This Handbook provides an overview and guide for state-of-the-art research methods in clinical psychological science. It is the ideal resource for clinical psychologists, graduate students studying clinical psychology, and practitioners or researchers in allied disciplines including psychiatry and social work.
This Handbook provides an overview and guide for state-of-the-art research methods in clinical psychological science. It is the ideal resource for clinical psychologists, graduate students studying clinical psychology, and practitioners or researchers in allied disciplines including psychiatry and social work.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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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