# Lca and binary outcome variables

Latent class analysis LCA is a subset of structural equation modelingused to find groups or subtypes of cases in multivariate categorical data. LCA may be used in many fields, such as: LCA may be used in many fields, such as:

By introducing the latent variable, independence is restored in the sense that within classes variables are independent local independence. In statisticsa latent class model LCM relates a set of observed usually discrete multivariate variables to a set of latent variables. The data in this case consists of a N-way contingency table with answers to the items for a number of respondents. A class is characterized by a pattern of conditional probabilities that lca and binary outcome variables the chance that variables take on certain values.

As a practical instance, the variables could be multiple choice items of a political questionnaire. Given group membership, the conditional probabilities specify the chance certain answers are chosen. Within each latent class, the observed variables are statistically independent. Lca and binary outcome variables this example, the latent variable refers to political opinion and the latent classes to political groups. It is a type of latent variable model.

As in factor analysis, the LCA can also be used to classify case according to their maximum likelihood class membership. Multivariate mixture estimation MME is applicable to continuous data, and assumes that such data arise from a mixture of distributions: Because the criterion for solving the LCA is to achieve latent classes within which there is no longer any association of one symptom with another because the class is the disease which causes their associationand the set of diseases a patient has or class a case is a member of causes the symptom association, the symptoms will be "conditionally independent", i. Views Read Edit View history.

It is called lca and binary outcome variables latent class model because the latent variable is discrete. This page was last edited on 17 Decemberat Given group membership, the conditional probabilities specify the chance certain answers are chosen. Retrieved from " https: Confronted with a situation as follows, a researcher might choose to use LCA to understand the data:

This two-way model is related to probabilistic latent semantic analysis and non-negative matrix factorization. Usually the observed variables are statistically dependent. The data in this case consists of a N-way contingency table with answers to the items for a number of respondents.

Because the criterion for solving the LCA is to achieve latent classes within which there is no longer any association of one symptom with another because the class is the disease which causes their associationand the set of diseases a patient has or class a case is a member of causes the lca and binary outcome variables association, the symptoms will be "conditionally independent", i. The LCA will attempt to detect the presence of latent classes the disease lca and binary outcome variablescreating patterns of association in the symptoms. A class is characterized by a pattern of conditional probabilities that indicate the chance that variables take on certain values.

These subtypes are called "latent classes". The LCA will attempt to detect the presence of latent classes the disease entitiescreating patterns of association in the symptoms. By using this site, you agree to the Terms of Use and Privacy Policy.