Latent class analysis is a statistical tool for classifying objects or individuals according to their values on a set of observed, i.e. manifest, variables. Like cluster analysis, it is aimed at identifying clusters of individuals or objects that are in some sense ‘similar’. In order to separate to the terminology of cluster analysis, the groups of individuals are called ‘classes’ or ‘latent classes’ in latent class analysis (LCA) instead of clusters.

Unlike cluster analysis, the grouping is not done by means of some measure of similarity or distance between each pair of objects to be classified. There is also no need to define some criterion of cluster distance (or similarity), nor to select one of the various cluster algorithms (e.g. agglomerative, centroid method, etc.). In ...

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