Multivariate Analysis with LISREL

Karl G. Jöreskog is Professor Emeritus at Uppsala University, Sweden, and Senior Professor at the BI Norwegian School of Business in Oslo. He has received three honorary doctorates: from the Faculty of Economics and Statistics at the University of Padua, Italy, 1993, from the Norwegian School of Economics, Bergen, Norway, 1996, and from the Faculty of Psychology at the Friedrich-Schiller-Universität, Jena, Germany, 2004. Professor Jöreskog is a member of the Swedish Royal Academy of Sciences, a Fellow of the American Statistical Association, and an Honorary Fellow of the Royal Statistical Society. He has received many awards including the American Psychological Association Distinguished Award for the Applications of Psychology and the Psychometric Society Award for Career Achievement to Educational Measurement. Together with Dag Sörbom he developed the LISREL computer program.Ulf H. Olsson is Professor at Department of Economics and Provost at BI Norwegian Business School in Oslo with responsibility for research and academic resources. He has worked on structural equation modeling, statistical modeling and psychometrics and published several research articles in leading statistics and psychometric journals. Dr. Olsson has also authored textbooks on statistics and mathematics. In 2003 Olsson was awarded the BI Norwegian Business School’s research prize.Fan Y. Wallentin is Professor of Statistics at Uppsala University, Sweden. She received her Ph.D. in Statistics in 1997. She is a recipient of the Arnberg Prize from the Swedish Royal Academy of Sciences. Dr. Wallentin's program of research is on the theory and applications of latent variable modeling and other types of multivariate statistical analysis, particularly their applications in the social and behavioral sciences. She has published research articles in several leading statistics and psychometrics journals. She has taught courses on Structural Equation Models in Sweden, USA, China and several European countries. She has broad experience in statistical consultation for researchers in social and behavioral sciences.

“This is book presents all options of the latest version of Lisrel in terms of concise theoretical introductions followed by applications to empirical data. … It was a pleasure to read this book because of its style of presentation in terms of many empirical examples and interesting plots.” (Peter C. M. Molenaar, Structural Equation Modeling, Vol. 26 (2), 2019)“This book consists of 10 chapters and 7 appendices covering introductory and advanced topics in multivariate analysis. The book is primarily suitable for MS and Ph.D. students inthe areas of social and behavioral sciences, economists, and researchers in the field. … this book covers a large number of introductory and advanced multivariate statistical topics suitable for students in graduate programs and researchers in social and behavioral sciences, and economics disciplines.” (Morteza Marzjarani, Technometrics, Vol. 59 (4), November, 2017)“This book presents topics in multivariate statistics in ten chapters. … audience for the book are researchers and graduate students in social sciences, public health, education, psychology, economics, and management. Because the book takes on a practical approach to multivariate statistics with numerous examples, graduate students will find an excellent source for hands-on practice. … The inclusion of numerous color plots and graphics and a very clear and personal expository language make this book a friendly companion to advanced statistics.” (Abdolvahab Khademi, Journal of Statistical Software, Vol. 78, June, 2017)

• Preface.- Getting Started.- Regression Models.- Generalized Linear Models.- Multilevel Analysis.- Principal Components (PCA).- Exploratory Factor Analysis (EFA).- Confirmatory Factor Analysis(CFA).- Structural Equation Models (SEM) with Latent Variables.- Analysis of Longitudinal Data.- Multiple Groups.- Appendix.- Basic Matrix Algebra and Statistics.- Testing Normality.- Computational Notes on Censored Regression.- Normal Scores.- Assessment of Fit.- General Statistical Theory.- Iteration Algorithms.- References.