Learning objectives
Students will acquire the theoretical isntruments and the applications to understand and to autonomously develop the statical techniques, referring to General and Generalized Linear Models, most often used in psychobiology and cognitive neurosciences. They also will learn the APA guidelines for the writing of scientific texts (disertations, reports, articles, et cetera).
Prerequisites
Students must had succefully passed Tecniche di Analisi dei Dati I, before to take the exam of Tecniche di Analisi dei Dati II.
Course unit content
Statistical inference: NHST versus model fitting. General Linear Model: features and assumptions. Models of relations among quantitative variables: zero and first order correlations, linear regression, path analsysis, mixed models. Models of relations among cathegorical and continuous variables: ANOVAs. Models of relations among cathegorical variables (Generalized Linear Model): Poisson and logistic regressions, non parametric tests.
Full programme
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Bibliography
Micciolo, R., Espa, G., Canal, L. (2013). Ricerca con R – metodi di inferenza statistica. Apogeo edizioni (capp. 1, 2, 5).
Gallucci, M., Leone, L. (2012). Modelli statistici per le scienze sociali. Pearson. (pp. 19-264; 425-457).
Task Force on Statistical Inference – American Psychological Association (1999). Follow up report: Statistical methods in psychology journals. (pp. 1-11). http://www.apa.org/science/leadership/bsa/statistical/tfsi-followup-report.pdf
Teaching methods
Frontal lesson and group exercises (program R)
Assessment methods and criteria
The students will must do three ongoing written tests using the R Program, at home, and consign them within the established deadlines: only students respecting all these terms will be considered as attending. Non attending students will subdue to a written test using R and to an oral examination.
Other information
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2030 agenda goals for sustainable development
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