Latent class analysis of multigroup heterogeneity in propensity for academic dishonesty

Sunil Kumar, Apurba Dabgotra and Diganta Mukherjee

The Journal of Mathematical Sociology, 2024, vol. 48, issue 1, 81-99

Abstract: Latent class analysis (LCA) is a cross-sectional latent variable mixture modeling (LVMM) approach. Like all LVMM approaches, LCA aims to find heterogeneity within the population by identifying homogenous subgroups of individuals, with each subgroup (called latent class) possessing a unique set of characteristics that differentiate it from other subgroups. LCA can be carried out with categorical latent and indicator variables. But, LCA is unable to examine the association between respective items and the latent variable among categories of individuals. Multiple-group LCA, in particular, is a useful extension of LCA which enables the testing of homogeneity of the class patterns between groups of the individual through a series of constraints. In this paper, we have performed a multi-group latent class analysis for measuring self reported academic dishonesty among the students of University of Jammu. From the analysis, three general behaviors of academic cheaters are identified as rare, frequent, and instant cheaters. Further, from the multi-group LCA, it is envisaged that female students of University of Jammu are more instantaneous cheaters than male students. Students who are self-reported cheaters from sciences and humanities of the University of Jammu are persistent in cheating whereas from professional courses they are more occasional.

Date: 2024
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DOI: 10.1080/0022250X.2023.2179999

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