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ksiazka tytuł: Structural Equation Modeling with Mplus autor: Byrne Barbara M.
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Structural Equation Modeling with Mplus

Basic Concepts, Applications, and Programming
Wersja papierowa
Wydawnictwo: Taylor & Francis Ltd
ISBN: 978-18-487-2839-4
Format: 15.2x22.9cm
Liczba stron: 432
Oprawa: Miękka
Wydanie: 2011 r.
Język: angielski

Dostępność: dostępny
400,90 zł

<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 '&nbsp;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 under "Supplementary Material" below</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>

 

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