Filonczuk Audrey, Cheng Ying
Department of Psychology, University of Notre Dame, 390 Corbett Hall, Notre Dame, IN, 46556, USA.
Behav Res Methods. 2025 Jan 14;57(1):55. doi: 10.3758/s13428-024-02574-2.
Aberrant responses (e.g., careless responses, miskeyed items, etc.) often contaminate psychological assessments and surveys. Previous robust estimators for dichotomous IRT models have produced more accurate latent trait estimates with data containing response disturbances. However, for widely used Likert-type items with three or more response categories, a robust estimator for estimating latent traits does not exist. We propose a robust estimator for the graded response model (GRM) that can be applied to Likert-type items. Two weighting mechanisms for downweighting "suspicious" responses are considered: the Huber and the bisquare weight functions. Simulations reveal the estimator reduces bias for various test lengths, numbers of response categories, and types of response disturbances. The reduction in bias and stable standard errors suggests that the robust estimator for the GRM is effective in counteracting the harmful effects of response disturbances and providing more accurate scores on psychological assessments. The robust estimator is then applied to data from the Big Five Inventory-2 (Ober et al., 2021) to demonstrate its use. Potential applications and implications are discussed.
异常反应（例如，粗心的回答、键入错误的项目等）经常会干扰心理评估和调查。先前用于二分IRT模型的稳健估计器在处理包含反应干扰的数据时，能够产生更准确的潜在特质估计值。然而，对于广泛使用的具有三个或更多反应类别的李克特式项目，不存在用于估计潜在特质的稳健估计器。我们提出了一种可应用于李克特式项目的等级反应模型（GRM）的稳健估计器。考虑了两种对“可疑”反应进行加权的机制：休伯权重函数和双平方权重函数。模拟结果表明，该估计器在不同的测试长度、反应类别数量和反应干扰类型下都能减少偏差。偏差的减少和稳定的标准误差表明，GRM的稳健估计器能够有效地抵消反应干扰的有害影响，并在心理评估中提供更准确的分数。然后将该稳健估计器应用于大五人格量表-2（Ober等人，2021）的数据，以展示其用途。讨论了其潜在的应用和意义。
