People

Our research is carried out in the area of mathematical psychology, psychometrics, statistics applied to psychology, and in the psychological and neuropsychological assessment

The following three research units are involved in the project:

  University of Padua Research Unit

The team from the University of Padua is composed of researchers in Psychometrics:

Prof. Luca Stefanutti (Principal Investigator)

Prof.Egidio Robusto

Prof. Pasquale Anselmi

Dott.ssa Debora de Chiusole

Dott. Andrea Brancaccio

Dott.ssa Marina Ottavia Epifania

The QPgroup (Quantitative Psychology Group) has its operational headquarters in the Applied Psychology section of the FISPPA Department.

Particularly relevant topics linked to the PRIN2020 project are 1. Formal modeling of human cognitive processes, within the frameworks of knowledge space theory and item response theory; 2 Development and validation of psychometric tools for the neuropsychological and psychological assessment; 3. Development of intelligent tutoring system for psychological assessment.

  University of Perugia Research Unit

Prof. Giulia Balboni (local coordinator)

Dott.ssa Alice Bacherini

Dott.ssa Pierluigi Irene

  University of Bologna Research Unit

The team from the University of Bologna is composed of researchers in Psychometrics:

Prof. Mariagrazia Benassi (local coordinator),

Sara Giovagnoli,

Sara Garofalo,

Matteo Orsoni.

The team has its operational headquarters on the Cesena Campus at the Psychometrics and Neuropsychology Lab and at the SPEV Clinical Service of the Department of Psychology “Renzo Canestrari”. Research in the Lab focuses on methodology and applied statistics.

Particularly relevant topics linked to the PRIN2020 project are: 1. the creation and validation of psychometric tools for the neuropsychological assessment of clinical populations (in particular, Specific Learning Disorders and Psychiatric Disorders); 2. The study and applicability of clustering methods as tools for second level diagnostic evaluation; 3. The implementation of machine learning models for the planning and structuring of rehabilitation intervention protocols.