How does LCA compare to other statistical methods?
In purpose, LCA is closely analogous to cluster analysis: it is used to discover groups or types of case based on observed data, and, possibly, to also assign case to groups. There is a very strong connection between LCA and a particular kind of cluster analysis called multivariate mixture estimation (Day, 1969; Titterington, Smith & Makov, 1985; Wolfe, 1970). With multivariate mixture estimation, observed data for multiple continuous variables are assumed to derive from a mixture of two or more underlying multivariate-normal distributions. The continuous data version of LCA, latent profile analysis, is a restricted version of multivariate mixture estimation–the constraints are that measures are assumed uncorrelated within each distribution. LCA is often called a categorical-data analogue to factor analysis. The precise rationale for this comparison is unclear. Factor analysis is concerned with the structure of variables (i.e.
In purpose, LCA is closely analogous to cluster analysis: it is used to discover groups or types of case based on observed data, and, possibly, to also assign case to groups. There is a very strong connection between LCA and a particular kind of cluster analysis called multivariate mixture estimation (Day, 1969; Titterington, Smith & Makov, 1985; Wolfe, 1970). With multivariate mixture estimation, observed data for multiple continuous variables are assumed to derive from a mixture of two or more underlying multivariate-normal distributions. The continuous data version of LCA, latent profile analysis, is a restricted version of multivariate mixture estimation–the constraints are that measures are assumed uncorrelated within each distribution. LCA is often called a categorical-data analogue to factor analysis. The precise rationale for this comparison is unclear. Factor analysis is concerned with the structure of variables (i.e., their correlations), whereas LCA is more concerned with t
