Researchers apply Latent Class Analysis (LCA), a multivariate technique, for cluster, factor, or regression purposes.
Often researchers use Latent Class Analysis (LCA) when they need to classify cases into a set of latent classes.
In Latent Class Analysis (LCA), researchers perform the analysis on latent classes and rely on categorical indicator variables. And they assign a value of ‘1’ to indicator variables if their condition is true, and ‘0’ otherwise.
Here 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 identifies cases where the variables of interest do not correlate with any other variables in the class.
And the model parameters in Latent class analysis (LCA) are the maximum likelihood estimates (MLE) of conditional response probabilities.
The number of latent classes in Latent Class Analysis (LCA) can be determined in two ways. 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.
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The other method is the method of bootstrapping of the latent classes in (LCA). The rho estimates refer to the item response probabilities in LCA.
The odds ratio in (LCA) measures the effective sizes of the covariates in the model. In LCA, researchers calculate the odds ratio by performing 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 (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 (LCA) refer to the probability of that observation that is classified in a given class.
(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. In certain measures assess model fit.
The latent model in Latent Class Analysis (LCA) fits the data with the help of the likelihood ratio chi-square
The larger the value of the statistic the more inefficient the model is to fit the data.
In Latent Class Analysis (LCA), the difference chi-square calculates the difference between the chi-square values of two nested models.
To assess the validity or reliability of Latent Class Analysis (LCA), researchers use a statistic called the Cressie-Read statistic. They determine the validity of LCA by comparing the probability value of the Cressie-Read statistic 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.