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Empirically indistinguishable multidimensional IRT and locally dependent unidimensional item response models
Author(s) -
Ip Edward Haksing
Publication year - 2010
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711009x466835
Subject(s) - item response theory , dimension (graph theory) , robustness (evolution) , construct (python library) , multidimensional data , econometrics , multidimensional analysis , contrast (vision) , computer science , cognitive psychology , psychology , mathematics , psychometrics , artificial intelligence , statistics , data mining , biochemistry , chemistry , pure mathematics , gene , programming language
Multidimensionality is a core concept in the measurement and analysis of psychological data. In personality assessment, for example, constructs are mostly theoretically defined as unidimensional, yet responses collected from the real world are almost always determined by multiple factors. Significant research efforts have concentrated on the use of simulated studies to evaluate the robustness of unidimensional item response models when applied to multidimensional data with a dominant dimension. In contrast, in the present paper, I report the result from a theoretical investigation that a multidimensional item response model is empirically indistinguishable from a locally dependent unidimensional model, of which the single dimension represents the actual construct of interest. A practical implication of this result is that multidimensional response data do not automatically require the use of multidimensional models. Circumstances under which the alternative approach of locally dependent unidimensional models may be useful are discussed.