Seminar

Indexes

Quantitative surveys. Social Science Toolkit

Wednesday 4 December 2024 Wednesday 4 December 2024

From 3 to 6 PM

Danielle Navarro, Generative Art, licence CC BY 4.0

ENS-PSL, salle de conférences du Centre Sciences des Données

ENS-PSL
45 rue d'Ulm
75005 Paris
France

48.8418371, 2.3440403

Seventh session of the course "Enquêtes quantitatives. Boîte à outils pour sciences sociales", given by Théo Boulakia.

You've built a model. It's certainly wrong (they all are). But maybe it's useful. The aim of this session is to give you a number of keys to answer these two questions: How wrong is my model? Does it serve a purpose? By models, we mean the more or less complicated machines that statisticians build to make predictions based on a set of clues. The social sciences, which are less concerned with predictions, more commonly use models to test the existence of a causal link between two phenomena. Building on the previous session, we will present Bayesian versions of simple models, for regression tasks (number of people who will vote for X in the next election) and classification tasks (probability that X will be elected). We'll show how to evaluate the quality of the predictions made by these models.

Wednesday 4 December 2024

Quantitative surveys. Social Science Toolkit

S1 2024-2025.

Data analysis course in social sciences, delivered by Théo Boulakia. The sessions take place on Mondays, from 3 p.m. to 6 p.m., in the conference room of the Data Science Center (ENS-PSL, 45 rue d'Ulm, at the top of stairs B or C).

Goals of the course: This course offers an introduction to various quantitative methods based on social science surveys. Each session is organized around the meeting between a question (sociological, anthropological, historiographical), data and a method: cartography, dimensionality reduction, partitioning, sequence analysis, textual analysis, Bayesian statistics. The objective of the course is to acquire a schematic understanding of the implementation of these methods, their merits and their limitations. How to represent spatial data and temporal sequences? What do Bayesian statistics provide that the frequentist approach lacks? How to analyze the morpho-synthactic properties of a text? How to go from a large number of variables to a small number of classes? These questions will arise in context, in a dynamic of adjustment between data, method and research question (an investigation dynamic). Programming questions will be covered only in broad terms, no experience in this area is required.

Validation: Submit a four-page document applying one of the methods discovered in progress to your own data: presentation of the data, interest of the method, implementation and interpretation. People without programming experience will be assisted with implementation.

Registration : theo.boulakia [a] ens.psl.eu