- 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
Multilevel modeling in general concerns models for relationships between variables defined at different levels of a hierarchical data set, which is often viewed as a multistage sample from a hierarchically structured population. Common applications are individuals within groups, repeated measures within individuals, longitudinal modeling, and cluster randomized trials. This chapter treats the multilevel regression model, which is a direct extension of single-level multiple regression, and multilevel structural equation models, which includes multilevel path and factor analysis. Multilevel analysis was originally intended for continuous normally distributed data. This chapter refers to recent extensions to non-normal data but does not treat these in detail. The end of the chapter presents some statistical issues such as assumptions, sample sizes, and applications to data that are not completely nested.
Keywords: Multilevel model, mixed model, random coefficient, cluster sampling, hierarchical data
Joop J. Hox, Department of Methodology and Statistics, Utrecht University, The Netherlands
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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
