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Confirmatory Factor Analysis for Applied Research
Timothy A. Brown
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"For each chapter, Brown provides a comprehensive review of the topic by providing a clear commentary on the issues. He enhances this commentary with equations where necessary, illustrations, and numerical examples....The book delivers on its promise. It provides a comprehensive review of CFA techniques as a collection of essential tools that serve the contemporary researcher....The statistically sophisticated reader is provided with a current review of the appropriate use of these techniques....It could be used in an upper level graduate methodology course or by the active researcher who wishes to expand his or her repertoire of empirical techniques."
-APA PsycCRITIQUES
"This user-friendly guide to confirmatory factor analysis provides information and examples to help students and researchers through most any methodological situation. An abundance of practical advice on options for analyses, data issues, and reading and interpreting the data output make this a useful guide for almost anyone. Graduate students will find this an especially worthwhile addition to their statistical education and it would be a valuable course textbook for professors...4 stars!"
-Doody's Review Service
"I confidently recommend this book to any colleague teaching a course in confirmatory factor analysis (CFA), structural equation modeling, or scale development. The text will also be an invaluable resource for applied researchers, due to the quantity and quality of the information it contains. Included are very clear explanations of the 'thick' technical terminology and excellent elaboration on the shortcomings of the previously published CFA research within the past 10-15 years. Other strengths are clearly written chapters on higher order analyses and multitrait-multimethod models; clear coverage of conducting CFA with missing data and conducting reliability analysis within the general framework of SEM, providing a very defensible and unified approach to the issues of reliability and validity during the process of scale/instrument development; and strategies for data screening and dealing with nonnormally distributed data."
-Larry Price, Doctoral Program in Education, Texas State University-San Marcos
"I found the author's coverage of confirmatory factor analysis (CFA) both clear and accurate. I thought the explanations were pitched at the right level of mathematical and statistical complexity for the intended audience. In fact, the coverage of certain topics—such as the problem of empirical under-identification and the computation of determinants and their functional significance in the assessment of global model fit—is among the best I've read. This is a book that students will be glad to have on their shelves when they turn to their own data later on."
-Christopher M. Federico, Department of Psychology and Department of Political Science, University of Minnesota
"Compared to other books, this book offers a lot of details which would facilitate better understanding of confirmatory factor analysis (CFA). The author is very good at explaining a lot of processes by examples, and includes clear figures and tables."
-Ke-Hai Yuan, Department of Psychology, University of Notre Dame
-APA PsycCRITIQUES
"This user-friendly guide to confirmatory factor analysis provides information and examples to help students and researchers through most any methodological situation. An abundance of practical advice on options for analyses, data issues, and reading and interpreting the data output make this a useful guide for almost anyone. Graduate students will find this an especially worthwhile addition to their statistical education and it would be a valuable course textbook for professors...4 stars!"
-Doody's Review Service
"I confidently recommend this book to any colleague teaching a course in confirmatory factor analysis (CFA), structural equation modeling, or scale development. The text will also be an invaluable resource for applied researchers, due to the quantity and quality of the information it contains. Included are very clear explanations of the 'thick' technical terminology and excellent elaboration on the shortcomings of the previously published CFA research within the past 10-15 years. Other strengths are clearly written chapters on higher order analyses and multitrait-multimethod models; clear coverage of conducting CFA with missing data and conducting reliability analysis within the general framework of SEM, providing a very defensible and unified approach to the issues of reliability and validity during the process of scale/instrument development; and strategies for data screening and dealing with nonnormally distributed data."
-Larry Price, Doctoral Program in Education, Texas State University-San Marcos
"I found the author's coverage of confirmatory factor analysis (CFA) both clear and accurate. I thought the explanations were pitched at the right level of mathematical and statistical complexity for the intended audience. In fact, the coverage of certain topics—such as the problem of empirical under-identification and the computation of determinants and their functional significance in the assessment of global model fit—is among the best I've read. This is a book that students will be glad to have on their shelves when they turn to their own data later on."
-Christopher M. Federico, Department of Psychology and Department of Political Science, University of Minnesota
"Compared to other books, this book offers a lot of details which would facilitate better understanding of confirmatory factor analysis (CFA). The author is very good at explaining a lot of processes by examples, and includes clear figures and tables."
-Ke-Hai Yuan, Department of Psychology, University of Notre Dame
