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
33 - Missing Data Analyses
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
The methodological literature recommends multiple imputation and maximum likelihood estimation as best practices in handling missing data in published research. Relative to older methods such as listwise and pairwise deletion, these approaches are preferable because they rely on a less stringent assumption about how missingness relates to analysis variables. Furthermore, in contrast to deletion methods, multiple imputation and maximum likelihood estimation enable researchers to include all available data in the analysis, resulting in increased statistical power. This chapter provides an overview of multiple imputation and maximum likelihood estimation for handling missing data. Using an example from a study of predictors of depressive symptoms in children with juvenile rheumatic diseases, the chapter illustrates the use of multiple imputation and maximum likelihood estimation using variety of statistical software packages.
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2020
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