All of the models presented here add advanced features to “baseline” static and dynamic latent class models. This includes information about latent class moderation, adding inverse propensity score weights to models, multilevel latent class models, and associative latent transition analysis, among other topics. Note that these models fall under the broad classification of loglinear modeling with latent variables.
LCA: Baseline LCA with 3+ level categorical indicators
Description 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. This code corresponds to the research paper titled “Intersecting racism and homonegativism among sexual minority men of color: Latent class analysis of multidimensional stigma with subgroup differences in health and sociostructural burdens” published in Social Science & Medicine in 2022. The paper can be found here: https://www.sciencedirect.com/science/article/abs/pii/S0277953621009345 Software Downloads Mplus Model Features Model Category Your Content Goes Here Model Type Your Content...
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.
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 grouping variable with measurement invariance
Description 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. Software Downloads Latent Gold Mplus Exercise Exercise 6 This exercise asks you to select and interpret a latent profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable as well as add a grouping variable for biological sex. Then, it asks you to interpret all parameters in the model. Please be sure to impose measurement...
LPA: Baseline LPA with continuous and categorical indicators (mixed indicator model)
Description 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. This code corresponds to the research paper titled “Constellations of Family Risk for Long-Term Adolescent Antisocial Behavior” published in Journal of Family Psychology in 2020. The paper can be found here: https://pmc.ncbi.nlm.nih.gov/articles/PMC7375013/ Software Downloads Mplus Model Features Model Category Your Content Goes Here Model Type Your Content Goes Here Indicator Type Your Content Goes Here Available Software Your Content Goes Here Measurement Invariance Your Content Goes Here Approach to Outcomes Your...
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
Description 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. This code corresponds to the research paper titled “Triadic Family Structures and Their Day-to-Day Dynamics From an Adolescent Perspective: A Multilevel Latent Profile Analysis” published in Fam Process in 2022. Note that there is an important difference between the code available here and the exact code used to fit the model in the paper: in the code available here the measurement model is freely estimated, whereas in the paper the measurement model was fixed. The paper can...
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