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Overview
Latent profile analysis is a variant on LCA for continuous variables. Log-linear modeling with latent variables is a form of analysis combining latent class analysis with log-linear analysis. Latent class analysis software such as Latent Gold implement latent class models for cluster analysis, factor analysis, and regression with latent classes. Unlike traditional models, such latent class models support nominal and ordinal as well as continuous data. Mixture modeling with structural equation models is a major type of latent class analysis and is discussed separately.
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An odds ratio of, say, 1.5 for class 1 means that a one unit increase in the covariate corresponds to a 50% greater likelihood (that is, multiplies the odds by a factor of 1.5) of a case being in class 1 compared to being in the reference class. Odds ratios, as in other applications, are thus effect size measures where 1.0 indicates no effect (independence) and the greater above 1.0, the greater the effect size associated with that covariate.
For factor models the Factors tab of the Analysis dialog box allows users to change the number of factors and/or factor levels, restrict factor effects to zero on selected variables, and to restrict selected factor correlations to zero. The Correlations box allows correlations between selected pairs of factors to be included in the factor model. The Technical tab of the Analysis dialog box contains options to control the number of iterations and convergence limits, start values, Bayes constants, treatment of missing data, and error variances. In cluster and factor models one may include direct effects in the model in the Output tab. A direct effect between two indicators and direct effects of selected covariates on selected indicators can be selected for inclusion in a model using the Residuals tab on the Analysis dialog box.
