Assessment of differential rater functioning in latent classes with new mixture facets models

Kuan Yu JIN, Wen Chung WANG

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

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. Copyright © 2017 Taylor & Francis Group, LLC.
Original languageEnglish
Pages (from-to)391-402
JournalMultivariate Behavioral Research
Volume52
Issue number3
Early online dateMar 2017
DOIs
Publication statusPublished - 2017

Citation

Jin, K.-Y., & Wang, W.-C. (2017). Assessment of differential rater functioning in latent classes with new mixture facets models. Multivariate Behavioral Research, 52(3), 391-402.

Keywords

  • Bayesian inference
  • Differential facet functioning
  • Differential rater functioning
  • Item response theory
  • Mixture models

Fingerprint Dive into the research topics of 'Assessment of differential rater functioning in latent classes with new mixture facets models'. Together they form a unique fingerprint.