Students will acquire the theoretical instruments 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).
Students must had succefully passed Tecniche di Analisi di Dati I, before to take the exam of Tecniche di Analisi di Dati II.
Course contents summary
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.
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-repo...
Integrative educational material (lecture notes, PPT presentations) is available at: http: //psicobiologia.unipr.it.
Frontal lessons and group exercises (statistical package R)
Assessment methods and criteria
Attending as well as not attending students may choose to take three ongoing written exams, concerning statistical theory arguments and a data analysis using the package R Program. Otherwise, or if their ongoing global evaluation did not get a passing grade, students will take an analogous written test, covering th eoverall exam program. An additional oral examination is discretional.