Statistics Definitions > Latent Class Analysis

## What is Latent Class Analysis?

Latent Class Analysis (LCA) is a way to use observed variables to group subjects from multivariate data into “latent classes,” which are groups or subgroups with similar membership. Examples of latent classes include:

- People based on how much they drink.
- Patients based on signs and symptoms.
- People based on purchasing patterns in a store.
- Examinees based on answers to test questions.

Latent Class Analysis **uncovers the complex patterns of association that can exist between observations**. The latent classes are formed with conditional probability patterns that indicate the chance variables will take on certain values.

Latent Class Analysis is very similar to Factor Analysis; the main difference is that LCA includes categorical dependent variables and Factor Analysis does not.

LCA can be used with binary data, Likert scale, nominal variables or ordered categorical variables. It can’t be used with ordinal variables.

## Types of Latent Class Analysis

LCA falls into three broad categories:

- Cluster models: identifies
*clusters*that group people together, based on similar behaviors, characteristics, interests, or values. Clusters are represented by a K-category latent variable. The number and size of the classes are not known beforehand. - Factor models: identifies
*factors*that group together variables with a common source of variation. - Regression models: predict a dependent variable as a function of predictors.

## Software for Latent Class Analysis

Many popular statistical software programs, like IBM SPSS, do not have the capability for running LCA. At the time of writing, IBM does plan to add LCA to SPSS in the future. Programs that do support LCA include the popular R and SAS. Other, less well-known programs (some of which, like MLLSA, are free) include:

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