ArticleStructural equation modeling : a multidisciplinary journal2020
Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning.
Article in Structural equation modeling : a multidisciplinary journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Anti-Asian Racism: Developing a Brief, Reliable, and Valid Measure.Journal of racial and ethnic health disparities · 2026Article
- A primer on intensive longitudinal psychometrics.Behavior research methods · 2026Review
- An iterative strategy for referent variable selection in MIMIC-interaction modeling.Behavior research methods · 2026Article
- Reducing Differential Item Functioning via Process Data.Psychometrika · 2025Article
- Anchor Detection Strategy in Moderated Non-Linear Factor Analysis for Differential Item Functioning (DIF).Applied psychological measurement · 2025Article
- Empathy and Autism: Establishing the Structure and Different Manifestations of Empathy in Autistic Individuals Using the Perth Empathy Scale.Journal of autism and developmental disorders · 2025Article
- A Novel Method for Detecting Intersectional DIF: Multilevel Random Item Effects Model with Regularized Gaussian Variational Estimation.Psychometrika · 2025Article
- Adjusting for Measurement Bias in the MEEQ-B Across Sex and Race/Ethnicity in the ABCD Study.Journal of studies on alcohol and drugs · 2025Article
- Measurement of Connectedness with Nature: Evidence of Validity and Reliability for Use in Colombian Urban and Rural Sustainability Contexts.International journal of environmental research and public health · 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
- Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.International journal of psychology : Journal international de psychologie · 2025Article
- Bayesian Adaptive Lasso for Detecting Item-Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models.Psychometrika · 2024Article
- Using moderated nonlinear factor models to adjust for differential item functioning in the Student-Teacher Relationship Scale from kindergarten to Grade 6.Journal of school psychology · 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
- Examining Differential Item Functioning from a Multidimensional IRT Perspective.Psychometrika · 2024Article
- DIF Analysis with Unknown Groups and Anchor Items.Psychometrika · 2024Article
- Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle.Behavior research methods · 2023Review
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.