Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling

by Timothy Z. Keith

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely.





Covers both MR and SEM, while explaining their relevance to one another

Also includes path analysis, confirmatory factor analysis, and latent growth modeling

Figures and tables throughout provide examples and illustrate key concepts and techniques
For additional resources, please visit: http: //tzkeith.com/

Genres: Textbooks, Psychology, Nonfiction

604 pages, Paperback

First published July 4, 2005

Reviews

[Veronica · Rating 5 out of 5]

I owe this book my entry into the stats world. I really appreciated authors who have a way of using plain language to explain complex content, without dumbing down the level of content being delivered. I've attended a couple of intro stats lectures before reading the book and found that the book really gives you the kind of foundational understanding you need for proceeding into more complicated stuff.

[Shivantha Perera]

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[Steven Peterson · Rating 5 out of 5]

This is a somewhat exotic book for most folks at Goodreads for me to review here. However, for those interested in multivariate social science statistics, this is an excellent volume. The book introduces readers to regression, factor analysis, and structural equation modeling. It is accessible, but does not "dumb down" discussion of the techniques.

For example, with structural equation modeling (SEM), one wants to know how well the model does in explaining the actual data. Keith does a nice job in laying out what the researcher would look for in making sense of the results--and assessing how well the model does. The author's discussion of SEM in this book and in chapters/articles published elsewhere is a Godsend to those of us who see the value of the technique and need some guidance in its use.

This would be a nice volume for advanced statistics classes and is well worth considering as a textbook in such classes. For those who use statistics in research, this is also very useful.

[Rachel · Rating 3 out of 5]

Pretty engaging, as far as statistics textbooks go, though I won't be rushing back to reread this one any time soon. I like the author's way of writing, he actually tries to make a book on regression semi entertaining, and very nearly succeeds :)

[Linda · Rating 2 out of 5]

Slightly confusing and somewhat dated, but overall not terrible by means of explaining the reasoning behind specific statistical methods.

[Jaclyn · Rating 4 out of 5]

Great, easy to understand SEM book. However, highly recommend buying the paper copy as there are a ton of diagrams and outputs you will want to compare across pages.