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Books > Science & Math > Mathematics > Applied > Probability & Statistics > B004FPZ2B8
  1. Principal Components Analysis (Quantitative Applications in the Social Sciences)
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  2. Principal Components Analysis (Quantitative Applications in the Social Sciences)

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    Customer Ratings (5 reviews)
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Additional Information

Principal components analysis offers researchers a "feel" for analysing particular sets of multidimensional data. A multivariate analysis technique applied to a wide variety of settings such as medicine and chemistry as well as the social sciences, principal components analysis can be used to determine the number of factors to be retained in a factor analysis; for extracting the initial factors in a factor analysis; and in selecting a subset of variables to represent a much larger set. It is particularly useful in coping with multicolinearity in regression analysis, a persistent problem in behavioral and social science data sets.

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George H. Dunteman
Kindle Edition
Kindle eBook
SAGE Publications, Inc
SAGE Publications, Inc
SAGE Publications, Inc
SAGE Publications, Inc
Most Helpful Customer Reviews

What's good: Dunteman offers a helpful discussion of the possible inputs to PCA, esp. correlation vs. covariance matrices. He also presents useful hints for
- deciding how many principal components (PCs) to use,
- interpreting the linear combinations of inputs that produce the PCs,
- contrasting the meanings of second and higher PCs to the first, and
- relating PCs to other analyses, like factor analysis or simple variable elimination.
In short, he helps the reader select inputs, understand outputs, and relate results to alternative analyses - all good stuff.

What's not: I never did see a clear algorithm for extracting PCs. The author indicates that "latent vectors" (PCs) and "latent roots" are really eigenvectors and eigenvalues, for which algorithms are well understood. This vagueness seemed to pervade every discussion of some derived value or other. Either the algorithm was missing from the discussion, or was implicit in some expression too terse... Read more
I am a big fan of this little "green book" statistical series. Thanks to it, I already taught myself Logit Regression, Cluster Analysis, Discriminant Analysis, Factor Analysis, and Correspondence Analysis. Most of these were excellent; "Principal Component Analysis" (PCA) was good.

The reasons I don't consider it excellent like some of the others are: First, the terminology is kind of dated and confusing. The author talks about of Latent Roots and Latent vectors when the more common names nowadays are Eigenvalues and Eigenvectors. Also, the author mentions in the introduction, he will explain most concepts without relying on Matrix Algebra. Yet, he does to a great extent. If you are not familiar with Matrix Algebra, you will be forced to learn it to better understand this book. Finally, the author gives you many formulas that are sometimes difficult to understand, especially when he rarely fleshes out the related calculations in a concrete example... Read more
I found it interesting to be given the option to "upgrade" this item after I purchased it. I promptly did so, to test out this feature, and was greeted with the "search inside this book" functionality, but with full access to the book. This let me get a jump start reading though the book.

This book is very clear for an academic paper and provided a good jumping off point to review my rusty linear algebra. The technique it describes is great for distilling data with high dimensionality and low correlation (a tag cloud for instance) into a smaller set of highly correlated variables (such as could be mapped to a plane for a visual representation).

Like most reference books, initially I skimmed through this and now have it close at hand to aid in the project(s) that inspired it's purchase.
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