The article by Gitta H. Lubke and Bengt Muthén explores the use of factor mixture models to investigate population heterogeneity, particularly when the sources of heterogeneity are unobserved. Factor mixture models combine latent class and common factor models, allowing for the exploration of unobserved population heterogeneity while incorporating observed covariates. The authors discuss the characteristics of factor mixture models compared to other methods for analyzing heterogeneous data, such as multiple group analyses and latent class analysis. They provide a detailed description of the factor mixture model, including its components and how it can be used to model both categorical and continuous observed variables. The article also covers different ways to specify the effects of covariates, emphasizing the importance of measurement invariance. A step-by-step analysis of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied to explore population heterogeneity, focusing on mean differences between latent classes and the impact of background variables. The authors conclude by highlighting the flexibility and interpretability of factor mixture models in handling complex data structures and the importance of theoretical considerations in specifying class-specific effects.The article by Gitta H. Lubke and Bengt Muthén explores the use of factor mixture models to investigate population heterogeneity, particularly when the sources of heterogeneity are unobserved. Factor mixture models combine latent class and common factor models, allowing for the exploration of unobserved population heterogeneity while incorporating observed covariates. The authors discuss the characteristics of factor mixture models compared to other methods for analyzing heterogeneous data, such as multiple group analyses and latent class analysis. They provide a detailed description of the factor mixture model, including its components and how it can be used to model both categorical and continuous observed variables. The article also covers different ways to specify the effects of covariates, emphasizing the importance of measurement invariance. A step-by-step analysis of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied to explore population heterogeneity, focusing on mean differences between latent classes and the impact of background variables. The authors conclude by highlighting the flexibility and interpretability of factor mixture models in handling complex data structures and the importance of theoretical considerations in specifying class-specific effects.