in Regression Kaufman173 An Introduction to Exponential Random Graph Modeling Harris
174 Introduction to Time Series Analysis Pickup
175 Factorial Survey Experiments Auspurg/Hinz
176 Introduction to Power Analysis: Two-Group Studies Hedberg
177 Linear Regression: A Mathematical Introduction Gujarati
178 Propensity Score Methods and Applications Bai/Clark
179 Multilevel Structural Equation Modeling Silva/Bosancianu/Littvay
180 Gathering Social Network Data adams
181 Generalized Linear Models for Bounded and Limited Quantitative Variables, Smithson and Shou
182 Exploratory Factor Analysis, Finch
Sara Miller McCune founded SAGE Publishing in 1965 to support the dissemination of usable knowledge and educate a global community. SAGE publishes more than 1000 journals and over 800 new books each year, spanning a wide range of subject areas. Our growing selection of library products includes archives, data, case studies and video. SAGE remains majority owned by our founder and after her lifetime will become owned by a charitable trust that secures the company’s continued independence.
Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne
Exploratory Factor Analysis
W. Holmes Finch
Ball State University
Los Angeles
London
New Delhi
Singapore
Washington DC
Melbourne
Copyright © 2020 by SAGE Publications, Inc.
All rights reserved. Except as permitted by U.S. copyright law, no part of this work may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without permission in writing from the publisher.
All third party trademarks referenced or depicted herein are included solely for the purpose of illustration and are the property of their respective owners. Reference to these trademarks in no way indicates any relationship with, or endorsement by, the trademark owner.
FOR INFORMATION:
SAGE Publications, Inc.
2455 Teller Road
Thousand Oaks, California 91320
E-mail: [email protected]
SAGE Publications Ltd.
1 Oliver’s Yard
55 City Road
London EC1Y 1SP
United Kingdom
SAGE Publications India Pvt. Ltd.
B 1/I 1 Mohan Cooperative Industrial Area
Mathura Road, New Delhi 110 044
India
SAGE Publications Asia-Pacific Pte. Ltd.
18 Cross Street #10-10/11/12
China Square Central
Singapore 048423
ISBN: 9781544339887
Printed in the United States of America
This book is printed on acid-free paper.
Acquisitions Editor: Helen Salmon
Associate Editor: Chelsea Neve
Editorial Assistant: Megan O’Heffernan
Production Editor: Jyothi Sriram
Copy Editor: Diane DiMura
Typesetter: Hurix Digital
Proofreader: Sarah J. Duffy
Indexer: Amy Murphy
Cover Designer: Ginkhan Siam
Marketing Manager: Shari Countryman
Series Editor’s Introduction
Social and behavioral research rests fundamentally on measurement. Much of what we wish to study cannot be directly observed. Exploratory Factor Analysis describes tools for exploring relationships between observed variables and latent variables, also known as factors.
The volume provides an accessible introduction to applied exploratory factor analysis (EFA) for those new to the method. Professor Finch’s goal is to give readers sufficient background to embark on their own analyses, follow up on topics of interest, and more broadly engage the literature. In doing so, he draws on his experience teaching this material as well as his expertise as a contributor to the literature. Exploratory Factor Analysis is thorough, but understandable. What the reader needs to know is explained “just in time.” Forty years ago, in the early years of the QASS series, Jae-On Kim and Charles W. Mueller contributed two volumes on factor analysis, one introducing the method and the other explaining its application. This volume replaces them both.
Professor Finch begins with problems of measurement, explaining that variables of interest are often latent constructs, not directly observable, and discussing the implications. After a quick review of the mathematical underpinnings of factor analysis, Chapters 3–5 discuss factor extraction, rotation, and retention in that order. Professor Finch walks through the requirements, assumptions, and logic of each of these steps in an EFA. Throughout, he gives much practical advice about how to implement them and about the many choices an analyst must make. For factor extraction, should the analyst use ML (maximum likelihood), PAF (principal axis factoring), or some other method? What are the differences? What is the point of factor rotation? Which is better for a particular purpose, orthogonal or oblique rotation, and given the choice, how does the analyst choose among the many rotation methods available? Which method for determining the number of factors has the most support in the literature? And importantly, for results to be reproducible, how should the analysis be described and results be presented? In the final chapter, Professor Finch discusses factor scores and the problems that can arise using them, determining appropriate sample size, and how to handle missing data as well as introducing some more advanced topics. A strength of the volume is that it clearly differentiates EFA not only from Confirmatory
