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In statisticsa latent class model LCM relates a set of observed usually discrete multivariate variables to a set of latent variables. It is a type of latent variable model. It is called a latent class model because the latent variable is discrete.

A class is characterized by a pattern of conditional probabilities that indicate the chance that variables take on certain values. Latent class analysis LCA is a subset of structural equation modelingused to find groups or subtypes of cases in multivariate categorical data.

These subtypes are called "latent classes". Confronted with a situation as follows, a researcher might choose to use LCA to understand the data: Imagine lca and binary outcome variables symptoms a-d have been measured in a range of patients with diseases X Y and Z, and that disease X is associated with the presence of symptoms a, b, and c, disease Y with symptoms b, c, d, and disease Z with symptoms a, c and d.

The LCA will attempt to detect the presence of latent classes the disease entitiescreating patterns of association in the symptoms.

As in factor analysis, the LCA can also be used lca and binary outcome variables classify case according to their maximum likelihood class membership. Because the criterion for solving the LCA is to achieve latent classes within which there is no longer any association of one symptom with lca and binary outcome variables 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.

Within each latent class, the observed variables are statistically independent. This is an important aspect. Usually the observed variables are statistically dependent.

By introducing the latent variable, independence is restored in the sense that within classes variables are independent local independence.

We then say that the association between the observed variables is explained by the classes of the latent variable McCutcheon, This two-way model is related to probabilistic latent semantic analysis and non-negative matrix factorization.

As in much of statistics, there is a large number of methods with lca and binary outcome variables names and uses, which share a common relationship. Cluster analysis is, like LCA, used to discover taxon-like groups of cases in data. Multivariate mixture estimation MME is applicable to continuous data, and assumes that such data arise from a mixture of distributions: If a multivariate mixture estimation is constrained so that measures must be uncorrelated within each distribution it is termed latent profile analysis.

Modified to handle discrete data, lca and binary outcome variables constrained analysis is known as LCA. Discrete latent trait models further constrain the classes to form from segments of a single dimension: As a practical instance, the variables could be multiple choice items of a political questionnaire.

Software anabinary optionsi opzionix data in this case consists of a N-way contingency table with answers to the items for a number lca and binary outcome variables respondents.

In this example, the latent variable refers to political opinion and the latent classes to political groups. Given group membership, the conditional probabilities specify the chance certain answers are chosen.

LCA may be used in many fields, such as: From Wikipedia, the free encyclopedia. Introduction to theory and application]. Retrieved from " https: Classification algorithms Latent variable models Market research Market segmentation.

Views Read Edit Lca and binary outcome variables history. This page was last edited on 17 Decemberat By using this site, you agree to the Terms of Use and Privacy Policy.

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