Topic Type: Statistical Analysis Techniques Using Artificial Intelligence Algorithms
Duration in hours: 24 hours of frontal lectures
Start and End Dates: December 2025
Teaching Methods: Frontal lectures and practical exercises
Lecturers: Dr. Merylin Monaro
Program: The course opens with an introduction to artificial intelligence and machine learning, covering the following topics: definition of machine learning, types of learning (supervised, unsupervised, reinforcement learning), task types (regression, classification, clustering), performance metrics for algorithms (e.g., accuracy, AUC, sensitivity, specificity), types of classifiers (e.g., decision trees, SVM, logistic regression, KNN, etc.), feature selection techniques, and model validation techniques (e.g., k-fold cross-validation).
The concepts acquired are consolidated through practical exercises (application of machine learning techniques to data collected in psychological and forensic contexts). These activities are carried out through learning and using the WEKA software.
The second part of the course is dedicated to examples of practical applications of machine learning to neuroscience, psychology, and particularly to the forensic domain (e.g., the use of machine learning and natural language processing for lie detection, identification of at-risk personality profiles, brain and mind reading).
Final Assessment mode: Practical exercise
Topic Type: Advanced Statistical Methodologies: Exploratory and Confirmatory Factor Analysis, Structural Equation Modeling, General, Mixed, and Generalized Linear Models
Duration in hours: 20 hours of frontal lectures
Start and End Dates: January–February 2025
Teaching Methods: Frontal lectures and practical exercises
Lecturer: Dr. Marco Iosa
Program: This course focuses on more complex and general models useful for psychometric statistical analysis. It begins with the presentation of statistical techniques in factor analysis, both exploratory and confirmatory, using the IBM SPSS Statistics software.
It then introduces, again with the support of SPSS, models belonging to the GLM family (Univariate, Multivariate, and Repeated Measures), with theoretical conceptual explanations and concrete application examples on psychometric datasets.
The course then moves to generalized linear models, with particular focus on selecting the appropriate link function depending on the model being constructed and on interpreting the statistical software output.
Finally, structural equation models (SEM) are presented using IBM’s AMOS application. SEMs are introduced both conceptually and through examples of model design and interpretation of software-generated results.
By the end of the course, students will have solid proficiency and substantial autonomy in developing advanced statistical models suited to a variety of scenarios.
Final Assessment Mode: Practical exercise
Topic Type: Methodologies for Conducting Meta-Analyses
Duration in hours: 20 hours of frontal lectures
Start and End Dates: January–February 2025
Teaching Methods: Frontal lectures and practical exercises
Lecturer: Dr. Giovanna Parmigiani
Program:
The course aims to equip doctoral students with the ability to:
i) understand international guidelines for producing and updating meta-analyses;
ii) formulate the research question, define study eligibility criteria, design the bibliographic search, select studies and extract data, assess the quality of included studies, produce Summary of Findings tables, conduct statistical analyses, and present the results;
iii) draft and register a meta-analysis protocol and acquire the methodological knowledge necessary to write a full meta-analysis.
Final Assessment Mode: Practical exercise