Properties, Factor Structure, and Validity of the Sensitivity to Threat and Affiliative Reward Scale in Children and Adults.Assessment · 2023Article
- A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD.Child psychiatry and human development · 2022Article
- Comprehensive measurement invariance of alcohol outcome expectancies among adolescents using regularized moderated nonlinear factor analysis.Addictive behaviors · 2022Article
- Evaluations of the sum-score-based and item response theory-based tests of group mean differences under various simulation conditions.Statistical methods in medical research · 2021Article
- Differential Item Functioning Analyses of the Patient-Reported Outcomes Measurement Information System (PROMIS®) Measures: Methods, Challenges, Advances, and Future Directions.Psychometrika · 2021Article
- Differential Item Functioning Analysis Without A Priori Information on Anchor Items: QQ Plots and Graphical Test.Psychometrika · 2021Article
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Abstract
A common challenge in the behavioral sciences is evaluating measurement invariance, or whether the measurement properties of a scale are consistent for individuals from different groups. Measurement invariance fails when differential item functioning (DIF) exists, that is, when item responses relate to the latent variable differently across groups. To identify DIF in a scale, many data-driven procedures iteratively test for DIF one item at a time while assuming other items have no DIF. The DIF-free items are used to anchor the scale of the latent variable across groups, identifying the model. A major drawback to these iterative testing procedures is that they can fail to select the correct anchor items and identify true DIF, particularly when DIF is present in many items. We propose an alternative method for selecting anchors and identifying DIF. Namely, we use regularization, a machine learning technique that imposes a penalty function during estimation to remove parameters that have little impact on the fit of the model. We focus specifically here on a lasso penalty for group differences in the item parameters within the two-parameter logistic item response theory model. We compare lasso regularization with the more commonly used likelihood ratio test method in a 2-group DIF analysis. Simulation and empirical results show that when large amounts of DIF are present and sample sizes are large, lasso regularization has far better control of Type I error than the likelihood ratio test method with little decrement in power. This provides strong evidence that lasso regularization is a promising alternative for testing DIF and selecting anchors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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