Rasch Mixture Models for DIF Detection
A Comparison of Old and New Score Specifications
Bibliographic Data
| ID | 20283157 |
|---|---|
| Authors | Hannah Frick (0000-0002-6049-5258, Universität Innsbruck, Innsbruck, Austria), Carolin Strobl (0000-0003-0952-3230, Universität Zürich, Zürich, Switzerland), Achim Zeileis (0000-0003-0918-3766, Universität Innsbruck, Innsbruck, Austria) |
| Year | 2015 |
| Volume | 75 |
| Issue | 2 |
| Pages | 208-234 |
| Publication date | 2015-04-01 |
| 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 | 10.1177/0013164414536183 |
| PMID | 29795819 |
| OpenAlex | W2131579875 |
| Language | EN |
| Citations received | 4 |
| References cited | 28 |
Rasch mixture models can be a useful tool when checking the assumption of measurement invariance for a single Rasch model. They provide advantages compared to manifest differential item functioning (DIF) tests when the DIF groups are only weakly correlated with the manifest covariates available. Unlike in single Rasch models, estimation of Rasch mixture models is sensitive to the specification of the ability distribution even when the conditional maximum likelihood approach is used. It is demonstrated in a simulation study how differences in ability can influence the latent classes of a Rasch mixture model. If the aim is only DIF detection, it is not of interest to uncover such ability differences as one is only interested in a latent group structure regarding the item difficulties. To avoid any confounding effect of ability differences (or impact), a new score distribution for the Rasch mixture model is introduced here. It ensures the estimation of the Rasch mixture model to be independent of the ability distribution and thus restricts the mixture to be sensitive to latent structure in the item difficulties only. Its usefulness is demonstrated in a simulation study, and its application is illustrated in a study of verbal aggression
Confounding · Differential item functioning · Econometrics · Item response theory · Latent variable · Mixture model · Polytomous Rasch model · Psychometrics · Rasch model · Statistics · Advanced Causal Inference Techniques · Mathematics · Psychology · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
Explanatory Item Response Models
A Goodness of Fit Test for the Rasch Model
A Rasch Model for Partial Credit Scoring
Rasch Models in Latent Classes
Objective Criteria for the Evaluation of Clustering Methods
Finite Mixture Models
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Estimating the Dimension of a Model
Mixed Rasch Modeling of the Self-Rating Depression Scale
Item Discrimination and Type I Error in the Detection of Differential Item Functioning
Differential Item Functioning Detection With Latent Classes
Type I Error Inflation for Detecting DIF in the Presence of Impact
Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model
A rating formulation for ordered response categories
Second‐Degree Moral Hazard in a Real‐World Credence Goods Market
| Citations per year | 0.4 |
|---|---|
| Citation span | 2016 - 2025 (10) |
| Highly cited | No |