Learning objectives
1. Knowledge and understanding. Students will be able to achieve a good knowledge of the most widely used advanced statistical models in the context of psychological scientific research, applying them to real data in research contexts.
2. Ability to apply knowledge and understanding. Students will be able to use the R development environment to implement statistical data analysis techniques appropriate to the research hypothesis and the nature of the variables in question.
3. Independent judgment. Students will be able to develop critical skills and independent judgment with respect to the techniques of analysis of complex models in technical reports and the interpretation of the results obtained.
4. Communication skills. Students will be able to communicate the results of data analysis, both in numerical and graphic form, with the appropriate iconographic apparatus and the adequate technical-statistical lexicon, to specialist and non-specialist interlocutors, according to APA indications .
5. Learning ability. Students will be able to learn in a predominantly autonomous way new techniques for conducting statistical analysis techniques, in particular in the development environment.
Prerequisites
Having attended the Data Analysis Techniques course is required as a prerequisite.
Course unit content
The course aims to provide students with the theoretical and applicative tools to understand and independently develop the most important statistical techniques that constitute the applications of the General Linear Model (GLM), its extension (Generalized Linear Model) and the techniques for reducing the complexity of the data most frequently applied in psychobiological and cognitive neuroscience research. Guidelines will also be provided for the correct drafting of statistical techniques used in the production of texts illustrating research results (degree theses, communication at conferences, scientific articles), according to APA recommendations. All analyzes will be handled via the R computing environment.
Full programme
Relationship models between quantitative variables: multiple linear regression.
Relationship models between continuous variables and categorical variables: univariate (with one and with multiple predictors; ANOVA) and multivariate (MANOVA) analysis of variance, univariate (ANCOVA) and multivariate (MANCOVA) analysis of covariance.
Non-linear regression models (mixed models); binomial and multinomial logistic regression, Cox regression with survival analysis.
Complexity reduction methods: factor analysis, cluster analysis.
Bibliography
Gallucci, M., Leone, L. (2012). Statistical models for the social sciences (2nd ed). Pearson. Chapters: 3-4-5-6-7-8-9-12-13-14.
Handout provided by the teacher, available on the teaching page on the Elly platform.
Follow
Task Force on Statistical Inference – American Psychological Association (1999). up report: Statistical methods in psychology journals. (pp. 1-11). http://www.apa.org/science/leadership/bsa/statistical/tfsi-followup-report.pdf
Teaching methods
Lessons will take place in person. The teaching material will be stored on the specific platform with access reserved for students (Elly) and will include iconographic and/or audio-video supporting presentations.
It is strongly recommended to accompany the theoretical preparation with the execution of the proposed exercises; it is suggested to ask the teacher for feedback on their correctness.
Assessment methods and criteria
Written exam, with two open theory questions on the entire program, and an exercise in the R environment. The exercise involves the analysis of data that will be made available (via Elly) a maximum of 48 hours before the test. The analyzes will include a first part relating to descriptive statistics on the data and two subsequent parts in which inferential tests of hypotheses will be required. In addition to the correct execution of the statistics, the ability to adequately interpret and comment on the analysis outputs will be considered an essential part of the sufficiency. The evaluation out of thirty will be as follows:
first theory question: 0-8 points; second theory question: 0-8 points
exercise: 0-14 points, divided as follows:
first part 0-4 points;
second part 0-5 points;
third part 0-5 points.
The student has the right to request that the exam be integrated with an oral test, provided that the outcome of the written test is overall sufficient. The oral test is structured in a similar way to the written test: questions relating to the contents of the entire program and a short exercise in the R environment on data used for the exercises during the course.
Students with DSA/BSE must first contact the University Centro Accoglienza e Inclusione (Reception and Inclusion Center), cai@unipr.it
Other information
- - -
2030 agenda goals for sustainable development
- - -
