Methods in quantitative sociology

Objective

The objective of this course is to provide hands-on presentation of advanced statistical methods popular in the social sciences and sociology in particular. The course consists of three modules: each addresses a specific methodology in an interactive and practical manner, with focus on the analysis of empirical data using specialized software.

Upon completion of this course, students should be able to:

• comprehend the utilization of the aforementioned methods in sociological articles, evaluate their contributions and limitations, and
• implement the aforementioned methods in a pertinent manner using empirical data.

Planning

1.    Multinomial models (2*3h): These models allow the study of hierarchical data (e.g., students in classes, patients in hospitals), taking into account the influence of contextual effects on the phenomena studied, as well as the effect of individual characteristics.
2.    Latent class analysis (LCA) and mixture models (3*3h): a comprehensive family of probabilistic approaches to classification and for the statistical treatment of preliminary hypotheses on complex data.
3.    Spatial Data and Mapping (3*3h): This module will introduce students to tools (QGIS or ArcGIS, R, Python) to gather, process, and visualize spatial data for sociological research. Example data and exercises may include mapping and analyzing Airbnb properties, local businesses, gentrification, and/or ethnoracial composition.

References

Di Prete TA, Forristal JD, 1994, “Multilevel Models: Methods and Substance”, Annual Review of Sociology, 20, p. 331-357.
Hastie, T.J.; Tibshirani, RJ; Friedman, JH, 2009, The Elements of Statistical Learning, Springer.
Magidson Jay, Vermunt Jeroen, 2004. “Latent class models,” In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 175–198). Newbury Park, CA: Sage.
Muthén Bengt, 2008, “Latent variable hybrids: Overview of old and new models”, In Hancock, GR, & Samuelsen, KM (Eds.), Advances in latent variable mixture models (pp. 1-24).
Snijders TA, Bosker J., 1999, Introduction to Multilevel Analysis, London, Sage.