Equation Models: From Paths To Networks — Structural

: Contemporary methods that expand SEM's ability to map complex, naturally occurring networks. Key Technical Insights

: 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. 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. : Contemporary methods that expand SEM's ability to

: Discussed extensively, including its differences from PLS regression. : Discussed extensively, including its differences from PLS

: The text covers the full range of SEM, including:

(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

Westland places a strong emphasis on research design and data adequacy, addressing topics often neglected in standard "cookbook" textbooks.