Assessment of Differential Rater Functioning in Latent Classes with New Mixture Facets Models

ABSTRACT

Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity. However, DRF may occur when the group membership is unknown (latent) and thus has to be estimated from data. To solve this problem, in this study, we developed a new mixture facets model to assess DRF when the group membership is latent and we provided two empirical examples to demonstrate its applications. A series of simulations were also conducted to evaluate the performance of the new model in the DRF assessment in the Bayesian framework. Results supported the use of the mixture facets model because all parameters were recovered fairly well, and the more data there were, the better the parameter recovery.

KEYWORDS:

• Bayesian inference
• differential facet functioning
• differential rater functioning
• item response theory
• mixture models

Article information

Conflict of Interest Disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical Principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was supported by Grant 845111 from the General Research Fund, Hong Kong.

Role of the Funders/Sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The authors would like to thank Dr. Zita Oravecz and anonymous reviewers for their comments on prior versions of this manuscript. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors' institution or the General Research Fund is not intended and should not be inferred.