Niels J. Blunch

Introduction to Structural Equation Modeling Using IBM SPSS Statistics and EQS (eBook, PDF)

• Format: PDF

Produktbeschreibung

This student orientated guide to structural equation modeling promotes theoretical understanding and inspires students with the confidence to successfully apply SEM. Assuming no previous experience, and a minimum of mathematical knowledge, this is an invaluable companion for students taking introductory SEM courses in any discipline.

Niels Blunch shines a light on each step of the structural equation modeling process, providing a detailed introduction to SPSS and EQS with a focus on EQS' excellent graphical interface. He also sets out best practice for data entry and programming, and uses real life data to show how SEM is applied in research.

The book includes:

• Learning objectives, key concepts and questions for further discussion in each chapter.
• Helpful diagrams and screenshots to expand on concepts covered in the texts.
• A wide variety of examples from multiple disciplines and real world contexts.
• Exercises for each chapter on an accompanying .
• A detailed glossary.

Clear, engaging and built around key software, this is an ideal introduction for anyone new to SEM.

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Produktdetails

• Verlag: SAGE Publications
• Seitenzahl: 360
• Erscheinungstermin: 2. November 2015
• Englisch
• ISBN-13: 9781473943308
• Artikelnr.: 44045525

Autorenporträt

Niels J. Blunch was formerly in the Department of Marketing and Statistics at the University of Aarhus, Denmark.

Inhaltsangabe

Preface
PART 1: PREPARING YOURSELF AND YOUR DATA
Chapter 1: Introduction
Chapter 2: Measuring Your Variables: Reliability and Validity
Chapter 3: Factor Analysis
PART 2: THE THREE BASIC MODELS
Chapter 4: Structural Equation Modeling with EQS
Chapter 5: Data-entering and programming in EQS
Chapter 6: Models with Only Manifest Variables
Chapter 7: The Measurement Model in SEM: Confirmatory Factor Analysis
Chapter 8: The General Model
PART 3: ADVANCED MODELS AND TECHNIQUES
Chapter 9: Mean Structures and Multi-group Analysis
Chapter 10: Incomplete and Non-normal Data
Chapter 11: Latent Curve Models
Appendix A: Statistical Prerequisites
Appendix B: Glossary
Appendix C: EQS Statements