Latent Class Analysis (LCA) is a multivariate that allows to categorize individuals into homogenous groups. It is useful to identify individuals who have a series of characteristics in common from a complex set of data. It is also referred to as “mixture modeling based clustering” or “finite mixture modeling” where latent variables are categorical (Mindrila, 2020). The latent categories represent a set of sub-population of individuals and membership to these sub-populations are determined based on patterns of variations in data. An appropriate latent class model might have several latent variables as explanatory variables and these latent variables might be useful to explain more comprehensively the observed relationships among the set of observed variables under consideration.