Sem Paths to Networks Westland

What it’s about

This book provides a comprehensive and critical guide to the world of structural equation modeling (SEM) for researchers and doctoral students. It traces the evolution of path analysis methodologies from their roots in genetics with Sewall Wright, through the divergent developments of the Scandinavian school (PLS-PA, LISREL) and the Chicago school (systems of regression equations). The author demystifies the statistical underpinnings of each approach, highlighting their unique strengths, weaknesses, and the often-misrepresented controversies surrounding them. It offers indispensable practical advice on crucial research design aspects, including data collection, calculating adequate sample size, and the proper treatment of survey data, particularly the pitfalls of Likert scales. By equipping readers with a deep understanding of the assumptions and limitations of these powerful tools, the book aims to prevent common errors and elevate the quality of quantitative research, ultimately showing how the path-based thinking of SEM is merging into the broader, more powerful domain of network analysis.

The through-line

Who it’s for

A quantitative social science researcher or doctoral student who needs to test complex theoretical models involving unobservable concepts like 'trust' or 'satisfaction'. They want to produce rigorous, defensible, and publishable findings.

The problem

The researcher is confronted with a confusing landscape of SEM methodologies (PLS-PA, LISREL, etc.) and software, each with conflicting claims, arcane assumptions, and inadequate guidance on critical issues like sample size calculation and handling survey data. They feel uncertain about their methodological choices, anxious that their results might be invalid or rejected by reviewers, and fearful of inadvertently contributing to the proliferation of 'bad science'.

The plan

1. Understand the historical context and statistical underpinnings of different SEM methods.
2. Learn the specific strengths and weaknesses of PLS-PA, LISREL, and Systems of Regression to choose the right tool for your research.
3. Master the principles of calculating adequate sample size and properly handling Likert scale data.
4. Adopt a rigorous paradigm for model specification, testing, and interpretation.
5. See how path modeling is evolving into the broader science of network analysis.

The payoff

The researcher confidently designs robust studies, justifies their methodological choices, and produces valid, defensible results. · Their work is published in high-impact journals and contributes meaningfully to their field. · They are recognized as a methodologically rigorous and thoughtful scholar.

See our guide

• The Four-S Discipline — One System, Four Capabilities →
• See How The Pieces Connect — Models That Link Drivers To Outcomes →
• Study People And Organizations With Scientific Rigor →
• Choose And Apply The Right Analytical Method →
• The Behavioral Science Underneath It All →
• Build A People Analytics Function →
• Design The Organization And Change It →
• Do Research That Holds Up →
• Design Surveys People Answer Honestly →
• Understand What Drives Individual Performance →

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Additional reading

• Latent variable path modeling with partial least squares · J.-B. Lohmöller

  The book identifies this as the foundational text describing the PCA-OLS algorithm that is implemented, often unmodified, in most modern PLS-PA software packages.

• Why Most Published Research Findings Are False · J. P. A. Ioannidis

  The book highlights this paper's findings to underscore the severe real-world consequences of the methodological weaknesses (like inadequate sample size) that it aims to address.

• The Design of Experiments · R. A. Fisher

  Referenced as a classic text that establishes the fundamental principles of statistical inference, variation, and data reduction that underlie all valid empirical research, including SEM.

Featured in these guides

• The Behavioral Science Underneath It All →Cited source
• Build A People Analytics Function →Cited source
• See How The Pieces Connect — Models That Link Drivers To Outcomes →Cited source
• Design The Organization And Change It →Cited source
• Study People And Organizations With Scientific Rigor →Cited source
• Do Research That Holds Up →Cited source
• Choose And Apply The Right Analytical Method →Cited source
• Design Surveys People Answer Honestly →Cited source
• Understand What Drives Individual Performance →Cited source