Methods and techniques of Quantitative Research


Knowledge and understanding of the main methods and techniques of quantittiv data analysis and their uses.
Aquisition of skills for critically assessing and interpreting the outputs of the main tools for quantitative analysis.

General characterization





Responsible teacher

Ana Lúcia Albano Teixeira


Weekly - Available soon

Total - 224

Teaching language



Available soon


Carvalho, H. (2008). Análise multivariada de dados qualitativos: Utilização da ACM com o SPSS. Lisboa: Sílabo.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Essex: Pearson.
Laureano, R. (2011). Testes de hipóteses com o SPSS: O meu manual de consulta rápida. Lisboa: Sílabo.
Marôco, J. (2014). Análise de equações estruturais: Fundamentos teóricos, software e aplicações. Lisboa: Report Number.
Marôco, J. (2011). Análise estatística com o SPSS Statistics. Lisboa: Report Number.
Reis, E. (2007). Estatística descritiva. Lisboa: Sílabo.
Reis, E., Melo, P., Andrade, R., & Calapez, T. (2001). Estatística aplicada, Vol.2. Lisboa: Sílabo.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Boston: Pearson.
Vicente, P., Reis, E., & Ferrão, F. (2001). Sondagens: A amostragem como factor decisivo de qualidade. Lisboa: Sílabo.

Teaching method

Introductory lectures by the teachers, followed by practical exercises and analysis by students of examples of applications in published research.

Evaluation method

One written essay discussing the applicatio of at least on of the introduced methods in resach context, presentd and discussed in class.(100%)

Subject matter

1. Kinds of data and measurement scales
2. Sampling
a. representativeness
b. types of samples
c. error
3. Descriptive statistics
a. graphical representation
b. contingency tables
c. measures of central tendency
d. measures of dispersion
e. the normal distribution
4. Chi-squared independence test (hypothesis testing)
5. Association measures
6. Correlation coefficients
7. Tests of comparison of means: t-test for independent samples
8. Variance analysis: one-factor ANOVA
9. Multiple and multilevel linear regression
10. Multiple correspondence analysis (MCA)
11. Multidimensional scaling (MDS)
12 Principal components analysis (PCA)
13 Structural equations analysis (SEM)


Programs where the course is taught: