Intoduction to advanced statistical methods in discipline of psychology 1600-SZD-N-WZMS-PS
The course introduces key concepts related to regression analysis in social sciences. It identifies common pitfalls and misuses of statistics and discusses the remedies. Among others, it touches upon causal interpretation of regression results, interaction analysis and quasi-experimental reasoning. As such, the course provides PhD students with knowledge pre-requisite for the core applied course in advanced statistical analysis (Advanced statistical methods in psychology)
Type of course
Course coordinators
Learning outcomes
Knowledge: Knows and understands:
Assumptions behind the ordinary least squares regression model
Threats stemming from violation of the aforementioned assumptions
Threats to causal interpretation of regression results (e.g. causality loop)
Methods of establishing causality through a broadly conceived regression analysis (e.g. instrumental variables, synthetic controls)
Pitfalls of interaction analysis
Skills: Can:
Critically discuss strengths and weaknesses of particular advanced statistical analyses
Social competences: Is ready to:
No particular social competences offered
Assessment criteria
Description of requirements related to participation in classes, including the permitted number of explained absences; 2 unexcused absences
Principles for passing the classes and the subject (including resit session); participation in classes, written work
Methods for the verification of learning outcomes; Active participation in classes, written project
Evaluation criteria: active participation in classes, written work
Bibliography
Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.
Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political Analysis, 14(1), 63-82.
Gelman, A., & Weakliem, D. (2009). Of beauty, sex and power: Too little attention has been paid to the statistical challenges in estimating small effects. American Scientist, 97(4), 310-316.
Górecki, M. A., & Kukołowicz, P. (2018). Electoral formula, legal threshold and the number of parties: a natural experiment. Party Politics, 24(6), 617-628.
King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 666-687.
Kirk, D. S. (2009). A natural experiment on residential change and recidivism: Lessons from Hurricane Katrina. American Sociological Review, 74(3), 484-505.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: