2.00 crédits
15.0 h
Q2
Enseignants
Heeren Alexandre;
Langue
d'enseignement
d'enseignement
Anglais
Thèmes abordés
Chaque année, une liste d'ateliers est proposée aux étudiants. Ces ateliers abordent de façon détaillée les outils, les méthodes et les analyses spécifiquement utilisés dans la recherche en Psychologie et Sciences de l'Education. Chaque atelier a une durée de 15 heures
Acquis
d'apprentissage
d'apprentissage
A la fin de cette unité d’enseignement, l’étudiant est capable de : | |
| 1 |
A2. modéliser une situation (groupe ou organisation) en référence à des méthodes et outils relevant de la psychologie B2. planifier une intervention visant à améliorer une situation donnée (individu, groupe ou organisation) C1. présenter oralement et par écrit une synthèse en l'adaptant à différents publics et aux intentions poursuivies; C2. structurer et présenter des données recueillies. E1. maitriser les étapes, les méthodologies et les outils de la recherche scientifique en psychologie et sciences de l'éducation F2. mettre en oeuvre les moyens et les opportunités pour développer un projet professionnel personnel. |
Contenu
Graph theory and network analysis have started to infiltrate psychological sciences, especially in research agendas dealing with large datasets. Accordingly, this course will provide a general overview of the application of graph theory and network analysis in psychological sciences. Applications on real data sets will be provided throughout the workshop. Given the audience's diversity, illustrations will range from social networks to brain networks and symptoms networks.
Through this course, participants will:
- become familiar with general notions of graph theory and network analysis
- learn how to model network data using R, to implement algorithms from the field of graph theory (e.g., community detection, small-wordness), and to use up-to-date tools from statistical network analysis (e.g., graphical Lasso, subset bootstrap, Bayesian modeling) to optimize network estimation and visualization
- understand the advantages, challenges, and limitations of network analysis in comparison to other analytical approaches
- and become able to critically assess papers dealing with network analysis and graph theory in psychological sciences.
Through this course, participants will:
- become familiar with general notions of graph theory and network analysis
- learn how to model network data using R, to implement algorithms from the field of graph theory (e.g., community detection, small-wordness), and to use up-to-date tools from statistical network analysis (e.g., graphical Lasso, subset bootstrap, Bayesian modeling) to optimize network estimation and visualization
- understand the advantages, challenges, and limitations of network analysis in comparison to other analytical approaches
- and become able to critically assess papers dealing with network analysis and graph theory in psychological sciences.
Méthodes d'enseignement
Teaching and assessment will be delivered via a classroom setting, on the Louvain-la-Neuve campus, but could be carried out remotely (via Teams) should the health situation require to do so.
Modes d'évaluation
des acquis des étudiants
des acquis des étudiants
First session: Oral test about theorical aspects and use of R to do network analysis (9 points /20), oral presentation and review of a research paper using network analytical tools (9 points / 20), and continuous assessment via homeworks (2 points / 20)
Second session: Oral test (20 points /20)
Second session: Oral test (20 points /20)
Ressources
en ligne
en ligne
Handouts, as well as examples of R programming codes, will be made available via Moodle.
Bibliographie
A list of reading articles will be provided at the end of each session.
Support de cours
- Network Science, by ALBERT-LÁSZLÓ BARABÁSI
- Network Psychometrics with R: A Guide for Behavioral and Social Scientists, Edited By Adela-Maria Isvoranu, Sacha Epskamp, Lourens Waldorp, Denny Borsboom
Faculté ou entité
en charge
en charge
EPSY
