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A Primer on Partial Least Squares Structural Equation Modeling 1st Edition by Josephb F Hair, G Tomas M Hult, Christian M Ringle, Marko Sarstedt ISBN 1452217440 9781452217444

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A Primer on Partial Least Squares Structural Equation Modeling PLS SEM 1st Edition Josephb F. Hair Digital Instant Download

Author(s): Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt
ISBN(s): 9781452217444, 1452217440
Edition: 1st
File Details: PDF, 16.62 MB
Year: 2013
Language: english
SKU: EB-4725082 Category: Tags: , , ,

A Primer on Partial Least Squares Structural Equation Modeling 1st Edition by Josephb F Hair, G Tomas M Hult, Christian M Ringle, Marko Sarstedt – Ebook PDF Instant Download/Delivery: 1452217440, 9781452217444
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Product details:

ISBN 10: 1452217440 
ISBN 13: 9781452217444
Author: Josephb F Hair, G Tomas M Hult, Christian M Ringle, Marko Sarstedt

A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM′s distinctive methodological features make it a viable alternative to the more popular covariance-based SEM approach. This text―the only comprehensive book available to explain the fundamental aspects of the method―includes extensive examples on SmartPLS software, and is accompanied by multiple data sets that are available for download from the accompanying website.

A Primer on Partial Least Squares Structural Equation Modeling 1st Table of contents:

Chapter 1: Introduction to Structural Equation Modeling (SEM)

  • What is SEM?

  • Covariance-Based vs. Variance-Based SEM

  • When to Use PLS-SEM

  • Common Applications Across Disciplines

Chapter 2: Fundamentals of PLS-SEM

  • The PLS-SEM Algorithm

  • Model Components: Measurement vs. Structural Models

  • Reflective and Formative Measurement Models

  • Assumptions and Limitations

Chapter 3: Specifying the PLS-SEM Model

  • Developing the Conceptual Framework

  • Defining Constructs and Indicators

  • Drawing the Path Diagram

  • Setting Up in Software (e.g., SmartPLS, ADANCO, R)

Chapter 4: Data Considerations and Preparation

  • Sample Size Requirements

  • Data Screening and Cleaning

  • Handling Missing Data and Outliers

  • Assessing Multicollinearity

Chapter 5: Model Estimation and Bootstrapping

  • Running the PLS Algorithm

  • Understanding Outer Weights, Loadings, and Path Coefficients

  • Bootstrapping: Assessing Significance and Confidence Intervals

Chapter 6: Assessing the Measurement Model

  • Evaluating Reflective Models: Reliability and Validity

  • Cronbach’s Alpha, Composite Reliability, AVE

  • Discriminant Validity: Fornell-Larcker and HTMT

  • Assessing Formative Models

Chapter 7: Assessing the Structural Model

  • Evaluating Path Coefficients

  • Coefficient of Determination (R²), Effect Size (f²), and Predictive Relevance (Q²)

  • Model Fit in PLS-SEM

  • Mediation and Moderation Analysis

Chapter 8: Advanced Topics in PLS-SEM

  • Multi-Group Analysis (MGA)

  • Higher-Order Constructs

  • Importance-Performance Map Analysis (IPMA)

  • Predictive Modeling with PLSpredict

Chapter 9: Reporting PLS-SEM Results

  • Best Practices in Writing Results

  • Tables, Figures, and Interpretation

  • Reporting According to Journal Standards

  • Common Pitfalls to Avoid

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Tags: Josephb F Hair, G Tomas M Hult, Christian M Ringle, Marko Sarstedt, Partial Least, Squares Structural