Statistics/Neuropsychology

Teaching Methods

It is used a mixed methodology consisting of lectures and active methods. The lectures are used mainly to convey technical information about each of the statistical techniques covered in the syllabus. The active methods, namely based on problem solving (with fictitious data or adapted from real investigations), engage students in a process of cooperative learning (in pairs, preferably), and aim to develop practical skills in the full implementation of the data analysis process, using the different techniques taught.

Learning Outcomes

The unit aims to develop a set of methods of data analysis at an advanced level. At the end of the program the student should be able to: 1) select the appropriate statistical method to a given research problem, considering the theoretical foundations on which it is based; 2) assess whether the data meet the mathematical assumptions underlying the chosen method; 3) know how to use an appropriate statistical software to perform the planned statistical techniques; 4) to interpret the outputs achieved and select the information relevant to the testing of the planned hypotheses; 5) write a technical report including the key aspects of the results achieved.  

Work Placement(s)

No

Syllabus

1. ANOVA

 Factorial ANOVA;

 Multiple Comparisons (a priori and a posteriori);

 ANOVA - repeated measures;

 Mixed ANOVA (SPANOVA);

 Analysis of covariance.

II. Regression:

 Multiple regression;

 Hierarchical regression;

 Logistic regression;

 Testing the assumptions of ANOVA.
III. Multivariate analysis:

 MANOVA;

 Discriminant analysis;

 Canonical correlation.

IV. Structural equation models:

 Trajectory analysis / Path analysis;

 Structural equation models with latent variables;

 Confirmatory factor analysis and regression models.

V. Longitudinal Data Analysis:

Time series analysis;

Multilevel analysis.

Bibliography

Cohen, B. (2008). Explaining Psychological Statistics. Hoboken, NJ: Wiley.

Glass, G. V. & Hopkins, K. D. (1996). Statistical Methods in Education and Psychology (3rd ed.). Boston: Allyn & Bacon

Hair, J. Black, W., Babin, B., &Anderson, R. (2010)). Multivariate data analysis (6^th ed.). New Jersey: Prentice-Hall

Howell, D. (1997). Statistical methods for psychology (4ª Ed.). Belmont, CA: Duxbury Press.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.

Maroco, J. (2011). Análise estatística – Com o SPSS Statistics (5ª ed.). Pero Pinheiro: ReportNumber.

Norusis, M. (2012). IBM SPSS Statistics 19 Guide to Data Analysis. Upper Saddle River, NJ: Pearson.

Pestana, M. H. e Gageiro, J. N. (2008). Análise de Dados para Ciências Sociais - A complementaridade do SPSS (5ª ed.). Lisboa: Edições Sílabo.

Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Boston: Allyn and Bacon.