Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming (Multivariate Applications Series)

Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming (Multivariate Applications Series)

• Used Book in Good Condition

<P>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<EM>plus</EM> 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:</P> <UL> <LI>an explanation of the issues addressed</LI> <LI>illustrated and annotated testing of the hypothesized and post hoc models</LI> <LI>explanation and interpretation of all M<EM>plus</EM> input and output files</LI> <LI>important caveats pertinent to the SEM application under study</LI> <LI>a description of the data and reference upon which the model was based</LI> <LI>the corresponding data and syntax files available at http://www.psypress.com/sem-with-mplus/datasets .</LI></UL> <P>The first two chapters introduce the fundamental concepts of SEM and important basics of the M<EM>plus</EM> 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.</P> <P>Intended for researchers, practitioners, and students who use SEM and M<EM>plus</EM>, 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<EM>plus</EM>, a prerequisite of basic statistics through regression analysis is recommended.</P>