# Data analysis: measure patterns

lpsys2144  2019-2020  Louvain-la-Neuve

Data analysis: measure patterns
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
6 credits
45.0 h + 15.0 h
Q1
Teacher(s)
Caesens Gaëtane; Grégoire Jacques; Penta Massimo (compensates Grégoire Jacques); Penta Massimo;
Language
French
Main themes
Item response models, particularly the Rasch model, for the construction of measurement scales
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

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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.
Teaching methods
Evaluation methods
Multiple choice and/or open questions according to the sections.
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
Online resources
Check Moodle
Faculty or entity
EPSY

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Psychology

Master [120] in Statistic: General

Master [120] in Education (shift schedule)