Confirmatory factor analysis for applied research

1 Confirmatory factor analysis for applied ResearchMethodology in the Social SciencesDavid A. Kenny, Series EditorSPECTRAL analysis OF TIME-SERIES DATAR ebecca M. WarnerA PRIMER ON REGRESSION ARTIFACTSD onald T. Campbell and David A. KennyREGRESSION analysis FOR CATEGORICAL MODERATORSH erman AguinisHOW TO CONDUCT BEHAVIORAL research OVER THE INTERNET:A BEGINNER S GUIDE TO HTML AND CGI/PERLR. Chris FraleyPRINCIPLES AND PRACTICE OF STRUCTURAL EQUATION MODELINGS econd EditionRex B. KlineCONFIRMATORY factor analysis FOR applied RESEARCHT imothy A. BrownConfirmatory FactorAnalysis forApplied ResearchTimothy A.

2 BrownSERIES EDITOR S NOTE byDavid A. KennyTHE GUILFORD PRESSNew York London 2006 The Guilford PressA Division of Guilford Publications, Spring Street, New York, NY rights reservedNo part of this book may be reproduced, translated, stored in aretrieval system, or transmitted, in any form or by any means,electronic, mechanical, photocopying, microfilming, recording,or otherwise, without written permission from the in the United States of AmericaThis book is printed on acid-free digit is print number:987654321 Library of Congress Cataloging-in-Publication DataBrown, Timothy factor analysis for applied research /Timothy A.

3 Cm. (Methodology in the social sciences)Includes bibliographical references and : 978-1-59385-274-0 (pbk.)ISBN-10: 1-59385-274-6 (pbk.)ISBN-13: 978-1-59385-275-7 (hardcover)ISBN-10: 1-59385-275-4 (hardcover)1. factor analysis . I. Title. II. 5195354 dc222006001103 For my father, Kaye,and Nick and GregAbout the AuthorTimothy A. Brown, PsyD,is a professor in the Department of Psychologyat Boston University, and Director of research at Boston University sCenter for Anxiety and Related Disorders. He has published extensively inthe areas of the classification of anxiety and mood disorders, vulnerabilityto emotional disorders, psychometrics, and methodological advances insocial sciences research .

4 In addition to conducting his own grant-sup-ported research , Dr. Brown serves as a statistical investigator or consultanton numerous federally funded research projects. He has been on the edito-rial boards of several scientific journals, including recent appointments asAssociate Editor of theJournal of Abnormal Editor s NoteSeries Editor s NoteSeries Editor s NoteFor some reason, the topic of Confirmatory factor analysis (CFA) has notreceived the attention that it deserves. Two closely related topics, explor-atory factor analysis (EFA) and structural equation modeling (SEM), havedozens of textbooks written about them.

5 Book-length treatments of CFAare rare and that is what makes this book might think that there are so few books on CFA because it is sorarely used. However, this is not the case. Very often, those who conductEFA follow up the analysis with CFA. Additionally, SEM always involves ameasurement model and very often the best way to test that model is withCFA. Poor-fitting structural equation models are almost always due to CFAproblems. Thus, to be proficient at SEM, the analyst must know CFA. Thisbook very nicely explains the links between CFA and these two differentmethods, in particular the natural process of beginning with EFA, proceed-ing to CFA, and then think it is ironic that SEM has received so much more attention thanCFA, because the social and behavioral sciences have learned much morefrom CFA than from SEM.

6 In particular, through CFA we are able tounderstand the construct validity of attitudes and personality, and CFAprovides important information about the relative stability of individualdifferences throughout the most books on factor analysis , this one spares us all the matri-ces with their transposes, Kronecker products, and inverses. Certainlymatrix algebra is critical in the theory, proofs, and estimation of CFA, butfor day-to-day practitioners, it just gets in the way. This is not to say thatthe author, Timothy A. Brown, doesn t discuss technical issues where nec-essary.

7 The text is complicated where example of one such complicated topic is the multitrait multimethod matrix, first proposed by Donald Campbell and DonaldFiske. I am pleased that Brown decided to devote a full chapter to thetopic. Interestingly, a generation of researchers tried to find EFA modelsfor the matrix and never developed a completely satisfactory generation of researchers worked on several CFA models for thematrix, and Brown very nicely summarizes the models they useful feature of this book is that it contains an entire chapterdevoted to issues of statistical power and sample sizes.

8 Investigators needto make decisions, costly both in terms of time and money, about samplesize. Very often they make those decisions using rather arbitrary proce-dures. The book outlines a formal and practical approach to that breadth of applications, the book provides examples from severaldifferent areas of the social and behavioral sciences. It also illustrates theanalyses using several different software programs. Preferences for com-puter programs change as fast as preferences do for hair styles; thus, it isan advantage that the book is not tied to one computer program.

9 Mostreaders would benefit from analyzing data of their own as they read validity, instrument development and validation, reductionof the number of variables, and sources of bias in measurement, to namejust a few, are subjects supported by high-quality CFA. Almost all researchdata include many variables; therefore, Brown s detailed and careful treat-ment of this important topic will be of benefit in almost all research situa-tions. A gap in the field of multivariate data analysis that has existed for fartoo long has finally been filled. Researchers now have a readable, detailed,and practical discussion of KENNYxSeries Editor s NotePrefacePrefacePrefaceThis book was written for the simple reason that no other book of its kindhad been published before.

10 Although many books on structural equationmodeling (SEM) exist, this is the first book devoted solely to the topic ofconfirmatory factor analysis (CFA). Accordingly, for the first time, manyimportant topics are brought under one cover for example, the similari-ties/differences between exploratory factor analysis (EFA) and CFA, theuse of maximum likelihood EFA procedures as a precursor to CFA, diag-nosing and rectifying the various sources for the ill-fit of a measurementmodel, analysis of mean structures, modeling with multiple groups ( ,MIMIC), CFA scale reliability evaluation, formative indicator models, andhigher-order factor analysis .