Using Robust Estimation Method to Improve Person Traits Assessment

Abstract

Measurement and evaluation play a crucial role in psychology and pedagogy, with testing serving as the primary tool for assessment. Researchers and administrators consistently seek methods to accurately assess a subject’s traits based on test results. Traditional person traits estimation methods heavily rely on the authenticity of responses and suppose all respondents honestly and normally respond to all items. However, when aberrant responses occur, biased results can arise with traditional methods, thereby diminishing the precision of person trait estimation. Robust estimation method is believed as an effectively method to mitigate the impact of aberrant responses on estimation accuracy. Nevertheless, extant robust estimation approaches, while reducing estimation bias for aberrant test-takers, also impede estimation precision for normal test-takers. To address this issue, we proposed an innovative robust estimation method that can balance the mitigation of aberrant behavior’s impact on accuracy with the assurance of precision in normal test-taker estimation. Simulation findings reveal the newly proposed method consistently maintains exceptional estimation accuracy, demonstrating precise estimates even in the absence of anomalous behavior. The empirical study further clarifies the applicability and advantages of our method within psychological and educational assessments.