Picture 1 of 2
Stock photo
Picture 1 of 2
Stock photo
Quantitative Applications in the Social Sciences Ser.: Metric Scaling : Correspondence Analysis by Susan C. Weller and A. Kimball Romney (1990, Trade Paperback)
Better World Books West (354273)
98.7% positive feedback
Price:
US $6.45
ApproximatelyRM 27.59
+ $18.43 shipping
Returns:
30 days return. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
Condition:
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-100803937504
ISBN-139780803937505
eBay Product ID (ePID)1897903
Product Key Features
Number of Pages96 Pages
Publication NameMetric Scaling : Correspondence Analysis
LanguageEnglish
SubjectResearch, Statistics
Publication Year1990
TypeTextbook
AuthorSusan C. Weller, A. Kimball Romney
Subject AreaSocial Science, Psychology
SeriesQuantitative Applications in the Social Sciences Ser.
FormatTrade Paperback
Dimensions
Item Weight5 Oz
Item Length8.5 in
Item Width5.5 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN90-008731
Dewey Edition20
Series Volume Number75
IllustratedYes
Volume NumberVol. 75
Dewey Decimal300/.72
Table Of ContentIntroduction The Basic Structure of a Data Matrix Principal Components Analysis Multidimensional Preference Scaling Correspondence Analysis of Contingency Tables Correspondence Analysis of Non-Frequency Data Ordination, Seriation, and Guttman Scaling Multiple Correspondence Analysis
SynopsisPresents a set of closely related techniques that facilitate the exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling, correspondence analysis, principal components analysis, and multiple dimensional preference scaling are explored in detail for strengths and weaknesses over a wide range of data types and research situations. "The introduction illustrates the methods with a small dataset. This approach is effective--in a few minutes, with no mathematical requirement, the reader can understand the capabilities, similarities, and differences of the methods. . . . Numerical examples facilitate learning. The authors use several examples with small datasets that illustrate very well the links and the differences between the methods. . . . we find this text very good and recommend it for graduate students and social science researchers, especially those who are interested in applying some of these methods and in knowing the relationship among them." --Journal of Marketing Research "Illustrate[s] the service Sage provides by making high-quality works on research methods available at modest prices. . . . The authors use several interesting examples of practical applications on data sets, ranging from contraception preferences, to pottery shards from archeological digs, to durable consumer goods from market research. These examples indicate the broad range of possible applications of the method to social science data." --Contemporary Sociology "The book is a bargain; it is clearly written." --Journal of Classification, This book presents a set of closely related techniques that facilitate the visual exploration and display of a wide variety of multivariate data, both categorical and continuous. This technique, which places more highly related items closer to each other than those which are less related, makes complex structure simpler to visualize. Three methods of metric scaling - correspondence analysis, principal components analysis, and multiple dimensional preference scaling - are explored in detail for their strengths and weaknesses over a wide range of data types and research situations. The book focuses upon representing the relations among two or more sets of variables, and upon applications that are exploratory in nature rather than predictive., This book presents a set of closely-related techniques that facilitate the visual exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling - correspondence analysis, principal components analysis and multiple dimensional preference scaling - are explored in detail for their strengths and weaknesses over a wide range of data types and research situations. The book focuses upon representing the relations among two or more sets of variables, and upon applications that are exploratory in nature rather than predictive.
LC Classification NumberH61.27.W45 1990
Be the first to write a review
