This volume provides readers with a simple, non-technical introduction to correspondence analysis (CA), a technique for summarily describing the relationships among categorical variables in large tables. It begins with the history and logic of CA, followed by an explanation of the analysis of large contingency tables and of survey data. The author shows readers the steps to the analysis: category profiles (relative frequencies) and masses (marginal proportions) are computed, the distances between these points calculated, and the best fitting space of n-dimensions located. In addition, the author provides glossaries on appropriate programs from SAS and SPSS for doing CA. The book concludes with a comparison of CA and loglinear models.

Researchers and professionals in sociology, market research, and other social sciences will find Applied Correspondence Analysis a useful tool for doing CA in the various statistical packages.

This volume provides readers with a simple, non-technical introduction to correspondence analysis (CA), a technique for summarily describing the relationships among categorical variables in large tables. It begins with the history and logic of CA, followed by an explanation of the analysis of large contingency tables and of survey data. The author shows readers the steps to the analysis: category profiles (relative frequencies) and masses (marginal proportions) are computed, the distances between these points calculated, and the best fitting space of n-dimensions located. In addition, the author provides glossaries on appropriate programs from SAS and SPSS for doing CA. The book concludes with a comparison of CA and loglinear models.

Researchers and professionals in sociology, market research, and other social sciences will find Applied Correspondence Analysis a useful tool for doing CA in the various statistical packages.

This volume provides readers with a simple, non-technical introduction to correspondence analysis (CA), a technique for summarily describing the relationships among categorical variables in large tables. It begins with the history and logic of CA, followed by an explanation of the analysis of large contingency tables and of survey data. The author shows readers the steps to the analysis: category profiles (relative frequencies) and masses (marginal proportions) are computed, the distances between these points calculated, and the best fitting space of n-dimensions located. In addition, the author provides glossaries on appropriate programs from SAS and SPSS for doing CA. The book concludes with a comparison of CA and loglinear models.

Researchers and professionals in sociology, market research, and other social sciences will find Applied Correspondence Analysis a useful tool for doing CA in the various statistical packages.