The PLS-SEM Book

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2027). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (4 ed.). Sage.

The fourth edition is aligned with SmartPLS 4 (Ringle et al., 2024) and offers a practical, step-by-step introduction to partial least squares structural equation modeling (PLS-SEM).

Get the fourth edition from Sage

Brief description

A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) is the standard introductory text for researchers, doctoral students, instructors, and applied analysts who want to learn how to specify, estimate, evaluate, and report PLS-SEM models. The fourth edition explains the method in clear language with limited emphasis on formulas and Greek symbols, making it suitable for readers with limited statistical or mathematical training.

The book uses a running corporate reputation case study to illustrate the core stages of a PLS-SEM analysis. Readers learn how to create a path model, examine data, run the PLS-SEM algorithm, assess reflective and formative measurement models, evaluate the structural model, analyze mediation and moderation, assess prediction, and apply advanced methods.

What is new in the fourth edition?

The fourth edition updates the Primer for current PLS-SEM research, the SmartPLS 4 software environment, and recent best-practice recommendations. Important updates include:

SmartPLS 4 throughout the book: Updated screenshots, workflows, and software instructions help readers apply the book examples in the current SmartPLS 4 interface.
Updated PLS-SEM fundamentals: The book reflects current methodological insights on the nature, goals, and appropriate use of PLS-SEM as a causal-predictive approach to structural equation modeling.
Expanded model design and specification guidance: The fourth edition gives more attention to model design, structural model specification, measurement model specification, control variables, and special modeling situations such as binary moderators.
Updated estimation and model fit coverage: The book expands the discussion of model estimation and model fit so that readers can better understand how to build, estimate, and judge robust PLS path models.
Revised measurement and structural model evaluation procedures: The fourth edition updates guidance for assessing reflective measurement models, formative measurement models, and structural model results.
Stronger prediction-oriented assessment: New and updated coverage helps researchers evaluate predictive performance, including PLSpredict and the cross-validated predictive ability test (CVPAT).
More detailed mediation and moderation analysis: The book extends the treatment of mediator and moderator analysis, including effect size estimation, parallel mediation, binary moderators, and more complex conditional relationships.
New and expanded advanced methods: Advanced topics include combined importance-performance map analysis (cIPMA), necessary condition analysis (NCA), endogeneity assessment with Gaussian copulas, latent class techniques for unobserved heterogeneity, measurement invariance, and consistent PLS-SEM.
Learning-outcome-based summaries: Chapter summaries are organized around learning outcomes to support teaching, review, and self-study.

Why use this PLS-SEM book?

The Primer is designed as an applied guide rather than a purely technical treatment. It is especially useful for researchers who want to move from conceptual model development to empirical results and publication-ready reporting. The book supports common research tasks such as:

specifying structural models and measurement models;
deciding when PLS-SEM is appropriate compared with CB-SEM or regression with sum scores;
estimating path models with the PLS-SEM algorithm;
assessing reliability, validity, collinearity, effect sizes, explanatory power, model fit, and predictive power;
analyzing mediation, moderation, control variables, higher-order constructs, heterogeneity, endogeneity, and model comparison;
reporting PLS-SEM results transparently in theses, dissertations, conference papers, and journal articles.

Learn the book topics in SmartPLS

Use these SmartPLS documentation pages together with the Primer to move from reading to application:

Brief table of contents

Preface
About the Authors
Chapter 1: An Introduction to Structural Equation Modeling
Chapter 2: Specifying the Path Model and Examining Data
Chapter 3: Path Model Estimation
Chapter 4: Assessing PLS-SEM Results — Part I: Evaluation of the Reflective Measurement Models
Chapter 5: Assessing PLS-SEM Results — Part II: Evaluation of the Formative Measurement Models
Chapter 6: Assessing PLS-SEM Results — Part III: Evaluation of the Structural Model
Chapter 7: Mediator and Moderator Analysis
Chapter 8: Outlook on Advanced Methods
Glossary
References
Index

Chapter overview

Chapter 1: An Introduction to Structural Equation Modeling

Chapter 1 introduces structural equation modeling, explains the principles of SEM, compares PLS-SEM, CB-SEM, and regressions based on sum scores, and gives guidelines for choosing between PLS-SEM and CB-SEM.

Chapter 2: Specifying the Path Model and Examining Data

Chapter 2 explains how to specify the structural model and measurement models, collect and examine data, and create the path model in SmartPLS. This chapter also introduces the corporate reputation case study used throughout the book.

Chapter 3: Path Model Estimation

Chapter 3 covers the PLS-SEM algorithm and model estimation. It shows how to estimate the path model and interpret the first estimation results.

Chapter 4: Assessing PLS-SEM Results — Part I: Evaluation of the Reflective Measurement Models

Chapter 4 focuses on reflective measurement model evaluation, including reliability and validity assessment as part of the broader Stage 5 evaluation workflow.

Chapter 5: Assessing PLS-SEM Results — Part II: Evaluation of the Formative Measurement Models

Chapter 5 explains formative measurement model evaluation, including convergent validity, collinearity, and the significance and relevance of formative indicators.

Chapter 6: Assessing PLS-SEM Results — Part III: Evaluation of the Structural Model

Chapter 6 covers structural model evaluation, including collinearity, path coefficients, explanatory power, effect sizes, predictive relevance, predictive power, and the reporting of structural model results.

Chapter 7: Mediator and Moderator Analysis

Chapter 7 explains mediation and moderation analysis, including how to model, estimate, interpret, and report indirect effects and interaction effects in PLS-SEM.

Chapter 8: Outlook on Advanced Methods

Chapter 8 introduces advanced methods such as importance-performance map analysis, necessary condition analysis, higher-order constructs, confirmatory tetrad analysis, endogeneity assessment, observed and unobserved heterogeneity, measurement invariance, and consistent PLS-SEM.

Key features

Step-by-step software guidance shows how to use SmartPLS to obtain results and prepare PLS-SEM analyses for academic reporting.
Rules of thumb provide practical guidance for applying and interpreting PLS-SEM.
Accessible explanations reduce unnecessary technical complexity and support readers who are new to structural equation modeling.
Learning outcomes organize each chapter around what readers should understand and be able to apply.
Plain-language definitions help readers understand important concepts, statistical terms, and evaluation criteria.
Applied case study material demonstrates how PLS-SEM decisions unfold across the full analysis workflow.
Coverage of current PLS-SEM extensions helps readers connect the fundamentals with newer topics such as prediction, cIPMA, Gaussian copulas, latent class approaches, and measurement invariance.

Free SmartPLS 4 case study updates for the third edition

The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) used SmartPLS 3 for the case studies. The following free materials update the third-edition case studies to SmartPLS 4. They remain useful for readers who work with the third edition or want additional SmartPLS 4 practice material:

Chapter 2 — Specifying the Path Model and Examining Data
Chapter 3 — Path Model Estimation
Chapter 4 — Evaluation of Reflective Measurement Models
Chapter 5 — Evaluation of Formative Measurement Models
Chapter 6 — Evaluation of the Structural Model
Chapter 7 — Mediation and Moderation Analysis

Introductory video

The following video introduces the Primer and its applied approach to learning PLS-SEM. Note that the fourth edition now focuses on SmartPLS 4.

The authors

Joseph F. Hair, Jr. is a leading scholar in multivariate data analysis, marketing research, and business research methods. His work has shaped how researchers apply statistical methods in business, management, and the social sciences.

G. Tomas M. Hult is Professor and Byington Endowed Chair at Michigan State University. His research and teaching focus on international business, marketing strategy, research methods, and advanced quantitative analysis.

Christian M. Ringle is Professor of Management at Hamburg University of Technology and cofounder of SmartPLS. His research focuses on management, business analytics, PLS-SEM, and the development and application of modern multivariate methods.

Marko Sarstedt is Professor of Marketing at Ludwig-Maximilians-University Munich. His research advances marketing, consumer behavior, and quantitative research methods, with a strong focus on PLS-SEM and related techniques.

Praise for earlier editions

Claes Fornell, Chairman, CFI Group Worldwide: “Partial least squares’ modeling is a very robust and practical technique to tackle many of today’s multi-equation econometric models. In many situations, researchers are interested in both prediction and causality. Since PLS aims to account for the trace (sum of the diagonal in the covariance matrix), it is well suited for prediction. This is in contrast to covariance structure models, where the objective is to account for all the observed variable covariances, which is not particularly relevant for prediction. For the American Customer Satisfaction Index, we have used our own version of PLS since the very beginning. This book, by a great author team, puts PLS more practically into the hands of researchers by creating a logical and understandable way of applying PLS-based predictions based on structural relationships. The result is that we will likely see more use of PLS in research, and significant advances to complex data problems.”

Yves Doz, Solvay Chaired Professor of Technological Innovation, INSEAD: “Partial least squares’ modeling is an important statistical technique in management research but one that is most often used by very statistically oriented academicians. The PLS book written by a great team of authors who are all very familiar with using PLS makes the technique more practically understandable. Given the type of data used in management research, this book will facilitate the confident use of PLS by a much larger number of researchers to test holistic multi-equation models.”

David Ketchen, Lowder Eminent Scholar, Auburn University: “This PLS book is concise and application-oriented while being scientifically rigorous. With the use of PLS becoming more widespread and important as a tool in the field of management, this PLS book, by a superb author team, gives more scholars the needed practical knowledge to conduct rigorous research on partial least squares modeling.”

Roger Calantone, Eli Broad Chaired University Professor of Business, Michigan State University: “Partial least squares’ modeling is a great solution technique for a variety of small and large multivariate data problems. This book provides a deeply informed, yet practical, guide to understanding and using PLS for both novice and advanced researchers. This approach to understanding PLS carries one from a preliminary overview of the technique and its application, through the many subtle, but powerful nuances of the method. After 27 years of teaching variations of SEM, I am happy to discover a book that provides a gateway for the novice and a roadmap for the expert to confidently and appropriately model and estimate with PLS in a broad range of research contexts.”

Reviews

Ketchen Jr., D. J. (2013). A Primer on Partial Least Squares Structural Equation Modeling. Long Range Planning, 46(1–2), 184–185. Read the open access review.

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2027). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (4 ed.). Sage.

Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. In SmartPLS. https://www.smartpls.com/