Latent Class Analysis (LCA)

Quantitative Results
Statistical Analysis

Latent class analysis (LCA) is a multivariate technique that can be applied for cluster, factor, or regression purposes.

Latent class analysis (LCA) is commonly used by the researcher in cases where it is required to perform classification of cases into a set of latent classes. It is carried out on latent classes and is based on categorical types of indicator variables. In LCA, indicator variables are those variables that are assigned as ‘1’ if their condition is true, and are otherwise assigned as ‘0.’

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Latent class analysis (LCA) uses a variant called Latent profile analysis for continuous variables. Mixture modeling with the structural equation models is a major type of LCA.

Latent class analysis (LCA) divides the cases into latent classes that are conditionally independent. In other words, it divides those cases in which the variables of interest are not correlated within any other variables in the class.

The model parameters in Latent class analysis (LCA) are the maximum likelihood estimates (MLE) of conditional response probabilities.

There are two ways by which the number of the latent classes in the Latent class analysis (LCA) is determined. The first and more popular method is to perform an iterative test of goodness of fit models with the latent classes in LCA using the likelihood ratio chi square test.

The other method is the method of bootstrapping of the latent classes in Latent class analysis (LCA). The rho estimates refer to the item response probabilities in LCA.

The odds ratio in Latent class analysis (LCA) measures the effective sizes of the covariates in the model. The odds ratio in LCA is calculated by carrying out multinomial regression. The dependent variable in this regression in LCA is the latent class variable, and the independent variable is the covariate.

If the value of the odds ratio in Latent class analysis (LCA) is 1.5 for class 1, then it means that a unit increase in the covariate corresponds to a 50 % greater likelihood.

The posterior probabilities in Latent class analysis (LCA) refer to the probability of that observation that is classified in a given class.

Latent class analysis (LCA) is done using software called Latent Gold. This software implements Latent class models for cluster analysis, factor analysis, etc. The latent models support nominal, ordinal as well as continuous data. There are certain measures of model fit.

The latent model in Latent class analysis (LCA) can be fitted to the data with the help of likelihood ratio chi square. The larger the value of the statistic the more inefficient the model is to fit the data.

The difference chi square in Latent class analysis (LCA) is used to calculate the difference of the two model chi squares for the two nested models.

In order to assess the validity or the reliability of Latent class analysis (LCA) a statistic called Cressie-Read statistic is used. The validity of LCA can be assessed with the help of the probability value being compared with the probability value of the model chi square.

It is assumed that Latent class analysis (LCA) does not follow linearity within the data.

LCA does not follow the normal distribution of the data.

LCA does not follow the homogeneity of variances.