A Mixture Model for Random Responding Behavior in Forced-Choice Noncognitive Assessment: Implication and Application in Organizational Research

Author:

Peng Siwei1ORCID,Man Kaiwen2,Veldkamp Bernard P.3,Cai Yan1ORCID,Tu Dongbo1

Affiliation:

1. School of Psychology, Jiangxi Normal University, Nanchang, China

2. Department of Educational Studies in Psychology, Research Methodology, and Counseling, University of Alabama, Tuscaloosa, AL, USA

3. Faculty of Behavioral Management and Social Sciences, University of Twente, Enschede, The Netherlands

Abstract

For various reasons, respondents to forced-choice assessments (typically used for noncognitive psychological constructs) may respond randomly to individual items due to indecision or globally due to disengagement. Thus, random responding is a complex source of measurement bias and threatens the reliability of forced-choice assessments, which are essential in high-stakes organizational testing scenarios, such as hiring decisions. The traditional measurement models rely heavily on nonrandom, construct-relevant responses to yield accurate parameter estimates. When survey data contain many random responses, fitting traditional models may deliver biased results, which could attenuate measurement reliability. This study presents a new forced-choice measure-based mixture item response theory model (called M-TCIR) for simultaneously modeling normal and random responses (distinguishing completely and incompletely random). The feasibility of the M-TCIR was investigated via two Monte Carlo simulation studies. In addition, one empirical dataset was analyzed to illustrate the applicability of the M-TCIR in practice. The results revealed that most model parameters were adequately recovered, and the M-TCIR was a viable alternative to model both aberrant and normal responses with high efficiency.

Funder

National Natural Science Foundation of China

Graduate Student Innovation Fund of Jiangxi Provincial Department of Education

Publisher

SAGE Publications

Subject

Management of Technology and Innovation,Strategy and Management,General Decision Sciences

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