(in Polish) Dane makrospołeczne w badaniach porównawczych

General data

Course ID:	3502-FAKL844-OG
Erasmus code / ISCED:	14.2 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0314) Sociology and cultural studies The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	(unknown)
Name in Polish:	Dane makrospołeczne w badaniach porównawczych
Organizational unit:	Institute of Sociology
Course groups:	General university courses General university courses in the social sciences
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Type of course:	general courses
Prerequisites (description):	Knowledge of basic statistics and SPSS needed. Fluent English required for reading the codebook instructions. Basic experience in using SPSS and Excel is highly appreciated.
Short description:	The purpose of this course is to enable students to get experience using widely available datasets of social data and providing ways of utilizing them for writing their M.A. thesis. The course includes presentation of the data collections, overlook of their underlying assumptions, structure and content. Students will be encouraged to create their own datasets for quantitative research by finding ways for coding qualitative data. The course program includes also basic statistical method needed to analyze such data.
Full description:	The purpose of this course is to enable students to get experience using publically available datasets of social data and providing ways of utilizing them for writing their M.A. thesis. The course includes presentation of the data collections, overlook of their underlying assumptions, structure and content. Following data collections will be used during the course: Quality of Government, Comparative Political Dataset, World Bank Development Indicators, Comparative Manifesto Project. Students will be encouraged to create their own datasets for quantitative research by finding ways for coding qualitative data. The course program includes also basic statistical method needed to analyze such data such as interaction effects and lagged variables. Special attention will be devoted to indicators used for quantitative research in political sociology for party system research.
Bibliography:	Basic literatere includes codebooks of datasets and the following: • Brambor, Thomas, William Roberts Clark, Matt Golder. 2006. Understanding Interaction Models: Improving Empirical Analyses. „Political Analysis” 14: 63–82. • Jaccard, James, Robert Turrisi. 2003. Interaction Effects in Multiple Regression. Second Edition. Thousand Oaks, London, New Delhi: Sage Publications Inc, p.1-43. • Tavits, Margit, Natalia Letki. 2014. From Values to Interests? The Evolution of Party Competition in New Democracies. „Journal of Politics” 76 (1): 246-258. • Lissowski, Grzegorz, Jacek Haman, Mikołaj Jasiński. 2011. Podstawy statystyki dla socjologów. Zależności statystyczne. Tom 2. Warszawa: Wydawnictwo Naukowe Scholar, p. 158-161, 216-221. • Markowski, Radosław. 2003. Propozycja „Manifesto Research Group”: Metoda, wyniki, problemy – komentarz”. In: Radosław Markowski - (ed.), System partyjny i zachowania wyborcze Dekada polskich doświadczeń”. Warszawa: Instytut Studiów Politycznych PAN, Friedrich Ebert Stiftung • Metcalf, Lee Kendall. 2000. Measuring Presidential Power. „Comparative Political Studies” 33(5): 660-685. • Powell, Eleanor N., Joshua A. Tucker. 2013. Revisiting Electoral Volatility in PostCommunist Countries: New Data, New Results and New Approaches. „British Journal of Political Science” 44 (1): 123-147. • Taagepera, Rein, Bernard Grofman. 2003. Mapping The Indices of Seats–Votes Disproportionality and Inter-Election Volatility. „Party Politics” 9(6): 659-677.
Learning outcomes:	K_W12 Has in-depth knowledge of selected methods and techniques of social research, their limitations, specificity and areas of application K_W13 Is aware of the importance of a reflective and critical approach to the results of social research, analyses and research procedures K_W14 Knows how to plan and carry out complex qualitative and quantitative empirical research; is aware of the consequences of methodological choices K_W15 Has advanced and practically applicable knowledge about statistical description and inference K_U05 Can independently form and verify judgments about the causes of selected social phenomena K_U06 Can use theoretical categories and research methods in the description and analysis of social and cultural changes in modern societies, as well as their consequences K_U09 Can select and adjust proper research methods and techniques (including advanced ones) to perform an analysis of a particular social problem K_U10 Can plan and carry out a social study using advanced quantitative and qualitative methods and techniques of social research K_U11 Can interpret a social phenomenon using advanced statistical methods K_U12 Can use a selected computer program for data analysis, including its advanced functions K_K05 Can gather, find, synthesize and critically assess information about social sciences K_K06 Can argue a thesis using scientific evidence K_K14 Takes responsibility for planned and performed tasks
Assessment methods and assessment criteria:	Preparation of one’s own dataset accompanied with a codebook or writing a report based on macro data analysis. Class attendance required.

This course is not currently offered.

Course descriptions are protected by copyright.
Copyright by University of Warsaw.