lsped2047  2019-2020  Louvain-la-Neuve

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.
5 credits
20.0 h + 20.0 h
Q1
Teacher(s)
Masquelier Bruno; Rautu Iulia (compensates Masquelier Bruno);
Language
French
Content
LSPED2047 provides a solid introduction to quantitative methods in the social sciences. At the end of this course, students will be able to 
  • to acquire mastery of the tools of bivariate and multivariate quantitative data analysis.
  • use single and multiple regression methods and some applications of generalized linear models (logistic regression and Poisson regression)
  • understand and be able to use factorial analysis and classification techniques (also called cluster analysis)
  • to be autonomous in the use of R, a free software for data analysis.
Topics covered:
  • Univariate analysis (reminders): to describe the data.
  • Chi-square, relative risks, odds ratios: to analyze jointly two qualitative variables.
  • T-Test, F-test and ANOVA: to test the relationships between a qualitative and a quantitative variable. 
  • Correlations, simple linear regression: to analyze jointly two quantitative variables 
  • Factorial analyses: principal component analysis (PCA) for quantitative variables and Multiple Correspondence Analysis (MCA) for qualitative variables: to construct indicators or identify 'latent' dimensions of all the variables analysed.
  • Classification methods (Wald's hierarchical classification): to identify clusters of observation units or to develop typologies.
  • Multiple linear regression and the generalized linear model (logistic regression and Poisson regression): to predict the value of a dependent variable, and identify its determinants.
Teaching methods
The course is structured around lectures and practical work (see programme distributed in the first session and on Moodle). Participation in courses and partical sessions is essential. It is necessary to read chapters from the curriculum beforehand.
Evaluation methods
  •  A dispensatory test allowing students to evaluate their command of the R software is scheduled during the semester. 
  • Participation in three exercises associated with practical work is marked.
  • The final evaluation is also based on a written exam taken in the computer room during the exam session.
Online resources
Logiciel R: https://www.r-project.org/
Inferface Rstudio: https://www.rstudio.com/
Bibliography
G. Masuy-Stroobant and R. Costa, editors. Analyser les données en sciences sociales : De la préparation des données à l'analyse multivariée. P.I.E. Peter Lang, 2013.
D.C. Howell, V. Yzerbyt, Y. Bestgen, and M. Rogier. Méthodes statistiques en sciences humaines. Série Internationale. De Boeck Supérieur, 2008.
 
Teaching materials
  • Les supports de cours, ainsi que la bibliographie complémentaire se trouvent sur la page Moodle du cours.
  • G. Masuy-Stroobant and R. Costa, editors. Analyser les données en sciences sociales : De la préparation des données à l'analyse multivariée. P.I.E. Peter Lang, 2013. Disponible en bibliothèque et sur Moodle (certains chapitres)
Faculty or entity


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [60] in Sociology and Anthropology

Master [120] in Sociology

Master [120] in Population and Development Studies

Master [120] in Political Sciences: General

Master [120] in Education (shift schedule)

Mineure en statistique et science des données

Advanced Master in Quantitative Methods in the Social Sciences