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Quantitative and Qualitative Tools for the Innovation Driven Study of Politics
Study Course Description
Course Description Statuss:Approved
Course Description Version:1.00
Study Course Accepted:22.02.2024 11:29:35
| Study Course Information | |||||||||
| Course Code: | SZF_071 | LQF level: | Level 6 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Political Science | Target Audience: | Political Science | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Toms Rātfelders | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Faculty of Social Sciences | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Dzirciema street 16, Rīga, szf rsu[pnkts]lv | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 11 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 22 | ||||
| Classes (count) | 4 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 8 | ||||
| Total Contact Hours | 30 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | No prior knowledge required. A minimum knowledge of Stata and R software will help in the study process. | ||||||||
| Objective: | The objective of the course is to introduce students to the latest tools and methods used worldwide in political science research to help them with their course papers and Bachelor’s thesis. Given the increasing emphasis of political science research on the use of mixed methods, the course is designed as a composite of two parts – quantitative and qualitative – while emphasising the compatibility of the two categories of methods in answering current questions in politics. The course will cover qualitative research methods such as interviews, process tracing, qualitative comparative analysis (QCA), content, content/discourse analysis, social network analysis and ethnography. This will be complemented by quantitative methods such as bivariate/multivariate regression analysis, automated text analysis and experiments. The course will also develop students’ ability to think like scientists by introducing the principles of independent and dependent variables, good evidence selection practices, causal relationships (and how to establish them), and how to limit the influence of personal beliefs on research results. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction. How do scientists see the world? | Lectures | 1.00 | auditorium | |||||
| 2 | Dependent and independent variables: what are they, and why are they important? | Lectures | 1.00 | auditorium | |||||
| 3 | Causality and causal mechanisms: Process tracing method | Lectures | 1.00 | auditorium | |||||
| 4 | Theory in the research process - why is it needed? | Lectures | 1.00 | auditorium | |||||
| 5 | Presentation: Variables, causality and hypothesis in course paper | Classes | 1.00 | auditorium | |||||
| 6 | Good data collection practice: methods of interview and textual content analysis, work with archives | Lectures | 1.00 | auditorium | |||||
| 7 | Risks of researcher positionality and personal beliefs: the ethnographic method and field research | Lectures | 1.00 | auditorium | |||||
| 8 | Presentation: potential primary and secondary sources in the course paper | Classes | 1.00 | auditorium | |||||
| 9 | Case analysis, small sample and medium sample research: Case studies vs. Qualitative Comparative Analysis (QCA) | Lectures | 1.00 | auditorium | |||||
| 10 | Large sample research: statistical methods – bivariate and multivariate regression | Lectures | 1.00 | auditorium | |||||
| 11 | Large sample research: automated text analysis | Lectures | 1.00 | auditorium | |||||
| 12 | Presentation: selection of cases to analyse in the course paper | Classes | 1.00 | auditorium | |||||
| 13 | Social networks as an alternative perspective on political processes | Lectures | 1.00 | auditorium | |||||
| 14 | The role of ethics in the research process: experiments in political science | Lectures | 1.00 | auditorium | |||||
| 15 | Presentation: Early draft of the course paper | Classes | 1.00 | auditorium | |||||
| Assessment | |||||||||
| Unaided Work: | As part of the course, students are required to submit a number of pieces of independent writing that relate the knowledge they have acquired to the elements of the course paper developed. These papers will then be presented to other students, who will have the opportunity to express their criticism, suggestions and ask questions. Students’ knowledge and skills will also be tested through a series of written assignments that are not directly related to the topic of the course paper, but aim to develop technical skills in the application of a specific research method. For example, to learn bivariate and multivariate regressions, students will need to develop their own code in Stata software and be able to read and interpret the results in written form. On top of all this, students are also expected to read the required readings for each lecture. To assess the overall quality of the study course, the student must complete the course evaluation questionnaire on the Student Portal. | ||||||||
| Assessment Criteria: | The quality of students’ work will be assessed according to a number of criteria: 1) Active participation and involvement in both lectures and seminars (10%); 2) Presentation skills and suggestions for improving other students’ course papers (20%); 3) Quality of the written work – 1) ability to comply with the volume limit of the assignment (20%), 2) ability to develop the research elements of the assignment (50%), 3) integration of the literature read in the course (30%) (total of 40% of the final assessment); 4) Quality of the early draft of the course paper – 1) ability to comply with the volume limit (20%), 2) ability to develop the research elements of the assignment (30%), 3) integration of the literature read in the course (30%), 4) listening to the advice of the course leader and course mates (20%) (total of 30% of the final assessment). | ||||||||
| Final Examination (Full-Time): | Exam | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | As a result of the course, students will become more familiar with the political science literature and recognise the research methods used by researchers. They will be able to understand the basic principles on which the author’s arguments have been based, and potentially find possible research gaps. Students will also gain knowledge about the prerequisites for high-quality scientific research and learn to distinguish between inconclusive and conclusive evidence. They will also gain an understanding of the role of theory in explaining political issues and potentially begin to assess the impact of their positionality and personal views on the scientific results produced. | ||||||||
| Skills: | During the course, students will acquire first skills in the use of statistical software (Stata and R) and, through practical exercises, will learn to integrate methods into potential research projects at a basic level. Regular presentations will also help to develop the skills to present research results in front of a large audience. In addition, through peer discussion, students will have learned at a basic level how to provide constructive scientific criticism and advice on improving the research of others. | ||||||||
| Competencies: | Students will be able to orient in the main standards of political science, which will support them in developing both their course paper and Bachelor’s thesis. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Visa literatūra ir angļu valodā un piemērota gan latviešu, gan angļu plūsmas studentiem | ||||||||
| 2 | Bennett, Andrew, and Jeffrey T. Checkel, eds. 2015. Process Tracing: From Metaphor to Analytic Tool. Cambridge: Cambridge University Press. | ||||||||
| 3 | Kapiszewski, Diana, Lauren M. MacLean, and Benjamin L. Read. 2015. Field Research in Political Science: Practices and Principles. Cambridge: Cambridge University Press. | ||||||||
| 4 | Mosley, Layna, ed. 2013. Interview Research in Political Science. Ithaca, NY: Cornell University Press. | ||||||||
| 5 | Ragin, Charles C. 2008. Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. | ||||||||
| 6 | Schatz, Edward. 2013. Political Ethnography: What Immersion Contributes to the Study of Power. Chicago: University of Chicago Press. | ||||||||
| 7 | Emilie Hafner-Burton, Miles Kahler and Alexander H. Montgomery, “Network Analysis for International Relations,” International Organization 63, no. 3 (2009). | ||||||||
| 8 | Lobasz, Jennifer K. 2008. "The Woman in Peril and the Ruined Woman: Representations of Female Soldiers in the Iraq War." Journal of Women, Politics, and Policy (formerly Women and Politics) 29:305-334 | ||||||||
| 9 | Jennifer Milliken. 1999. "The Study of Discourse in International Relations: A Critique of Research and Methods." European Journal of International Relations 5 (2):225-254 | ||||||||
| 10 | Agresti, Alan and Barbara Finlay. 2018. Statistical Methods for the Social Sciences. 5th edition. Pearson/Prentice-Hall | ||||||||
| 11 | Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text as Data: A New Frame-work for Machine Learning in the Social Sciences. Princeton, NJ: Princeton University Press. | ||||||||
| 12 | Gary Goertz and James Mahoney. 2012. A Tale of Two Cultures. Qualitative and Quantitative Research in the Social Sciences. Princeton: Princeton University Press. | ||||||||
| 13 | The Oxford Handbook of Political Methodology. 2008. ed. Janet M. Box- Steffensmeier, Henry E. Brady, and David Collier. Cambridge, UK: Cambridge University Press | ||||||||
| 14 | Stephen Van Evera. 1997. Guide to Methods for Students of Political Science. Ithaca: Cornell University Press | ||||||||
