Seminar

Uncertainty

Quantitative surveys. Social Science Toolkit

Wednesday 13 November 2024 Wednesday 13 November 2024

From 3 to 6 PM

Danielle Navarro, Generative Art, licence CC BY 4.0

ENS-PSL
45 rue d'Ulm
75005 Paris
France

48.8418371, 2.3440403

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

In 1961, in a building at Yale University, 26 out of 40 people agreed to inflict electric shocks of increasing intensity on a heart patient who begged them to stop. The moans were pre-recorded, the shocks imaginary. It was an experiment that had a huge impact. Let's assume for a moment that these 40 people, recruited in Connecticut in the early 1960s, are representative of the "average human being". What's the probability of a random person in the street doing the same thing? Bayesian statistics allow you to ask such questions, and to answer them precisely. They also allow you to take into account any other information you may have, and to refine your beliefs as you gather more information. Intuitive and fun, this approach is growing fast, and benefits from excellent implementations in R, Python and Julia. In this session, we present the fundamental principles of Bayesian statistics, and introduce Markov Chain Monte Carlo (MCMC) methods in a simple and accessible way.

Prerequisites: A basic knowledge of probability is required to take full advantage of this session. For those who do not, it will suffice to read one or two chapters of the textbook beforehand.

Wednesday 13 November 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