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An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses.

ABSTRACT: Conventional item response theory (IRT) modeling of personality responses considers two item characteristics-location and discrimination-but only one person characteristic-location or trait level. An IRT modeling approach that jointly considers item and person discriminations, however, is thought to be more realistic and appropriate in this domain and has several potential advantages. This article develops a model of this type for unidimensional binary responses together with procedures for estimating item and person parameters and assessing model appropriateness (including person fit). A series of preliminary simulations suggests that the approach is feasible, and a real-data example illustrates the potential advantages with respect to the standard two-parameter model. Limitations of the proposal and further work are also discussed.

SUBMITTER: Ferrando PJ

PROVIDER: S-EPMC5978483 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses.

Ferrando Pere J PJ

Applied psychological measurement 20160104 3

Conventional item response theory (IRT) modeling of personality responses considers two item characteristics-location and discrimination-but only one person characteristic-location or trait level. An IRT modeling approach that jointly considers item and person discriminations, however, is thought to be more realistic and appropriate in this domain and has several potential advantages. This article develops a model of this type for unidimensional binary responses together with procedures for esti ...[more]

PMID: 29881049

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Project description:The analysis of response time has received increasing attention during the last decades, since evidence from several studies supported the argument that there is a direct relationship between item response time and test performance. The aim of this study was to investigate whether item response latency affects person's ability parameters, in that it represents an adaptive or maladaptive practice. To examine the above research question data from 8,475 individuals completing the computerized version of the Postgraduate General Aptitude Test (PAGAT) were analyzed. To determine the extent to which response latency affects person's ability, we used a Multiple Indicators Multiple Causes (MIMIC) model, in which every item in a scale was linked to its corresponding covariate (i.e., item response latency). We ran the MIMIC model within the Item Response Theory (IRT) framework (2-PL model). The results supported the hypothesis that item response latency could provide valuable information for getting more accurate estimations for persons' ability levels. Results indicated that for individuals who invest more time on easy items, their likelihood of success does not improve, most likely because slow and fast responders have significantly different levels of ability (fast responders are of higher ability compared to slow responders). Consequently, investing more time for low ability individuals does not prove to be adaptive. The opposite was found for difficult items: individuals spending more time on difficult items increase their likelihood of success, more likely because they are high achievers (in difficult items individuals who spent more time were of significantly higher ability compared to fast responders). Thus, it appears that there is an interaction between the difficulty of the item and person abilities that explain the effects of response time on likelihood of success. We concluded that accommodating item response latency in a computerized assessment model, can inform test quality and test takers' behavior, and in that way, enhance score measurement accuracy.

Dataset Information

An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses.

Publications

An IRT Modeling Approach for Assessing Item and Person Discrimination in Binary Personality Responses.

Similar Datasets

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