These models include example Mplus code. Mplus and its corresponding documentation is available at www.statmodel.com. Note that we recommend the “combination add-on” version.
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 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...
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 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...
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.
LCA: Baseline LCA with all binary indicators
This code fits a 4-class, baseline, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. This code also plots the item-response probabilities using a line graph.
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.
LCA: LCA with a covariate (1-step approach)
This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades in the model.
LCA: LCA with a grouping variable and measurement invariance
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.
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.
LPA: Baseline LPA with all continuous indicators
This code fits a 5-class, baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable.
LPA: Baseline LPA with all continuous indicators and a covariate
This code fits a baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable and biological sex as a covariate.
LPA: Baseline LPA with all continuous indicators and a grouping variable with measurement invariance
This code fits a baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable and biological sex as the grouping variable. It also imposes measurement invariance across the groups.
LPA: Baseline LPA with continuous and categorical indicators (mixed indicator model)
This code fits a mixed indicator latent-profile model (using both continuous and categorical indicators) to identify family subgroups that conform to risk factors associated with adolescent antisocial behavior.
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...
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....
LTA: Baseline LTA with 2 times, all binary indicators, and measurement invariance
This code fits a 2-time, 5-class, latent-transition model for delinquency over time using 6 binary indicators of the latent class variable. Measurement invariance across time is imposed such that analogous item-response probabilities within classes are restricted to be equal to each other across times.
Multilevel LPA: Baseline two-level LPA with classes at level 1 and level 2
This code fits a 2-level latent-profile model using a “non-parametric approach” to identify mother-father-adolescent relationship structures and dynamics on a daily basis.

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