Correspondence Analysis in the Social Sciences
Published on 4. August 1994
Book
Hardback
370 pages
978-0-12-104570-8 (ISBN)
Description
Correspondence Analysis in the Social Sciences gives a comprehensive description of this method of data visualization as well as numerous applications to a wide range of social science data. Various theoretical aspects are presented in a language accessible to both social scientists and statisticians and a wide variety of applications are given which demonstrate the versatility of the method to interpret tabular data in a unique graphical way.
Reviews / Votes
"This volume is particularly noteworthy for the chapters stressing computations and historical perspectives. There are many interesting data sets as well as detailed analyses." --JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATIONMore details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
AUDIENCE
Postgraduate students in psychology, sociology, business and statistics
Dimensions
Height: 229 mm
Width: 152 mm
Weight
700 gr
ISBN-13
978-0-12-104570-8 (9780121045708)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Michael J. Greenacre is Professor of Statistics at the University of South Africa. He has been involved with the theoretical development and practical applications of correspondence analysis in the USA, UK, South Africa and Germany. Previous publications include Theory and Applications of Correspondence Analysis (Academic Press, 1984) and Correspondence Analysis in Practice (Academic Press, 1993). Jorg Blasius is Researcher at the Zentralarchiv fur empirische Sozialforschung at the University of Cologne. His primary research interests are multivariate exploratory analysis, urban research, life-styles and methods of empirical social research.
Content
General Introduction:
M. Greenacre, Correspondence Analysis and its Interpretation.
J. Blasius, Correspondence Analysis in Social Science Research.
J. Blasius and M. Greenacre, Computation of Correspondence Analysis.
P.G.M. van der Heijden, A. Mooijaart, and Y. Takane, Correspondence Analysis and Contingency Table Models.
U. Bickenholt and Y. Takane, Linear Constraints in Correspondence Analysis.
The BMS (K.M. van Meter, M.-A. Schiltz, P. Cibois, and L. Mounier), Correspondence Analysis: A History and French Sociological Perspective. Generalizations to Multivariate Data:
M. Greenacre, Multiple and Joint Correspondence Analysis.
L. Lebart, Complementary Use of Correspondence Analysis and Cluster Analysis.
W.J. Heiser and J.J. Meulman, Homogeneity Analysis: Exploring the Distribution of Variables and their Nonlinear Relationships.
J. Rovan, Visualizing Solutions in more than Two Dimensions.
Analysis of Longitudinal Data:
B. Martens, Analyzing Event History Data by Cluster Analysis and Multiple Correspondence Analysis: An example using data about work and occupations of scientists and engineers.
V. Thiessen, H. Rohlinger, and J. Blasius, The Significance of Minor Changes in Panel Data: A correspondence analysis of the division of household tasks.
T. Muller-Schneider, The Visualization of Structural Change by Means of Correspondence Analysis.
Further Applications of Correspondence Analysis in Social Science Research:
H. Giegler and H. Klein, Correspondence Analysis of Textual Data from Personal Advertisements.
U. Wuggenig and P. Mnich, Explorations in Social Spaces: Gender, Age, Class Fractions and Photographical Choices of Objects.
H.M.J.J. (Dirk) Snelders and M.J.W. Stokmans, Product Perception and Preference in Consumer Decision-making.
References.
Index.
M. Greenacre, Correspondence Analysis and its Interpretation.
J. Blasius, Correspondence Analysis in Social Science Research.
J. Blasius and M. Greenacre, Computation of Correspondence Analysis.
P.G.M. van der Heijden, A. Mooijaart, and Y. Takane, Correspondence Analysis and Contingency Table Models.
U. Bickenholt and Y. Takane, Linear Constraints in Correspondence Analysis.
The BMS (K.M. van Meter, M.-A. Schiltz, P. Cibois, and L. Mounier), Correspondence Analysis: A History and French Sociological Perspective. Generalizations to Multivariate Data:
M. Greenacre, Multiple and Joint Correspondence Analysis.
L. Lebart, Complementary Use of Correspondence Analysis and Cluster Analysis.
W.J. Heiser and J.J. Meulman, Homogeneity Analysis: Exploring the Distribution of Variables and their Nonlinear Relationships.
J. Rovan, Visualizing Solutions in more than Two Dimensions.
Analysis of Longitudinal Data:
B. Martens, Analyzing Event History Data by Cluster Analysis and Multiple Correspondence Analysis: An example using data about work and occupations of scientists and engineers.
V. Thiessen, H. Rohlinger, and J. Blasius, The Significance of Minor Changes in Panel Data: A correspondence analysis of the division of household tasks.
T. Muller-Schneider, The Visualization of Structural Change by Means of Correspondence Analysis.
Further Applications of Correspondence Analysis in Social Science Research:
H. Giegler and H. Klein, Correspondence Analysis of Textual Data from Personal Advertisements.
U. Wuggenig and P. Mnich, Explorations in Social Spaces: Gender, Age, Class Fractions and Photographical Choices of Objects.
H.M.J.J. (Dirk) Snelders and M.J.W. Stokmans, Product Perception and Preference in Consumer Decision-making.
References.
Index.