Description

Latent class modeling refers to a group of techniques for identifying unobservable, or latent, subgroups within a population. Researchers have developed and expanded methods like latent class analysis (LCA) and latent transition analysis (LTA) over the last two decades. Our current research focuses on expanding methods to include latent class variables in larger models of complex developmental processes. Latent class analysis (LCA) identifies unobservable subgroups within a population. We work to expand LCA models to allow scientists to better understand the impact of exposure to patterns of multiple risks, as well as the antecedents and consequences of complex behaviors, so that interventions can be tailored to target the subgroups that will benefit most. Latent transition analysis (LTA) is a related method that allows scientists to estimate movement between subgroups over time.

LCA example

LCA Introductory Example: Profiles of Teen Sex and Drug Use

In this example, LCA identifies five subgroups of teenagers based on their substance use and sexual behaviors. The latent variable “youth risk behavior” is measured by the observed variables “sex,” “drinking,” “smoking,” and “other drugs.” This analysis allows us to identify complex behavior patterns and variables that predict high-risk behavior patterns, as well as identify subgroups of youth who are at-risk for negative health consequences. With this information, scientists can develop interventions that target individuals with the greatest need.

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Resources

There are a variety of resources available to help you learn more about LCA. See our Resources page for the following:

Static Models

LCA: Adding outcomes using an adjusted 3-step approach (automated, BCH)

Description This code adds a binary and a continuous outcome to the 4-class baseline LCA model with all binary indicators from Exercise 1 in the linked page. The binary outcome is political beliefs (not conservative vs. conservative) and the continuous outcome is the number of evenings out per week (average 0-7). This code uses the automated 3-step BCH approach as we explore the association between latent class membership and each outcome. Software Downloads Mplus Exercises Exercise 1 This exercise asks you to fit a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class...

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LCA: Adding outcomes using an adjusted 3-step approach (manual, BCH)

Description This code adds a binary and a continuous outcome to the 4-class baseline LCA model with all binary indicators from Exercise 1 in the linked page. The binary outcome is political beliefs (not conservative vs. conservative) and the continuous outcome is the number of evenings out per week (average 0-7). This code uses the the manual 3-step BCH approach as we explore the association between latent class membership and each outcome. Software Downloads Mplus Exercises Exercise 1 This exercise asks you to fit a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent...

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LCA: Baseline LCA with 3+ level categorical indicators

This code fits a longitudinal latent class model, using categorical indicators with 3+ levels, to identify latent classes indicated by multidimensional experiences of racism and heterosexism during the transition to adulthood among sexual minority men of color.

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LCA: Latent class moderation

This code demonstrates how to use a latent class moderator to examine heterogeneity in intervention effects among adolescents receiving treatment for cannabis use. First, the code identifies latent classes of contextual and individual risk at baseline using LCA. Then, it uses an adjusted 3-step approach with BCH weights to regress the outcomes on level of care, latent class membership, the interaction between them, and covariates.

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LCA: LCA with a grouping variable and without measurement variance

This code fits a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. It includes a grouping variable for year, and observations came from 3 different years. Measurement invariance across groups is not imposed resulting in an unrestricted latent class model with multiple groups.

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LPA: LPA with a grouping variable with measurement invariance across means and variances

Description This code fits a baseline, latent-profile model to identify and describe profiles of financial stress responses. It also imposes measurement invariance across the groups with means and variances equal. This code corresponds to the research paper titled “Financial stress response profiles and psychosocial functioning in low-income parents” published in Journal of Family Psychology in 2018. The paper can be found here: https://pubmed.ncbi.nlm.nih.gov/29878812/ Software Downloads Mplus Model Features Model Category Your Content Goes Here Model Type Your Content Goes Here Indicator Type Your Content Goes Here Software Options Your Content Goes Here Measurement Invariance Your Content Goes Here Approach to...

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LPA: LPA with a grouping variable without measurement invariance

Description This code fits a baseline, latent-profile model to identify and describe profiles of financial stress responses. It doesn’t impose measurement invariance across the groups. This model is similar to the model in the research paper titled “Financial stress response profiles and psychosocial functioning in low-income parents” published in Journal of Family Psychology in 2018. One key difference is that this model DOES NOT impose measurement invariance while the model in the paper DOES impose measurement invariance. The paper can be found here: https://pubmed.ncbi.nlm.nih.gov/29878812/ The code for the model in the paper (i.e. with measurement invariance) can be found here....

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