Description

During the last two decades, structural equation modelling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Social science researchers and students benefit greatly from acquiring knowledge and skills in SEM, since the methods can provide a bridge between the theoretical and empirical aspects of behavioural research. Ramlall explains in a rigorous, concise, and practical manner all the vital components embedded in structural equation modelling (SEM). Focusing on R and Stata to implement and perform various structural equation models, Ramlall examines the types, benefits, and drawbacks of SEM, delving into model specifications and identifications, fit evaluations, and path diagrams.

Applied Structural Equation Modelling for Researchers and Practitioners: Using R and Stata for Behavioural Research

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Hardback by Indranarain Ramlall

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During the last two decades, structural equation modelling (SEM) has emerged as a powerful multivariate data analysis tool in social... Read more

    Publisher: Emerald Publishing Limited
    Publication Date: 16/12/2016
    ISBN13: 9781786358837, 978-1786358837
    ISBN10: 1786358832

    Number of Pages: 152

    Non Fiction , Politics, Philosophy & Society

    Description

    During the last two decades, structural equation modelling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Social science researchers and students benefit greatly from acquiring knowledge and skills in SEM, since the methods can provide a bridge between the theoretical and empirical aspects of behavioural research. Ramlall explains in a rigorous, concise, and practical manner all the vital components embedded in structural equation modelling (SEM). Focusing on R and Stata to implement and perform various structural equation models, Ramlall examines the types, benefits, and drawbacks of SEM, delving into model specifications and identifications, fit evaluations, and path diagrams.

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