2023-2024 / PSYC5930-1

Selected issues in data collection and analysis


15h Th, 15h Pr

Number of credits

 Master in psychology (120 ECTS)4 crédits 


André Ferrara, Christian Monseur, Francis Pérée, Valentine Vanootighem


André Ferrara

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

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.


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

Association of one or more MOOCs