Multivariate Data Analysis


a) Understanding of how to use systematic, statistical and computer processing of data in relation to the general research strategy; b) Knowledge of and ability to apply mediator operations between the research dossier and analytical protocol; c) Ability to define the technical specifications for data analysis; d) Knowledge of and ability to use the principal statistical solutions for purpose of multivariate data analysis in Sociology; e) Knowledge and ability to use the analyses and measures of association for purposes of multivariate processing; f) nderstanding of modes of integration in analytical protocols, factor analysis (simple and multiple) and post factor analysis techniques; g) Understanding of algorithms of simple and multiple multivariate analyses; h) Ability to use the main computer programmes for statistical processing on the market; i) Ability to communicate the results of multivariate statistical analyses and their interpretation in a rigorous and significant manner.

General characterization





Responsible teacher

Ana Lúcia Albano Teixeira


Weekly - Available soon

Total - 168

Teaching language



Available soon


Benzécri, J. P. (1973). L´analyse des données: l´analyse des correspondances. Dunod. Benzécri, J.-P., et al. (1982). L’analyse des données: 2 l’analyse des correspondances. Dunod. Benzécri, J.-P., et al. (1984). L’analyse des données: 1 la taxinomie. Dunod. Carvalho, H. (2008). Análise multivariada de dados qualitativos. Utilização da ACM com o SPSS. Sílabo. Ghiglione, R., & Matalon, B. (1992). O Inquérito. Teoria e prática. Celta. Guimarães, R. C., & Cabral, J. S. (1997). Estatística. Lisboa: McGraw Hill. Hill, M. M., & Hill, A. (2000). Investigação por questionário. Sílabo. Marôco, J. (2010). Análise estatística com o PASW Statistics. Report Number. Pestana, M. H., & Gageiro, J. N. (2000). Análise de dados para ciências sociais. A complementaridade do SPSS. Sílabo.

Teaching method

Lectures presenting systematized and structured knowledge of both tehoretical and technical nature;
Exercises carried out in practical classes and as homework.

Evaluation method

two written tests (45% each)(90%), Continuous evaluation of exercises carried out in class (10%)

Subject matter

1. Methodological and statistical assumptions for multivariate data analysis 2. Multiple Correspondences Analysis 3. Principal Component Analysis 4. Classification Analysis - Cluster Analysis 5. Introduction to Linear Regression Analysis - Simple Linear Regression Analysis