- 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
The focus of this chapter is on the principles and methods of latent variable measurement models in applied research. After a review of the common factor model, examples of exploratory factor analysis and confirmatory factor analysis are provided along with a recently developed hybrid of these two approaches (exploratory structural equation modeling). In addition, more advanced applications are illustrated, including multiple-group models, to evaluate measurement invariance and population heterogeneity, and various types of higher-order factor models (e.g., second-order factor analysis, bifactor models). Future directions are discussed, including more recent advances in these methodologies (e.g., factor mixture models, multilevel factor models, nonlinear factor models).
Keywords: common factor model, latent variable, exploratory factor analysis, confirmatory factor analysis, exploratory structural equation modeling, measurement invariance, multiple-group solutions, higher-order factor analysis, bifactor model
Timothy A. Brown, Center for Anxiety and Related Disorders, Boston University.
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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
