Detecting Rater Biases in Sparse Rater-Mediated Assessment Networks

• Published in: Educational and psychological measurement Vol. 81; no. 5; pp. 996 - 1022
• Main Authors: Wind, Stefanie A., Ge, Yuan
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.10.2021; SAGE PUBLICATIONS, INC
• Subjects: Data Collection; Design; Educational evaluation; Educational tests & measurements; Evaluators; Missing data; Multiple Choice Tests; Scoring; Student Evaluation; Test Bias; differential rater functioning; performance assessment; rater effects; rater bias; missing data
• ISSN: 0013-1644; 1552-3888
• DOI: 10.1177/0013164420988108

Practical constraints in rater-mediated assessments limit the availability of complete data. Instead, most scoring procedures include one or two ratings for each performance, with overlapping performances across raters or linking sets of multiple-choice items to facilitate model estimation. These incomplete scoring designs present challenges for detecting rater biases, or differential rater functioning (DRF). The purpose of this study is to illustrate and explore the sensitivity of DRF indices in realistic sparse rating designs that have been documented in the literature that include different types and levels of connectivity among raters and students. The results indicated that it is possible to detect DRF in sparse rating designs, but the sensitivity of DRF indices varies across designs. We consider the implications of our findings for practice related to monitoring raters in performance assessments.