SO410 or a Short Introduction to Quantitative Social Research (Summer School) or and equivalent introduction to quantitative research (to the level of basic (OLS) regression)
OverviewThis module aims to develop basic quantitative research skills (to the level of regression) to understand more advanced issues in making causal claims. Learning will be oriented towards:
This module appears in:
22 hours over 11 weeks, normally 1 hour lecture and 1 hour seminar or a 2 hour data analysis workshop
Method of assessment
100% coursework (10% class participation, 35% group presentation, 55% Report)
Cartwright, Nancy (2013), 'Knowing what we are talking about: why evidence doesn't always travel'. Evidence & Policy: A Journal of Research, Debate and Practice, Volume 9, Number 1, pp. 97-112.
Christenfeld, N., R. Sloan, et al. (2004). "Risk factors, confounding, and the illusion of statistical control." Psychosomatic Medicine 66: 868-875.
Cook, T., & Campbell, D. (1979) Quasi-experimentation: Design and analysis issues for field settings. Rand McNally College Publications.
Hedström, P and Ylikoski, P, (2010). 'Causal Mechanisms in the Social Sciences'. Annual Review of Sociology, 36:49-67. DOI: 10.1146/annurev.soc.012809.102632
Jackson, M and Cox, DR (2013), 'The Principles of Experimental Design and Their Application in Sociology'. Annual Review of Sociology, Vol. 39: 27-49.
Morgan, SL and Winship, C (2007), Counterfactuals and Causal Inference: Methods and Principles for Social Research.
Shadish, William R., Thomas D. Cook and Donald T. Campbell. 2002. Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston, MA: Houghton-Mifflin.
On successfully completing the module students will be able to:
1 Critically understand the limitations of simple regression when making causal claims, with particular attention to endogeneity/confounding and causal heterogeneity;
2 Critically understand the strengths and limitations of more advanced methods for investigating causality through quantitative research (e.g. experiments, instrumental variable approaches, matching methods, longitudinal analysis);
3 Demonstrate a basic ability to themselves apply these more advanced methods for investigating causality, using appropriate statistical software (e.g. Stata);
4 Demonstrate an ability to select the most appropriate design for investigating causality in real-world settings, given practical constraints;
5 Demonstrate an ability to critique causal claims made in public debates and in academic research;
6 Demonstrate an ability to present the rationale and results of more advanced statistical methods for investigating causality to non-technical audiences.