Westland places a strong emphasis on research design and data adequacy, addressing topics often neglected in standard "cookbook" textbooks.
: The book explains how SEM accommodates unobservable theory constructs (like "intelligence" or "satisfaction") through latent variables, which is a critical feature for social science research.
(2019) by J. Christopher Westland is a concise reference that explores the evolution and application of Structural Equation Modeling (SEM). It is unique for showcasing a wide range of methodologies—from historical path analysis to modern neural network-based approaches—rather than focusing on just one school of thought. Core Themes and Historical Context Structural Equation Models: From Paths to Networks
The book frames SEM methodologies within their proper historical context to help researchers understand the specific strengths and weaknesses of different methods.
: The text covers the full range of SEM, including: Westland places a strong emphasis on research design
: Discussed extensively, including its differences from PLS regression.
: It traces SEM back to the natural sciences, specifically biology and Sewall Wright’s (1921) path analysis , which was developed to make sense of diverse biological observations. Christopher Westland is a concise reference that explores
: Chapters 4 and 5 provide detailed guidelines for data preparation and sample size calculations when using Likert scales instead of continuous metrics.