Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
6 credits
45.0 h + 15.0 h
Q1
Teacher(s)
Caesens Gaëtane; Grégoire Jacques; Penta Massimo; Penta Massimo (compensates Grégoire Jacques);
Language
French
Main themes
Item response models, particularly the Rasch model, for the construction of measurement scales
Factor analysis, structural equation models
Factor analysis, structural equation models
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
A2 : etc...ceci doit être rédigé de manière commune pour tous les cours et donc je suppose par l'instance responsable de l'adoption de ces définitions |
Content
The course combines lectures, articles, an introduction to using the software (in particular SPSS, R) and the analysis of real data by the students themselves. A theoretical and methodological framework is provided to promote student activity in the analysis and interpretation of data.
The Rasch and IRT models
The students discover the classical approach (Cronbach's alpha) and the modern approach (Rasch, IRT) through examples of analysis of a quantitative questionnaire. They will also discover the psychometrical foundations of scaling involved in interpreting answers to a questionnaire (unidimensionality criterion, fit indices, differential functioning, dichotomous and polytomous item analysis).
Factor analysis
The postulates and implications of exploratory and confirmatory factor analysis models. Common practice and specifici procedures (eg: rotations, parallel analysis...) as well as technical difficulties.
Common applications of the procedures and their software imlmentation with a critical approach to tjeresults, fit, and interpretation.
The Rasch and IRT models
The students discover the classical approach (Cronbach's alpha) and the modern approach (Rasch, IRT) through examples of analysis of a quantitative questionnaire. They will also discover the psychometrical foundations of scaling involved in interpreting answers to a questionnaire (unidimensionality criterion, fit indices, differential functioning, dichotomous and polytomous item analysis).
Factor analysis
The postulates and implications of exploratory and confirmatory factor analysis models. Common practice and specifici procedures (eg: rotations, parallel analysis...) as well as technical difficulties.
Common applications of the procedures and their software imlmentation with a critical approach to tjeresults, fit, and interpretation.
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Lectures, readings, demonstrations
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Written exam with multiple choice and/or open questions according to the sections.It is required to pass succesfully both parts of the course.
Other information
Either this course or Data Analysis: Prediction Models is a prerequisite the the Advanced Workshop of methods and analysis
The present course requires knowledge of basic concepts and methods in statistics and classical psychometrics. Namely
LPSP1011 Statistique : Analyse descriptive de données quantitatives
LPSP1209 Statistique, inférence sur une ou deux variables
LPSP1212 Psychométrie
The present course requires knowledge of basic concepts and methods in statistics and classical psychometrics. Namely
LPSP1011 Statistique : Analyse descriptive de données quantitatives
LPSP1209 Statistique, inférence sur une ou deux variables
LPSP1212 Psychométrie
Online resources
Check Moodle
Faculty or entity
EPSY
