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
Term 2024L: | Term 2025L: |
Learning outcomes
Knowledge (W; in Polish: “wiedza”) (the graduate knows and understands)
WG_01 to the extent enabling the revision of existing paradigms - the world’s achievements relating to theoretical
foundations as well as general and selected specific issues - relevant to a particular discipline within the social
sciences
WG_02 the main scientific developments in the disciplines of the social sciences in which the education is provided
WG_03 the methodology of scientific research in the field of the social sciences
WK_01 fundamental dilemmas of modern civilisation from the perspective of the social sciences P8S_WK
WK_02 the economic, legal, ethical and other essential conditions of conducting scientific research in the field of the
social sciences
Skills (U; in Polish: “umiejętności”) (the graduate is able to)
UW_01 Take advantage of knowledge from different academic fields, in particular the social sciences to creatively
identify, formulate and innovatively solve complex problems or perform research tasks, especially:
− define the aim and subject of scientific research in the field of the social sciences, formulate a research
hypothesis,
− develop research methods, techniques and tools and use them creatively,
− Draw conclusions on the basis of research results
UW_02 critically analysing and evaluating the research results within the social sciences, of expert activities and other
creative work and their contribution to the development of knowledge
Social competences (K; in Polish” “kompetencje społeczne”) (the graduate is ready to)
KK_01 critically evaluate achievements within a given scientific discipline in the field of the social sciences
KK_02 critically assess one's own contribution to the development of a scientific discipline
KK_03 recognise the value of knowledge in solving cognitive and practical problems within a specific discipline in the
field of the social sciences
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: