- Oxford Library of Psychology
- [UNTITLED]
- Oxford Library of Psychology
- About the Editor
- Contributors
- Introduction
- Overview of Traditional/Classical Statistical Approaches
- Generalized Linear Models
- Categorical Methods
- Configural Frequency Analysis
- Nonparametric Statistical Techniques
- Correspondence Analysis
- Spatial Analysis
- Analysis of Imaging Data
- Twin Studies and Behavior Genetics
- Quantitative Analysis of Genes
- Multidimensional Scaling
- Latent Variable Measurement Models
- Multilevel Regression and Multilevel Structural Equation Modeling
- Structural Equation Models
- Developments in Mediation Analysis
- Moderation
- Longitudinal Data Analysis
- Dynamical Systems and Models of Continuous Time
- Intensive Longitudinal Data
- Dynamic Factor Analysis: Modeling Person-Specific Process
- Time Series Analysis
- Analyzing Event History Data
- Clustering and Classification
- Latent Class Analysis and Finite Mixture Modeling
- Taxometrics
- Missing Data Methods
- Secondary Data Analysis
- Data Mining
- Meta-Analysis and Quantitative Research Synthesis
- Common Fallacies in Quantitative Research Methodology
- Index
Abstract and Keywords
Finite mixture models, which are a type of latent variable model, express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of manifest variables results from the existence of two or more distinct homogeneous subgroups, or latent classes, of individuals. This chapter presents the prevailing “best practices” for direct applications of basic finite mixture modeling, specifically latent class analysis (LCA) and latent profile analysis (LPA), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. In addition, a brief introduction to structural equation mixture modeling in the form of latent class regression is provided as well as a partial overview of the many more advanced mixture models currently in use. The chapter closes with a cautionary note about the limitations and common misuses of latent class models and a look toward promising future developments in mixture modeling.
Keywords: Finite mixture, latent class, latent profile, latent variable
Katherine E. Masyn, Graduate School of Education, Harvard University, Cambridge, MA
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- Oxford Library of Psychology
- [UNTITLED]
- Oxford Library of Psychology
- About the Editor
- Contributors
- Introduction
- Overview of Traditional/Classical Statistical Approaches
- Generalized Linear Models
- Categorical Methods
- Configural Frequency Analysis
- Nonparametric Statistical Techniques
- Correspondence Analysis
- Spatial Analysis
- Analysis of Imaging Data
- Twin Studies and Behavior Genetics
- Quantitative Analysis of Genes
- Multidimensional Scaling
- Latent Variable Measurement Models
- Multilevel Regression and Multilevel Structural Equation Modeling
- Structural Equation Models
- Developments in Mediation Analysis
- Moderation
- Longitudinal Data Analysis
- Dynamical Systems and Models of Continuous Time
- Intensive Longitudinal Data
- Dynamic Factor Analysis: Modeling Person-Specific Process
- Time Series Analysis
- Analyzing Event History Data
- Clustering and Classification
- Latent Class Analysis and Finite Mixture Modeling
- Taxometrics
- Missing Data Methods
- Secondary Data Analysis
- Data Mining
- Meta-Analysis and Quantitative Research Synthesis
- Common Fallacies in Quantitative Research Methodology
- Index
