Confirmatory Factor Analysis for Applied Research (Methodology in the Social Sciences) - Tapa blanda

'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 - are 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 clears figures and tables. I definitely recommend it to instructors who teach a course on CFA, especially for students who are not in quantitative psychology.' - Ke-Hai Yuan, Department of Psychology, University of Notre Dame

Emphasizing practical and conceptual aspects of confirmatory factor analysis (CFA) over mathematics and formulas, Brown uses rich examples derived from the psychology, management, and sociology literatures to provide in-depth treatment of the concepts, procedures, pitfalls, and extensions of CFA methodology. Chock-full of useful advice, and including tables that recap the steps of procedures, the text shows readers how to conduct exploratory factor analysis (EFA) and understand similarities and differences to CFA; formulate, program, and interpret CFA models using popular latent variable software packages such as LISREL, Mplus, Amos, EQS, and SAS/CALIS; and report results from a CFA study. Also covered are extensions of CFA to traditional IRT analysis, methods for determining needed sample sizes, and new CFA modeling possibilities (e.g., multilevel factor models, factor mixture models). Special features include a Web page offering data and program syntax files for many of the research examples, as well as links to CFA-related resources.