Duration
15h Th, 15h Pr
Number of credits
| Master in psychology (120 ECTS) | 4 crédits |
Lecturer
André Ferrara, Christian Monseur, Francis Pérée, Valentine Vanootighem
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Part I: Confirmatory factor analysis (Francis Pérée).
Structural equation modelling is essentially used to confirm the validity of theories involving situations characterised by the presence of latent variables that are not directly observable.
Familiarisation with the basic concepts and procedures used in the specification, identification, estimation and fitting of structural models. Review of some fundamentals of statistical analysis (data types, variable types, data file types). Notion of latent variable, characteristics and classical applications.
The three main types of models are discussed and illustrated by concrete examples (CFA, Path Analysis, SEM). The case of categorical observed variables (e.g. Likert scale) are also illustrated by some examples.
Part II: Multi-group confirmatory factor analysis (Christian Monseur).
Principles and implementation of the study of the invariance of a measurement instrument by multi-group confirmatory factor analysis (MGCFA) for continuous and categorical manifest variables.
Part III: Behavioural observation (Ferrarra André).
Presentation of typical situations requiring behavioural observation in the clinical setting or in laboratory research. General principles for the design of a behavioural observation grid. Fidelity and validity of the measures collected in the framework of the observation.
Part IV: Signal detection theory (Valentine Vanootighem).
Introduction to the theory of signal detection (SDT ). SDT is widely used to measure performance in perceptual, memory and categorisation tasks and has many applications in various domains. The specificity of SDT is to allow an estimation of subjects' performance in a forced-choice task while taking into account the strategies they adopt to make their decisions (liberal, neutral or conservative response criteria).
Learning outcomes of the learning unit
Students will be able to:
- perform CFA, Path Analysis, SEM and MGCFA analyses and interpret the results.
- evaluate with the appropriate tool the inter-observer fidelity and understand the elements that guarantee the quality of the observation of the behavior.
- use the theory of signal detection in the context of perception, memory and categorization tasks.
Prerequisite knowledge and skills
Planned learning activities and teaching methods
Each face-to-face session will be given in the CAFEIM room (B32). It will be completed by exercises to be done in class, exercises for which the correction will be provided, and by complementary online exercises (eCampus).
The use of the R language (with associated packages such as "lavaan", "irr", etc.) will allow students to work on their personal computer.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Recommended or required readings
All course materials can be found on eCampus. (PowerPoint of the oral sessions; scientific articles; data files for the exercises; R scripts; links to sites hosting R software and RStudio Desktop).
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Additional information:
In addition to open-ended questions, the written exam will include computer-based exercises to solve several concrete problems in the application of statistics (CFA, Path Analysis, SEM, MGCFA).
Students will have access to the course PowerPoint and their personal notes.
See additional information on eCampus.
Work placement(s)
Organisational remarks and main changes to the course
See eCampus for documents and details of planned learning activities.
Contacts
Ferrara André : 04/366.22.32 ; a.ferrara@uliege.be
Monseur Christian : 04/336.20.95 ; cmonseur@uliege.be
Pérée Francis : 04/366.22.31 ; fperee@uliege.be
Vanootighem Valentine : 04/366.20.21 ; Valentine.Vanootighem@uliege.be