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The Cambridge Handbook of Research Methods in Clinical Psychology

The Cambridge Handbook of Research Methods in Clinical Psychology

9781107189843
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Description
This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.
Product Details
Cambridge University Press
82323
9781107189843
9781107189843

Data sheet

Publication date
2020
Issue number
1
Cover
hard cover
Pages count
600
Dimensions (mm)
220.00 x 285.00
Weight (g)
1610
  • Section I. Clinical Psychological Science:: An Evolving Field:: 1. Trends in the evolving discipline of clinical psychology; 2. Defining and refining phenotypes:: operational definitions as open concepts; 3. Building models of psychopathology spanning multiple modalities of measurements; Section II. Observational Approaches:: 4. The conceptual foundations of descriptive psychopathology; 5. Survey and interview methods; 6. Psychometrics in clinical psychology research; 7. Latent variable models in clinical psychology; 8. Psychiatric epidemiology methods; Section III. Experimental and Biological Approaches:: 9. Conceptual foundations of experimental psychopathology:: historical context, scientific posture, and reflections on substantive and method matters; 10. A practical guide for designing and conducting cognitive studies in child psychopathology; 11. Peripheral psychophysiology; 12. Behavioral and molecular genetics; 13. Concepts and principles of clinical functional magnetic resonance imaging; 14. Reinforcement learning approaches to computational clinical neuroscience; Section IV. Developmental Psychopathology and Longitudinal Methods:: 15. Studying psychopathology in early life:: foundations of developmental psychopathology; 16. Adolescence and puberty:: understanding the emergency of psychopathology; 17. Quantitative genetics research strategies for studying gene-environment interplay in the development of child and adolescent psychopathology; 18. Designing and managing longitudinal studies; 19. Measurement and comorbidity models for longitudinal data; Section V. Intervention Approaches:: 20. The multiphase optimization strategy for developing and evaluating behavioral interventions; 21. Future directions in developing and evaluating psychological interventions; 22. Health psychology and behavioral medicine:: methodological issues in the study of psychosocial influences on disease; Section VI. Intensive Longitudinal Designs:: 23. Ambulatory assessment; 24. Modeling intensive longitudinal data; 25. Modeling the individual:: bridging nomothetic and idiographic levels of analysis; 26. Social processes and dyadic designs; 27. Models for dyadic data; Section VII. General Analytic Considerations:: 28. Reproducibility in clinical psychology; 29. Meta-analysis:: integration of empirical findings through quantitative modeling; 30. Mediation, moderation, and conditional process analysis:: regression-based approaches for clinical research; 31. Statistical inference for causal effects in clinical psychology:: fundamental concepts and analytical approaches; 32. Analyzing nested data:: multilevel modeling and alternative approaches; 33. Missing data analyses; 34. Machine learning for clinical psychology and clinical neuroscience.
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