This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance–performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Table of Contents:
Preface 1. Introduction 1.1. The Nature of Structural Equation Modeling 1.2. What is Composite-Based SEM? 1.3. For Which Purpose Should One Use Composite-Based SEM? 1.3.1. Using Composite-Based SEM for Confirmatory Research 1.3.2. Using Composite-Based SEM for Explanatory Research 1.3.3. Using Composite-Based SEM for Exploratory Research 1.3.4. Using Composite-Based SEM for Descriptive Research 1.3.5. Using Composite-Based SEM for Predictive Research 1.3.6. When to Use Composite-Based SEM? 1.4. Software Tutorial: Getting Started 1.4.1. First Steps in ADANCO 1.4.2. First Steps in cSEM 2. Auxiliary Theories, with Florian Schuberth 2.1. The Need for Auxiliary Theories 2.2. Different Types of Science 2.3. TheAuxiliaryTheory of Behavioral Science: MeasurementTheory 2.4. The Auxiliary Theory of Design Science: Synthesis Theory 3. Model Specification 3.1. What Is A Structural Equation Model? 3.2. The Outer Model 3.2.1. Composite Models 3.2.2. Reflective Measurement Models 3.2.3. Causal-Formative Measurement Models 3.2.4. Single-Indicator Measurement Models 3.2.5. Categorical Variables 3.3. The Inner Model 3.4. Software Tutorial: Model Specification 3.4.1. Specifying Structural Equation Models in ADANCO 3.4.2. Specifying Structural Equation Models in cSEM 4. Model Identification 4.1. The Necessity of Identification 4.2. Ensuring Model Identification in Composite-Based SEM 4.3. Ensuring Empirical Identification in Composite-Based SEM 4.4 ‘Chance Correlations’ 4.4.1. The Problem with ‘Chance Correlations’ 4.4.2. Avoiding ‘Chance Correlations’ 4.5. The Dominant Indicator Approach As a Solution to Sign Indeterminacy 4.6. Identification Rules 5. Model Estimation 5.1. Composite-Based Estimators for Composite Models 5.1.1. Stand-Alone Constructions: Sum Scores, Preset Weights, and Principal Components 5.1.2. The Partial Least Squares Path Modeling Algorithm 5.1.3. Generalized Structured Component Analysis 5.2. Composite-Based Estimators for Reflective Models 5.2.1. Consistent Partial Least Squares 5.2.2. Sum Scores with Correction for Attenuation 5.3. Fitting Functions 5.4. Tutorial: Model Estimation 5.4.1. Estimating Models Using ADANCO 5.4.2. Estimating Models Using cSEM 6. Global Model Assessment: Model Fit 6.1. The Motivation for Model Fit 6.2. Model Fit Tests 6.2.1. Non-Parametric Model Fit Tests 6.2.2. Parametric Model Fit Tests 6.3. Model Fit Indices 6.3.1. Standardized Root Mean Squared Residual (SRMR) 6.3.2. Root Mean Square Residual Covariance (RMS?) 6.3.3. Fit Measures Provided by Covariance-based SEM 6.4. What If Model Fit Is Low? 6.5. Beware of Alleged Goodness of Fit Indices 6.5.1. Four “Goodness of Fit Indices” That Are Not Model Fit Indices 6.5.2. The Different Meanings of Fit 6.6. Tutorial: Model Testing 6.6.1. Using ADANCO for Model Testing 6.6.2. Using cSEM for Model Testing 7. Local Model Assessment 7.1. The Need for Reliability and Validity 7.2. Assessing Composite Models of Emergent Variables 7.2.1. Nomological Validity 7.2.2. The Reliability of Composites 7.2.3. Weights 7.3. Assessing Reflective Measurement Models of Latent Variables 7.3.1. Construct Validity 7.3.2. Unidimensionality 7.3.3. Discriminant Validity 7.3.4. Reliability of Construct Scores 7.4. Assessing Causal-Formative Measurement Models 7.5. Assessing Inner Models 7.5.1. R2 and Adjusted R2 7.5.2. Inter-Construct Correlations 7.5.3. Path Coefficients 7.5.4. Indirect Effects 7.5.5. Total Effects 7.5.6. Effect Size (Cohen’s f 2) 7.6. Inferential Statistics and the Bootstrap 7.7. Construct Scores 7.8. What If There Is No Output? 7.9. Tutorial: Model Assessment 7.9.1. Model Assessment Using ADANCO 7.9.2. Model Assessment Using cSEM 8. Confirmatory Composite Analysis, with Florian Schuberth 8.1. Motivation 8.2. Confirmatory Composite Analysis: Model Specification 8.3. Confirmatory Composite Analysis: Model Identification 8.4. Confirmatory Composite Analysis: Model Estimation 8.5. Confirmatory Composite Analysis: Model Testing 8.6. Tutorial: Confirmatory Composite Analysis 8.6.1. Confirmatory Composite Analysis Using ADANCO 8.6.2. Confirmatory Composite Analysis Using cSEM 9. Mediation Analysis 9.1. The Logic of Mediation 9.2. Mediation Analysis Using Composite-based SEM 9.3. Tutorial: Mediation Analysis 9.3.1. Mediation Analysis Using ADANCO 9.3.2. Mediation Analysis Using cSEM 10. Second-Order Constructs 10.1. A Typology of Second-Order Constructs and Their Use 10.2. Modeling Type-I Second-Order Constructs: LatentVariablesMeasured by Latent Variables 10.3. Modeling Type-II Second-Order Constructs: Emergent Variables Made of Latent Variables 10.4. Modeling Type-III Second-Order Constructs: Latent Variables Measured by Emergent Variables 10.5. Modeling Type-IV Second-Order Constructs: Emergent Variables Made of Emergent Variables 10.6. Modeling Type-V Second-Order Constructs: Latent Variables Measured by Different Types of Variables 10.7. Modeling Type-VI Second-Order Constructs: Emergent Variables Made of Different Types of Variables 10.8. Tutorial: Second-Order Constructs 10.8.1. Modeling Second-Order Constructs with ADANCO 10.8.2. Modeling Second-Order Constructs with cSEM 11. Analyzing Interaction Effects 11.1. The Logic of Interaction Effects 11.2. Estimating Interaction Effects with Composite-Based SEM 11.2.1. Multigroup Analysis 11.2.2. The Two-Stage Approach for Analyzing Interaction Effects 11.2.3. The Orthogonalizing Approach for Analyzing Interaction Effects 11.3. Visualizing Interaction Effects 11.3.1. Surface Analysis 11.3.2. Spotlight Analysis 11.3.3. Floodlight Analysis 11.4. Three-way Interactions 11.5. Nonlinear Effects 11.6. Tutorial: Interaction Effects 11.6.1. Analyzing Interaction Effects Using ADANCO 11.6.2. Analyzing Interaction Effects Using cSEM 12. Importance–Performance Analysis 12.1. Nature and Fields of Application 12.2. A Step-by-Step Guide to Conducting IPA Using Composite-Based SEM 12.3. Tutorial: Importance–Performance Analysis 12.3.1. Using ADANCO for Importance–Performance Analysis 12.3.2. Using cSEM for Importance–Performance Analysis References Author Index Subject Index Acronyms Glossary About the Author Disclosure