Structural Equation Models: From Paths to Networks (Westland 2019)
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STRUCTURAL EQUATION MODELS: FROM PATHS TO NETWORKS (WESTLAND 2019)
Marko Sarstedt OTTO-VON-GUERICKE-UNIVERSITY MAGDEBURG MONASH UNIVERSITY MALAYSIA
Christian M. Ringle HAMBURG UNIVERSITY OF TECHNOLOGY UNIVERSITY OF WAIKATO
Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire. From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) lowdimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.
About the Book Westland’s (2019) Structural Equation Models: From Paths to Networks offers a concise, wellwritten, and non-technical reference for SEM. The textbook comprises 149 pages structured into eight chapters. The chapters are largely independent of one another, allowing them to be easily covered in a different order. The content of Westland’s (2019) textbook makes it very attractive as additional reading for methodological courses devoted to factor-based SEM and wanting to delve into the method’s historic roots. Readers will also appreciate the various overview tables offering excellent summaries of selected contents, as well as the book’s index, which allows them to quickly identify topics and key terms of interest. Correspondence should be made to Marko Sarstedt, School of Business and GA21, Monash University Malaysia, Subang Jaya, Selangor, Malaysia. Email: [email protected]
© 2020 The Author(s)
PSYCHOMETRIKA
Whereas most textbooks focus on either factor-based or component-based SEM (e.g., Hair et al. 2017; Kline 2016), Westland’s (2019) book is unique in that it showcases the full range of SEM methodologies, starting with Wright’s (1921) path analysis, followed
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