STA254††††† Department of Statistics, Stanford University, Fall, 2008


Correspondence Analysis and Related Methods


Michael Greenacre

Universitat Pompeu Fabra, Barcelona



Week 1 (colour)††††††† Week 1 (black&white)

Week 2 (colour)††††††† Week 2 (black&white)

Week 3 (colour)††††††† Week 3 (black&white)

Week 4 (colour)††††††† Week 4 (black&white)

Week 5 (colour)††††††† Week 5 (black&white)

Week 6 (colour)††††††† Week 6 (black&white)

Week 7 (colour)††††††† Week 7 (black&white)

Week 8 (colour)††††††† Week 8 (black&white)

Week 9 (colour)††††††† Week 9 (black&white)

Week10(colour)††††††† Week10(black&white)


R scripts

Week 1††††††††† chidist††††††††††† braycurtis†††††† ††††† jaccard

Week 2

Week 3

Week 4

Week 6††††††††† See also Appendix B of Correspondence Analysis in Practice

Week 7


R packages (zipped)

Correspondence analysis††††††††††††††††††††††††† ca†††††

Three-dimensional graphics††††††††††††††††††††† rgl†††††

Classification & regression trees††††††††††††† tree†††††

Multivariate analysis in ecology†††††††††††††††† vegan


Data sets

BioEnvGeo††† †† EU†† ††† Salud††††††††† Salud2††††††† WomenWork††††Environmental questionnaire data



Week 1

Week 2

Week 3

Week 4

Week 5: read chapter 10 of Correspondence Analysis in Practice

Week 6:

††††††††††† Data sets from ISSP 2002 survey: get documentationin issp02.pdf

††††††††††† Working woman can establish just as warm a relationship with her child (v4)

††††††††††† Menís job is to work; womenís job is to look after household (v11)

††††††††††† Couple can live together without getting married (v22)

††††††††††† Working mother should get paid maternity leave(v27)

††††††††††† How satisfied with life in general (v52)

††††††††††† How satisfied with family life (v54)

Week9: supplementary reading chapter 20 of Correspondence Analysis in Practice



R reference card

Popular article on correspondence analysis

Publishing quantitative results

Appendix A (Theory of Correspondence Analysis) of Correspondence Analysis in Practice


Additional reading

Chapter on Euclidean distance

Chapter on non-Euclidean distance

Chapter on distance and correlation between variables

Chapter on hierarchical clustering


CARME network



Basic information

The accent in this course is on learning about tools, mostly for visualizing multivariate data,that can be applied to practical problems.Correspondence analysis (CA) plays a central role in this area because it applies to count data, the most basic form of statistical measurement.Many other data types (raw categorical data, preferences, ratings, continuous measurements, distances) can be recoded in a form suitable for being visulalized using correspondence analysis, hence CA is a versatile framework for data visualization.†† CA is routinely used by ecologists, who count the occurrences of plants and animals, and social scientists, who count the responses of people; also by market researchers, linguists, psychologists, biomedical researchers and archeologists, to name a few...

Classes: Tuesdays & Thursdays 12h50-14h05, Sequoia 200.

Some reading and/or homework every week, some of which is graded.

Homework mainly consists of applying R functions to data sets and interpreting the results-- Narasís course on Computational Tools for Statistics is highly recommended.

Homework counts 20% towards final grade.

Final project counts 70% towards final grade.

Class attendance & participation counts 10% towards final grade.

My office hours for seeing students are Mondays 15.00-18.00and Thursdays 16.00-18.00 Ė my office is room Sequoia 128.

You can arrange a meeting at other times by contacting me by email.

You can use my regular email addresses for communication but I actually prefer to channel all emails about teaching through this address:

††††††††††††††††††††††† mmr.upf (at)

so please try to use that address if possible Ė this will be more efficient.