Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming
ISBN: 1848728395
EAN13: 9781848728394
Language: English
Pages: 432
Dimensions: 1.00" H x 9.00" L x 6.00" W
Weight: 1.00 lbs.
Format: Paperback
Publisher:
Book Overview
Modeled after Barbara Byrne's other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using M plus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author walks the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models explanation and interpretation of all M plus input and output files important caveats pertinent to the SEM application under study a description of the data and reference upon which the model was based the corresponding data and syntax files available at http: //www.psypress.com/sem-with-mplus/datasets . The first two chapters introduce the fundamental concepts of SEM and important basics of the M plus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. Intended for researchers, practitioners, and students who use SEM and M plus , this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or M plus , a prerequisite of basic statistics through regression analysis is recommended.