5/30/2011

Applied Multivariate Research: Design and Interpretation Review

Applied Multivariate Research: Design and Interpretation
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This is probably one of the most straightforward statistics books i have used. It got me through my MA's and now I am relying on it to finish my PhD dissertation. If you are looking for a quick answer to a review, or simply looking for a statistics book to buy, "BUY IT NOW."
I am a phD student in applied child development who does a lot of applied and educational research. This book has all the basic details and general ideas about statistics that most researchers would use, ranging from basic bivariate comparisons to advanced ideas such as factor analysis. It presents each topic in a very straightforward manner with simple terms and examples (mostly in psychology), and each chapter is accompanied by a chapter on how to implement the particular statistical procedure in SPSS. Unlike other books where they either present you with the mathematics of each statistical procedure, or books where they assumes you know what procedure to use and only present you with SPSS commands, this book presents both, though heavier on the latter.
This is particularly good for researchers like myself who need just enough information to choose which procedure to use (and not really needing to know all the mathematical proofs) and need step by step instructions to compute the statistics on SPSS (with screenshots, arrows pointing to what buttons to press, what to read, etc). However, please note that all the SPSS chapters, which include detailed screenshots, would probably make sense to people who are using SPSS 13.0 to SPSS 15.0 (12.0 would probably be fine, but I have never used anything before 12.0 so I can't comment on that). I imagine that version 16 would likely be the same, but being heavy on screenshots of a particular version of SPSS (windows version, nonetheless) means that this book would not last forever. Though, unless SPSS makes hugh interface changes in the future, anyone reading this book should be able to follow.
The biggest ++ of this book, from my perspective, is the way this book presents each example statistical problem (again, psychology heavy). It begins each concept chapter with an example research question, details why a particular statistical procedure is appropriate, tells you what to look for, assumptions underlying the statistics, basic ideas and basic algorithms of the statistics, and then what the statistics would show you. Then, the accompanying SPSS chapter follows with the SAME research problem, shows you how to find if the sample distribution satisfies assumptions in SPSS (with screenshots tell you which button to press!!!), tell you what numbers to look for (sometimes with assumption guidelines), what commands to use to run the statistical procedure(s), a detailed description of the output and what numbers to look for, and most importantly, an APA style example write up, with APA style charts and tables when appropriate.
You can tell that I LOVE THIS BOOK!~
However, I am not a statistician or a psychometrician. This book is not intended for people who are studying statistics or psychometrics. It does not provide detailed proofs, which is fine by me but many students would probably find this lacking. Further, it covers everything from data screening to MANOVA and Exploratory Factor Analysis well, but not anything beyond that (an introduction to confirmatory factor analysis using Amos is provided, but Amos, though related to SPSS, is not the most widely used CFA tool). Further, it presents only parametric data analysis techniques, which are common for most researchers. However, for my current research, I supplement this book with Pett's book on non-parametric statistics.
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Applied Multivariate Research: Design and Interpretationprovides full coverage of the wide range of multivariate topics in a conceptual rather than mathematical approach. The authors gear the text toward the needs, level of sophistication, and interest in multivariate methodology of students in applied programswho need to focus on design and interpretation rather than the intricacies of specific computations.

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