A Comprehensive Regression-Based Approach for Identifying Sources of Person Misfit in Typical-Response Measures

Bibliographic Data

ID20286301
AuthorsPere J Ferrando (0000-0002-3133-5466, Rovira i Virgili University, Tarragona, Spain, corresponding author), Urbano Lorenzo‐Seva (0000-0001-5369-3099, Rovira i Virgili University, Tarragona, Spain)
LanguageEN
References cited23

This article proposes a general parametric item response theory approach for identifying sources of misfit in response patterns that have been classified as potentially inconsistent by a global person-fit index. The approach, which is based on the weighted least squared regression of the observed responses on the model-expected responses, can be used with a variety of unidimensional and multidimensional models intended for binary, graded, and continuous responses and consists of procedures for identifying (a) general deviation trends, (b) local inconsistencies, and (c) single response inconsistencies. A free program called REG-PERFIT that implements most of the proposed techniques has been developed, described, and made available for interested researchers. Finally, the functioning and usefulness of the proposed procedures is illustrated with an empirical study based on a statistics-anxiety scale

Data mining · Econometrics · Empirical research · Item response theory · Machine learning · Parametric statistics · Psychometrics · Regression · Regression analysis · Scale (ratio) · Statistics · Variety (cybernetics) · Advanced Statistical Modeling Techniques · Artificial Intelligence · Behavioral and Psychological Studies · Computer Science · Mathematics · Psychometric Methodologies and Testing