Latent class analysis as a typology identification technique

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.