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        Multilevel Modelling for Public Health and Health Services Research

        Health in Context

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        Author(s)
        Leyland, Alastair H.
        Groenewegen, Peter P.
        Language
        English
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        Abstract
        This open access book is a practical introduction to multilevel modelling or multilevel analysis (MLA) – a statistical technique being increasingly used in public health and health services research. The authors begin with a compelling argument for the importance of researchers in these fields having an understanding of MLA to be able to judge not only the growing body of research that uses it, but also to recognise the limitations of research that did not use it. The volume also guides the analysis of real-life data sets by introducing and discussing the use of the multilevel modelling software MLwiN, the statistical package that is used with the example data sets. Importantly, the book also makes the training material accessible for download – not only the datasets analysed within the book, but also a freeware version of MLwiN to allow readers to work with these datasets. The book’s practical review of MLA comprises: Theoretical, conceptual, and methodological background Statistical background The modelling process and presentation of research Tutorials with example datasets Multilevel Modelling for Public Health and Health Services Research: Health in Context is a practical and timely resource for public health and health services researchers, statisticians interested in the relationships between contexts and behaviour, graduate students across these disciplines, and anyone interested in utilising multilevel modelling or multilevel analysis. “Leyland and Groenewegen’s wealth of teaching experience makes this book and its accompanying tutorials especially useful for a practical introduction to multilevel analysis.” ̶ Juan Merlo, Professor of Social Epidemiology, Lund University “Comprehensive and insightful. A must for anyone interested in the applications of multilevel modelling to population health”. ̶ S. (Subu) V. Subramanian, Professor of Population Health and Geography, Harvard University ; For researchers and students with a basic mastery of ordinary least squares and logistic regression Discusses multilevel analysis in context of public health, health services research, and epidemiology Includes an online component where users can download the datasets analyzed in the book, and also a freeware version of the multilevel modelling software MLwiN ​​​​​​​Can be used as part of a course on multilevel modelling, or as a self-training text
        URI
        http://library.oapen.org/handle/20.500.12657/39565
        Keywords
        Public Health; Health Services Research; Research Methodology; Epidemiology; Statistics for Life Sciences, Medicine, Health Sciences; Health Sciences; Sociological Methods; Statistics in Life Sciences, Medicine, Health Sciences; MLwiN; epidemiological methods; health services research methodology; multilevel analysis; multilevel modelling; statistical methods for public health research; Open Access; Public health & preventive medicine; Health systems & services; Social research & statistics; Epidemiology & medical statistics; Probability & statistics
        DOI
        10.1007/978-3-030-34801-4
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2020
        Imprint
        Springer
        Classification
        Public health and preventive medicine
        Health systems and services
        Social research and statistics
        Epidemiology and Medical statistics
        Probability and statistics
        Pages
        286
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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