A latent class analysis on the usage of mobile phones among management students

Kumar Sunil () and Dabgotra Apurba Vishal
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Kumar Sunil: Department of Statistics, University of Jammu, J&K, India
Dabgotra Apurba Vishal: Department of Statistics, University of Jammu, J&K, India

Statistics in Transition New Series, 2021, vol. 22, issue 1, 89-114

Abstract: In the past few years, wireless devices, including pocket PCs, pagers, mobile phones, etc, have gained popularity among a variety of users across the world and the use of mobile phones in particular, has increased significantly in many parts of the world, especially in India. Cell phones are now the most popular form of electronic communication and constitute an integral part of adolescents’ daily lives, as is the case for the majority of mobile phone users. In fact, mobile phones have turned from a technological tool to a social tool. Therefore, the influence of cell phones on young people needs to be thoroughly examined. In this paper, we explore the attitude of young adults towards cell phones and identify the hidden classes of respondents according to the patterns of mobile phone use. The Latent Class Analysis (LCA) serves as a tool to detect any peculiarities, including those gender-based. LCA measures the value of an unknown latent variable on the basis of the respondents’ answers to various indicator variables; for this reason, a proper selection of indicators is of great importance here. In this work, we propose a method of selecting the most useful variables for an LCA-based detection of group structures from within the examined data. We apply a greedy search algorithm, where during each phase the models are compared through an approximation to their Bayes factor. The method is applied in the process of selecting variables related to mobile phone usage which are most useful for the clustering of respondents into different classes. The findings demonstrate that young people display various feelings and attitudes toward cell phone usage.

Keywords: backward greedy search algorithm (BGSA); latent class analysis (LCA); AIC; BIC. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:1:p:89-114:n:13

DOI: 10.21307/stattrans-2021-005

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