Year: 2025
Type: article
Abstract: Aberrant behaviors among test‐takers in large‐scale assessments are often more prevalent within specific groups or testing sites. While various techniques have been developed to detect individual‐level test‐takers' aberrant behaviors, research in detecting those behaviors at the group level is rare. We propose a group fit statistic by extending the statistic to a multilevel item response model. This new statistic demonstrates adequate power and effectively controls the Type I error rate, particularly when true latent variable values are used or when group sizes are large, such as 500. When latent variable estimates are employed, an adjustment to the based on the posterior predictive checking approach can offer improved control over the Type I error rate.
DOI: 10.1111/jedm.70024
Authors: Yishan Ding, Ji Seung Yang, Youngjin Han
Institution: University of Maryland, College Park
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,040
Funder: University of Maryland
OpenAlex ID: W4417310146