Latent class analysis as a typology identification technique
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Abstract
In Psychology, it is common to find situations in which some kind of classification of people in subgroups or classes is needed. There are multivariate analysis techniques such as Hierarchical Cluster Analysis (HCA) that are commonly used for this purpose. Currently, there is a growing interest in the technique of Latent Class Analysis (LCA), although it is a relatively little known and used technique. Several authors have pointed out that the LCA has important advantages with respect to HCA, especially that the LCA allows for measures of goodness of fit. The aim of this paper is to present several applications of the LCA both from a simulation study and from real data and compare the performance of this technique against the HCA. The results from the simulation indicate that the LCA has a high performance to detect class structures. The results of the study from real data show that the different classes or mixtures present in the data may be overlapping, which makes grouping classes more difficult when applying LCA. The HCA can be a good analysis tool for the applied researcher since it can guide on the best model of LCA that should be interpreted. In research contexts in which the theoretical model is not clear, it is recommended to use both techniques in order to seek convergence of results.
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References
Chen, X., Li, X., Stanton, B., et al. (2004). Patterns of cigarette smoking among students from 19 colleges and universities in Jiangsu Province, China: a latent class analysis. Drug and Alcohol
Dependency, 76, 53–163. https://doi.org/10.1016/j.drugalcdep.2004.04.013
Furberg, H., Sullivan, P.F., Bulik, C., Maes, H., Prescott, C.A., Kendler, K.S., & Lerman, C. (2005). The types of regular cigarette smokers: A latent class analysis. Nicotine & Tobacco Research, 7(3), 351-360. https://doi.org/10.1080/14622200500124917
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. Pearson. Upper Saddle River, NJ.
Haughton, D., Legrand, P., & Woolford, S. (2009). Review of three latent class cluster analysis packages: Latent Gold, poLCA, and MCLUST. The American Statistician, 63(1), 81-91.
https://doi.org/10.1198/tast.2009.0016
Lazarsfeld, P., & Henry, N. (1968). Latent structure analysis. New York: Houghton-Mifflin.
Linzer, D.A., & Lewis, J.B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of statistical software, 42(10), 1-29.
Magidson, J., and Vermunt, J.K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 37-44.
Magidson, J., and Vermunt J.K. (2003). A nontechnical introduction to latent class models. White Paper. Statistical Innovations.
McCutcheon, A.L. (2002). Basic concepts and procedures in single and multiplegroup latent class analysis. In J.A. Hagenaars and A.L. McCutcheon (eds.), Applied Latent Class Analysis (pp. 56–87). Cambridge University Press. Cambridge.
Nylund, K.L., Asparouhov, T., & Muthén, B.O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural equation modeling: A multidisciplinary Journal, 14(4), 535-569.
https://doi.org/10.1080/10705510701575396
Poletto, L., Pezzotto, S.M., Morini, J., & Andrade, J. (1991). Prevalencia del hábito de fumar en jóvenes y sus padres: asociaciones relevantes con educación y ocupación. Revista de Saúde Pública, 25, 388-393.
R Development Core Team (2010). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.Rproject.
org.
Reyna, C., & Brussino, S. (2011). Revisión de los fundamentos del análisis de clases latentes y ejemplo de aplicación en el área de las adicciones. Trastornos Adictivos, 13(1), 11-19.
Sneath, P.H.A. & Sokal, R.R. (1973). Numerical Taxonomy. Freeman. San Francisco.
Vermunt, J.K., Magidson, J. (2002). Latent Class Cluster Analysis. In J.A. Hagenaars and A.L. McCutcheon (eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press. Cambridge.
Vermunt, J.K., Magidson, J. (2005). Latent GOLD [computer program on disk]. Version 4.0. Belmont: Statistical Innovations. USA.
von Davier, M., Naemi, B., & Roberts, R.D. (2012). Factorial versus typological models: A comparison of methods for personality data. Measurement: Interdisciplinary Research and Perspectives, 10(4), 185-208. https://doi.org/10.1080/15366367.2012.732798