summer school

Introduction to Network Psychometric Analysis in Psychology

 

Milan

 

Formazione continua
 


 

 

 

psychology

The Psychometric Network Analysis approach is emerging as a complement to latent variable models for the investigation of the complexity of psychological processes. By using network models it is possible to represent psychological processes as systems of interacting elements and investigate their dynamics of change over time.

The Summer School offers an introduction to this approach, considering its application to both cross-sectional and longitudinal data. Practices will be proposed through the use of the statistical software R.

Aims

The Summer School enables the acquisition of specific knowledge and skills:

Knowledge

  • Management of the complexity of phenomena that are not directly observable through different measurement models
  • Knowledge of Psychometric Network Analysis as a model for managing the complexity of psychological processes
  • Data management (collection, analysis, visualisation and communication of data and results)

Skills

  • Interpretation of charts and graphs for data management
  • Problem solving
  • Critical thinking skills
  • Use of R software

Participants will be invited to apply what they have learned to their data using R, with the possibility of receiving feedback from the lecturers; they will be able to obtain feedback on future data analysis projects, or practice on datasets made available by the lecturers.

Programme

Lectures, applications and exercises, and discussion of own research data, from 27 to 29 of May 2024. Exercises will be conducted with the R software of which a preliminary knowledge is recommended but not mandatory. Materials and videos introducing the use of the R software prepared by the lecturers will be shared as preparatory material at the Summer School.


Schedule
27 May (9.00 - 18.00)
Morning: Introduction to Psychometric Network Analysis, with examples of applications in psychology and practical session
Afternoon: Lectio Magistralis by Prof. Denny Borsboom (online)

28 May (9.00 - 18.00)
Morning: Stability of estimates, violations of normality, reproducibility, multigroup network analysis.
Afternoon: Practical session and application to participants' data

29 May (9.00 - 17.00)
Morning: Introduction to longitudinal and intensive designs, Multilevel Network Analysis, psychometrics
Afternoon: Flash presentations of students' projects
Lectures, applications and exercises, and discussion of own research data. Participants will be invited to apply what they have learned to their data using R, with the possibility of receiving feedback from the lecturers and tutor; they will be able to obtain feedback on future data analysis projects, or practice on datasets made available by the lecturers. A preliminary knowledge of the R software is recommended but not mandatory. Materials and videos introducing the use of the R software prepared by the lecturers will be shared as preparatory material at the Summer School.

Key features

The high scientific quality of the Faculty including an internationally renowned lecturer

The interaction with the lecturers, including discussion of the participants' research project

The certificate of attendance and the Open Badge to facilitate visibility of the skills acquired

Info point

Participants profile

PhD students, adjuct professors, research fellows, researchers, professors

When and where

From 27 to 29 May 2024 at Università Cattolica del Sacro Cuore in Milan
Enrol by 19 May

 

Participation fee

350 € + VAT: Full fee
315 € + VAT: Fee for graduates from UCSC Servizi Premium

Faculty

SCIENTIFIC DIRECTION
Semira Tagliabue
Associate professor in Psychometrics, Faculty of Psychology
Università Cattolica del Sacro Cuore

LECTURERS
Giulio Costantini

Associate professor in Psychometrics, Faculty of Psychology, Università Milano-Bicocca
Michela Zambelli
Post-doctoral fellowship in Psychometrics, Università di Genova, Adjunct Professor, Università Cattolica del Sacro Cuore
Denny Borsboom
Social and behavioural data science Centre, university of Amsterdam

 

Information