BMET 26A03 - Measurable social phenomena: understanding and applying quantitative methods

What can quantitative methods reveal about the social world, and what do they leave out? Quantitative methods have been central to the social sciences since their inception. Since the mid-20th century, statistical modelling and significance testing have become particularly dominant tools for social researchers. These approaches expand the scale, reliability, generalisability, and reproducibility of social research. At the same time, the growing reliance on quantification in social research reflects a broader shift in knowledge production. Although quantitative evidence is often treated as objective, its reliability is contingent on the conscientious and reflexive application of methodologies used to produce it. Yet, the technical complexity and specialized language of quantitative research often make it difficult to critically engage with their application to social questions. The aim of this workshop is to familiarise students with a range of quantitative approaches used in the social sciences - from ‘mere description’ to hypothesis testing. Through in-class workshops and critical reading of with -reviewed articles, students will engage with quantitative research on its own terms, learn how to evaluate the strengths and weaknesses of quantitative designs, to source and manipulate data, to develop their own research questions and to understand how to match research questions with methodological choices with data sources. The aim is therefor to both develop students’ practical ability to apply quantitative methods (in the R programming language), and their ability to effectively scrutinize the application of data and quantitative methods.

DESCRIPTIF SEANCES DU COURS:

There are no prior requisites for this class. Students will be asked to install R and R studio at the start of the semester. Although introductory R will be covered in the class, those interested in getting a head start might consider exploring Garrett Grolemund’s ‘Hands-On Programming with R’: https://rstudio-education.github.io/hopr/starting.html#starting .

This workshop will consist mostly of in-class work - discussion of peer reviewed articles as a means of creating critical engagement with and understanding of quantitative methods, collective work to develop a project proposal and practical labs to develop competencies in the R programming language.

50% class participation – students will be expected to be actively engaged in class discussion and to send memos each week with brief comments on the required readings.

35% Research proposal and dataset report – 3000-word research design, focused primarily on description of a data source, the relevant variables and their applicability to a specified research question (completed in groups of 2 or 3).

15% final project presentation (completed in groups of 2 or 3).

Students will be expected to read one to two articles per week. They will be expected to comment on these articles in the form of bullet points (no more than 1 page) and to be prepared to engage in discussion regarding these articles in class. Over the course of the semester, they will also be expected to work in groups to develop a quantitative research proposal for which they are expected to source appropriate data.