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Quantitative Applications in the Social Sciences Ser.: Principal Components Ana…

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eBay item number:313715198525
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Item specifics

Condition
Very good: A book that does not look new and has been read but is in excellent condition. No obvious ...
Subject
Social Sciences
ISBN
9780803931046

About this product

Product Identifiers

Publisher
SAGE Publications, Incorporated
ISBN-10
0803931042
ISBN-13
9780803931046
eBay Product ID (ePID)
746169

Product Key Features

Number of Pages
96 Pages
Language
English
Publication Name
Principal Components Analysis
Publication Year
1989
Subject
Research
Type
Textbook
Author
George Henry Dunteman
Subject Area
Social Science
Series
Quantitative Applications in the Social Sciences Ser.
Format
Trade Paperback

Dimensions

Item Height
0.3 in
Item Weight
4 Oz
Item Length
8.3 in
Item Width
5.4 in

Additional Product Features

Intended Audience
Scholarly & Professional
Series Volume Number
69
Illustrated
Yes
Volume Number
Vol. 69
Table Of Content
IntroductionBasic Concepts of Principal ComponentsGeometrical Properties of Principal ComponentsDecomposition Properties of Principal ComponentsPrincipal Components of Patterned Correlation MatricesRotation of Principal ComponentsUsing Principal Components to Select a Subset of VariablesPrincipal Components Versus Factor AnalysisUses of Principal Components in Regression AnalysisUsing Principal Components to Detect Outlying and Influential ObservationsUse of Principal Components in Cluster AnalysisUse of Principal Components Analysis in Conjunction with Other Multivariate Analysis ProceduresOther Techniques Related to Principal ComponentsSummary and Conclusions
Synopsis
For anyone in need of a concise, introductory guide to principle 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. In addition, the author shows how this technique can be used in tandem with other multivariate analysis techniques-such as multiple regression and discriminant analysis. Flexible in his presentation, Dunteman speaks to students at differing levels, beginning or advanced, bringing them new material that is both accessible and useful. "Two of the best attributes of the book are the prolific use of good examples--primarily social science based--and the repetition basics. . . . This book is a useful addition to the work in this area." --Issues in Researching Sexual Behavior "Most academic researchers and practitioners will benefit from the experience and practical advice in this paper. Not only does Dunteman contribute to our understanding of principal components, but he suggests several good ideas on how to make wider and better use of the technique." --Journal of Marketing Research "A concise, introductory guide with a minimal use of matrix algebra and with multiple real-life examples." --Bulletin de Methodologie Sociologique "An interesting and useful little book." --Journal of Classification, 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., The author expounds the application of this multivariate analysis technique to the social sciences. He demonstrates what it can offer the researcher in analyzing particular sets of multidimensional data. He shows how it 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; in selecting a subset of variables to represent a much larger set; and in coping with multicolinearity in regression analysis, a persistent problem in behavioural and social science data sets.

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