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Assessment of Differential Rater Functioning in Latent Classes with New Mixture Facets Models
1a Department of Psychology , The Education University of Hong Kong.
Multivariate Behavioral Research
|March 23, 2017
Summary
This study introduces a new mixture facets model to detect differential rater functioning (DRF) when rater or ratee group membership is unknown. Simulations show the model effectively recovers parameters, especially with more data.
Area of Science:
- Psychometrics
- Human Sciences
- Statistical Modeling
Background:
- Multifaceted data analysis often assumes no interaction between facets.
- Differential facet functioning (DFF) occurs when such interactions exist.
- Differential rater functioning (DRF) is a specific DFF type, typically studied with known group memberships.
Purpose of the Study:
- To develop a novel mixture facets model for assessing DRF with latent (unknown) group memberships.
- To demonstrate the model's application through empirical examples.
- To evaluate the model's performance using simulations.
Main Methods:
- Development of a new mixture facets model.
- Application of the model to two empirical datasets.
- Bayesian framework simulations to assess parameter recovery.
Main Results:
- The mixture facets model successfully assesses DRF with latent group membership.
- Parameter recovery was generally good across simulations.
- Increased data quantity improved parameter recovery.
Conclusions:
- The proposed mixture facets model is a viable tool for DRF assessment when group membership is latent.
- The model's performance is robust and improves with more data.
- This approach enhances the analysis of complex multifaceted data in human sciences.
Keywords:
Bayesian inferencedifferential facet functioningdifferential rater functioningitem response theorymixture modelsMore Related Videos
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