{"title":"Robust estimation of the latent trait in graded response models.","authors":"Audrey Filonczuk, Ying Cheng","doi":"10.3758/s13428-024-02574-2","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 1","pages":"55"},"PeriodicalIF":4.6000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-024-02574-2","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
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,粗心的反应,错误的项目等)经常污染心理评估和调查。以前对二分类IRT模型的鲁棒估计已经在包含响应干扰的数据中产生了更准确的潜在性状估计。然而,对于具有三个或更多反应类别的广泛使用的李克特型项目,没有一个可靠的估计器来估计潜在特征。我们提出了一个可应用于李克特类型项目的分级响应模型(GRM)的鲁棒估计器。考虑了两种加权机制来降低“可疑”响应的权重:Huber和bissquared权重函数。仿真表明,该估计器减少了各种测试长度、响应类别数量和响应干扰类型的偏差。偏差的减少和稳定的标准误差表明,GRM的稳健估计器可以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。然后将稳健估计器应用于Big Five Inventory-2中的数据(Ober等人,2021年)以演示其使用。讨论了潜在的应用和影响。
期刊介绍:
Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.