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The Cambridge handbook of research methods in clinical psychology / edited by Aidan G. C. Wright, Michael N. Hallquist.

Contributor(s): Wright, Aidan G. C, 1980- [editor.] | Hallquist, Michael N, 1981- [editor.].
Material type: materialTypeLabelBookPublisher: Cambridge : Cambridge University Press, 2020.Description: 1 online resource (xiii, 498 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781316995808 (ebook).Subject(s): Clinical psychology -- Research -- Methodology -- Handbooks, manuals, etcDDC classification: 616.89 Online resources: Click here to access online Summary: 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.
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Title from publisher's bibliographic system (viewed on 25 Mar 2020).

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.

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