All of the models presented here are considered “static” in the sense that they model a single categorical latent variable where an individual’s class membership does not change. These models include latent class analysis (LCA), latent profile analysis (LPA), and mixed indicator models. Although different names for these models appear throughout the literature, here we use the convention that models that include only categorical indicators are LCAs, include only continuous indicators are LPAs, and include both categorical and continuous indicators are mixed indicator latent class models.
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.
LCA: LCA with a covariate (1-step approach)
Description 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. Software Downloads Latent Gold Mplus SAS Stata Exercise Exercise 5 This exercise asks you to use a model-based approach (1-step approach) to add a covariate for grades to a 4-class model for marijuana use and attitudes that uses 7 binary indicators of the latent class variable. You have to carefully consider what latent class to use as the reference class in the multinomial logistic regression. You may wish to standardize the grades...
LCA: LCA with a covariate and a grouping variable (1-step approach)
Description 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 and a grouping variable for year in the model. Software Downloads Latent Gold SAS Exercise Exercise 5 This exercise asks you to use a model-based approach (1-step approach) to add a covariate for grades and a grouping variable for year to a 4-class model for marijuana use and attitudes that uses 7 binary indicators of the latent class variable. You have to carefully consider what latent class to use as the reference class in the...
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
Description 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. Software Downloads Latent Gold Mplus SAS Stata Exercise Exercise 4 This exercise asks you to add a grouping variable for year to a 4-class model for marijuana use and attitudes that uses 7 binary indicators of the latent class variable. It asks you to fit a model...
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
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 a covariate. 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 covariate for biological sex. Then, it asks you to interpret all parameters in the model. Note that, by default in most software packages, the variances of the indicators are restricted to...
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...
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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