For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.
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17 of 18 people found the following review helpful:
4.0 out of 5 stars
Another good "Green Book" statistical guide.,
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This review is from: Principal Components Analysis (Quantitative Applications in the Social Sciences) (Paperback)
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. Despite the negative comments mentioned above, the book has an equal or greater number of strong points too. Let's face it PCA is complicated. There is no way to make it appear really simple and easy to understand. This book is the kind you have to read, underline, work through examples, and review again. I suspect any book on PCA would be similar. In view of the above, the author takes interesting examples out of the social science. He develops a strong foundation in PCA. He also does a good job of showing how PCA is at the foundation of many other multivariate analysis methods. This green book series has allowed me to hang in there and keep up within an intense quantitative group of a major financial institution on the West Coast. Without them, I would have been left behind. For the record, I am an MBA type and not a quant type. So, if I can understand these books, so can you. I recommend this one book if you need to understand PCA. Just accept upfront, it is not going to be easy reading. But, it does the job of explaining PCA.
2 of 2 people found the following review helpful:
2.0 out of 5 stars
PCoA,
This review is from: Principal Components Analysis (Quantitative Applications in the Social Sciences) (Paperback)
I have used PCoA on a few occasions and was hoping this book would explain how the method actually works. This was not the case. The book assumes more knowledge on matrix algebra than I have. I read through it but learned very little that I didn't already know, and what I wanted to know wasn't there. The examples are marinally useful.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
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This review is from: Principal Components Analysis (Quantitative Applications in the Social Sciences) (Paperback)
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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Inside This Book
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First Sentence:
Principal components analysis is a statistical technique that linearly transforms an original set of variables into a substantially smaller set of uncorrelated variables that represents most of the information in the original set of variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
associated latent vectors, three variable subset, rotated factor loading matrix, smallest principal component, two variable subset, largest latent root, small principal components, crime variables, largest principal component, dimensional swarm, reduced correlation matrix, exact linear dependencies, linear discriminant function analysis, particular principal component, dimensional variable space, estimated regression parameters, latent roots, principal component loadings, many principal components, ith principal component, principal coordinate analysis, principal component scores, regression parameter estimates, linear composite, second principal component Key Phrases - Capitalized Phrases (CAPs): (learn more)
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Principal components analysis is a statistical technique that linearly transforms an original set of variables into a substantially smaller set of uncorrelated variables that represents most of the information in the original set of variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
associated latent vectors, three variable subset, rotated factor loading matrix, smallest principal component, two variable subset, largest latent root, small principal components, crime variables, largest principal component, dimensional swarm, reduced correlation matrix, exact linear dependencies, linear discriminant function analysis, particular principal component, dimensional variable space, estimated regression parameters, latent roots, principal component loadings, many principal components, ith principal component, principal coordinate analysis, principal component scores, regression parameter estimates, linear composite, second principal component Key Phrases - Capitalized Phrases (CAPs): (learn more)
Example Let New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 67
books:
See all 67 books this book cites
100
books
cite this book:
See all 100 books citing this book
- Cluster Analysis (Quantitative Applications in the Social Sciences) by Mark S. Aldenderfer in Back Matter (1), and Back Matter (2)
- Analyzing Panel Data (Quantitative Applications in the Social Sciences) by Gregory B. Markus in Back Matter
- Research Designs (Quantitative Applications in the Social Sciences) by Paul E. Spector in Back Matter
- Tests of Significance (Quantitative Applications in the Social Sciences) by Ramon E. Henkel in Back Matter
- Nonrecursive Causal Models (Quantitative Applications in the Social Sciences) by William D. Berry in Back Matter
See all 67 books this book cites
- Metric Scaling: Correspondence Analysis (Quantitative Applications in the Social Sciences) by Susan C. Weller on page 26, Back Matter (1), and Back Matter (2)
- Test Item Bias (Quantitative Applications in the Social Sciences) by Steven J. Osterlind in Back Matter (1), and Back Matter (2)
- Intelligent and Other Computational Techniques in Insurance: Theory and Applications (Series on Innovative Intelligence, 6) by A. F. Shapiro on page 100, and page 134
- Introduction to Factor Analysis: What It Is and How To Do It (Quantitative Applications in the Social Sciences) by Jae-On Kim in Back Matter (1), and Back Matter (2)
- Survey Questions: Handcrafting the Standardized Questionnaire (Quantitative Applications in the Social Sciences) by Jean M. Converse in Back Matter (1), and Back Matter (2)
See all 100 books citing this book
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