Laboratorio didattico: principi di statistica per le scienze cognitive
A.Y. 2026/2027
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: First semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Course syllabus
· Introduction to statistics in cognitive sciences.
· Data organization, coding, and management using SPSS.
· Descriptive statistics: frequencies, measures of central tendency, and measures of dispersion.
· Data visualization: tables and graphical representations.
· Fundamental concepts of statistical inference.
· Independent-samples and paired-samples t-tests.
· One-way Analysis of Variance (ANOVA).
· Correlation and simple linear regression.
· Chi-square tests and analysis of categorical variables.
· Logistic regression models.
· Cox proportional hazards models and survival analysis.
· Multivariate statistical models and methods.
· Data organization, coding, and management using SPSS.
· Descriptive statistics: frequencies, measures of central tendency, and measures of dispersion.
· Data visualization: tables and graphical representations.
· Fundamental concepts of statistical inference.
· Independent-samples and paired-samples t-tests.
· One-way Analysis of Variance (ANOVA).
· Correlation and simple linear regression.
· Chi-square tests and analysis of categorical variables.
· Logistic regression models.
· Cox proportional hazards models and survival analysis.
· Multivariate statistical models and methods.
Prerequisites for admission
No advanced statistical knowledge is required. However, a basic understanding of mathematics and computer use is recommended. The laboratory is designed to provide students with the fundamental tools for statistical data analysis in cognitive sciences using the SPSS software.
Teaching methods
The course consists of laboratory activities conducted in a computer lab using the SPSS software. Classes will combine brief theoretical introductions with guided practical exercises based on real or simulated datasets from the field of cognitive sciences. Students will engage in both individual and group activities aimed at developing practical skills in data analysis and interpretation. Teaching materials and datasets for independent practice will also be provided.
Teaching Resources
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Assessment methods and Criteria
Assessment is based on an oral presentation of a project report developed through the analysis of a dataset using SPSS. Students are expected to demonstrate their ability to:
· import and manage data in SPSS;
· perform descriptive statistical analyses;
· select and correctly apply the main statistical tests covered during the course;
· critically interpret the results obtained;
· present findings using appropriate tables and graphical representations.
Evaluation will take into account the correctness of the statistical procedures employed, the accuracy of the interpretation of results, and the clarity and effectiveness of the presentation of the project report.
· import and manage data in SPSS;
· perform descriptive statistical analyses;
· select and correctly apply the main statistical tests covered during the course;
· critically interpret the results obtained;
· present findings using appropriate tables and graphical representations.
Evaluation will take into account the correctness of the statistical procedures employed, the accuracy of the interpretation of results, and the clarity and effectiveness of the presentation of the project report.
- University credits: 3
Lessons: 20 hours
Professor:
Bonomi Alice
Shifts:
Turno
Professor:
Bonomi AliceProfessor(s)