Book contents
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- 28 Reproducibility in Clinical Psychology
- 29 Meta-Analysis
- 30 Mediation, Moderation, and Conditional Process Analysis
- 31 Statistical Inference for Causal Effects in Clinical Psychology
- 32 Analyzing Nested Data
- 33 Missing Data Analyses
- 34 Machine Learning for Clinical Psychology and Clinical Neuroscience
- Index
- References
32 - Analyzing Nested Data
Multilevel Modeling and Alternative Approaches
from Part VII - General Analytic Considerations
Published online by Cambridge University Press: 23 March 2020
Book contents
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- 28 Reproducibility in Clinical Psychology
- 29 Meta-Analysis
- 30 Mediation, Moderation, and Conditional Process Analysis
- 31 Statistical Inference for Causal Effects in Clinical Psychology
- 32 Analyzing Nested Data
- 33 Missing Data Analyses
- 34 Machine Learning for Clinical Psychology and Clinical Neuroscience
- Index
- References
Summary
Nested data arise frequently in clinical research. The nesting might be hierarchical, such as patients nested within clinicians, or it might be longitudinal, such as repeated assessments over time nested within individuals. As articulated in this chapter, whenever and however nesting occurs, it is necessary to account for the statistical dependence of observations within units when analyzing the data. Further, it is important to determine the level(s) of the data at which predictors exert their effects. Multilevel models are a particularly popular and useful approach for addressing these issues. We thus describe these models in detail, illustrating the application of multilevel models in clinical research via two examples. The first example considers nesting of siblings within families and demonstrates the importance of separating within- versus between-family effects. The second example focuses on the application of multilevel models with repeated measures to evaluate within-person change over time. Additionally, we provide a brief survey of other approaches to the analysis of nested data (e.g., cluster-robust standard errors, generalized estimating equations, fixed-effects models).
Keywords
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2020
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