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

Sunil Kumar; Apurba Vishal Dabgotra

doi:10.21307/stattrans-2021-005

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

Sunil Kumar Department of Statistics, University of Jammu, J&K, India ORCID:http://orchid.org/0000-0003-0249-8415 , Apurba Vishal Dabgotra Department of Statistics, University of Jammu, J&K, India ORCID:https://orcid.org/0000-0002-8056-7239 Statistics in Transition new series, vol. 22, 2021, 1, pages: 89–114 Published online: 3 March 2021 DOI 10.21307/stattrans-2021-005

951 Views 45 Downloads

ARTICLE

(English) PDF

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

REFERENCES

AKAIKE, H., (1973). Information Theory and an Extension of the Maximum Likelihood Principle. 2(nd) International Symposium on Information Theory, pp. 267–281.

BARTHOLOMEW, D., KNOTT, M. and MOUSTAKI, I., (2011). Latent Variable Models and Factor Analysis. Wiley.

BAUMGARTNER, H. and JAN-BENEDICT, E. M. STEENKAMP, (2006). Response Biases in Marketing Research. Handbook of Marketing Research, Thousand Oaks, CA: Sage, pp. 95–109.

BIEMER, P., (2010). Latent Class Analysis of survey error. A John Willey and Sons, Inc. publications.

BIEMER, P., WIESEN, C., (2002). Latent class analysis of embedded repeated measurements: An application to the National Household Survey on Drug Abuse. Journal of the Royal Statistical Society, Series A, 165(1), pp. 97–119.

BODUSZEK, D., O’SHEA, C., DHINGRA, K. and HYLAND, P., (2014). Latent Class Analysis of Criminal Social Identity in a Prison Sample. Polish Psychological Bulletin, 45(2), pp. 192–199.

DAYTON, C., MACREADY, G. , (1988). Concomitant-Variable Latent-Class Models. Journal of the American Statistical Association, 83(401), pp. 173–178.

DEAN, N., RAFTERY, A. E., (2010). Latent class analysis variable selection. Annals of the Institute of Statistical Mathematics, 62, pp. 11–35.

DEMPSTER, A. P., LAIRD, N. M. and RUBIN, D. B., (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39(1), pp. 1–38.

FOP, M., SMART, K. M. and MURPHY, T. B., (2017). Variable selection for latent class analysis with application to low back pain diagnosis. Annals of Applied Statistics, 11(4), pp. 2085–2115.

FOP, M., MURPHY, T. B., (2017). LCAvarsel: Variable selection for latent class analysis R package version, https://cran.r-project.org/package=LCAvarsel.

FORMANN, A. K., (1984). Constrained latent class models: theory and applications. British Journal Mathematical and Statistical Psychology, 38, pp. 87–111.

FORMANN, A. K., (1992). Linear logistic latent class analysis for polytomous data. Journal of American Statistical Association, 87, pp. 476–486.

GOODMAN, L. A., (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, pp. 215–231.

HABERMAN, S. J., (1979). Analysis of Qualitative Data. New York: Academic Press 1979; Vol. 2: New Developments.

HAGENAARS, J. A. and MCCUTCHEON, A. L., (2002). Applied Latent Class Analysis. Cambridge University Press, New York.

HUI, S. L., WALTER, S. D., ( 1980 ). Estimating the error rates of diagnostic tests. Biometrics, 36, pp. 167–171.

KASS, R. E., RAFTERY, A. E., (1995). Bayes factors. Journal of the American Statistical Association, 90, pp. 773–795.

KUMAR, S., (2015). Diagnose response bias and heterogeneity: A Latent class approach on Indian household inflation expectation survey. International Journal of Advances in Social Sciences, 3(4), pp. 152–158.

KUMAR, S., (2016). Latent class analysis for reliable measure of inflation expectation in the Indian public. European Journal of Economic and Statistics, 1(1), pp. 9–16.

KUMAR, S., HUSAIN, Z., and MUKHERJEE, D., (2017). Assessing consistency of consumer confidence data using latent class analysis with time factor. Economic Analysis & Policy, 55, pp. 35–46.

LANZA, S. T., RHOADES, B. L., (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention science : The Official Journal of the Society for Prevention Research, 14(2), pp. 157–168.

LAZARSFELD, P. F., (1950). The logical and mathematical foundation of latent structure analysis and the interpretation and mathematical foundation of latent structure analysis. S.A. Stou er et al. (eds.), Measurement and Prediction, pp. 362–472. Princeton, NJ: Princeton University Press.

LINZER, D. A., LEWIS, J., (2011). poLCA: Polytomous Variable Latent Class Analysis. Annals of Applied Statistics, 11(4), pp. 2085–2115, http://CRAN.R-project.org/package=poLCA.

MCLACHLAN, G. and PEEL, D., (2000). Finite Mixture Models. John Wiley & Sons, New York.

MCLACHLAN, G., KRISHNAN, T., (2008). The EM Algorithm and Extensions. Wiley.

MOOIJAART, A. B., (1992). The EM algorithm for latent class analysis with equality constraints. Psychometrika, 57(2), pp. 261–269.

PETERSEN, K. J., QUALTER, P. and HUMPHREY, N., (2019). The Application of Latent Class Analysis for Investigating Population Child Mental Health: A Systematic Review. Frontiers in Psychology, Vol.10.

PORCU, M., GIAMBONA, F., (2017). Introduction to Latent Class Analysis with Applications. The Journal of Early Adolescence, 37(1), pp. 129–158.

RAFTERY, A. E., DEAN, N., (2006). Variable selection for model-based clustering. The Journal of American Statistical Association, 101, pp. 168–178.

SAPOUNIDIS, T., STAMOVLASIS, D. and DEMETRIADIS, S., (2019). Latent Class Modeling of Children Preference Profiles on Tangible and Graphical Robot Programming. IEEE Transactions on Education, 62(2), pp. 127–133.

TRAI, (2018). https://www.medianama.com/2018/03/223-india-had-1-012-billionactive- mobile-connections-in-january-2018-trai/.

VERMUNT, K., JEROEN, K., (2010). Latent class modelling with covariates: Two improved three-step approaches. Political Analysis, 18, pp. 450–469.