How does LCA work?
LCA supposes a simple parametric model and uses observed data to estimate parameter values for the model. The model parameters are: (1) the prevalence of each of C case subpopulations or latent classes (they are called ‘latent’ because a case’s class membership is not directly observed); and (2) conditional response probabilities–i.e., the probabilities, for each combination of latent class, item or variable (the items or variables are termed the manifest variables), and response level for the item or variable, that a randomly selected member of that class will make that response to that item/variable. A conditional response probability parameter, then, might be the probability that a member of Latent Class 1 answers ‘yes’ to the Question 1. Consider a simple medical example with five symptoms (coded ‘present’ or ‘absent’) and two latent classes (‘disease present’ and ‘disease absent’).
LCA supposes a simple parametric model and uses observed data to estimate parameter values for the model. The model parameters are: (1) the prevalence of each of C case subpopulations or latent classes (they are called ‘latent’ because a case’s class membership is not directly observed); and (2) conditional response probabilities–i.e., the probabilities, for each combination of latent class, item or variable (the items or variables are termed the manifest variables), and response level for the item or variable, that a randomly selected member of that class will make that response to that item/variable. A conditional response probability parameter, then, might be the probability that a member of Latent Class 1 answers ‘yes’ to the Question 1. Consider a simple medical example with five symptoms (coded ‘present’ or ‘absent’) and two latent classes (‘disease present’ and ‘disease absent’). The model parameters are: (1) the prevalence of cases in the ‘disease present’ and ‘disease absent’
