MVZ016 Research Methods in Political Science III

Faculty of Social Studies
Autumn 2018
Extent and Intensity
1/1/0. 2 credit(s). Recommended Type of Completion: z (credit). Other types of completion: zk (examination).
Teacher(s)
doc. Mgr. Petr Ocelík, Ph.D. (lecturer)
Juraj Medzihorský, M.A. (lecturer)
Mgr. Miroslav Nemčok, Ph.D. (assistant)
Guaranteed by
prof. PhDr. Vít Hloušek, Ph.D.
Department of International Relations and European Studies – Faculty of Social Studies
Contact Person: doc. Mgr. Petr Ocelík, Ph.D.
Supplier department: Department of International Relations and European Studies – Faculty of Social Studies
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20
fields of study / plans the course is directly associated with
Course objectives
This course provides a concise hands-on introduction to computer assisted text analysis for social scientists. The participants will learn how to automate document collection and processing, scale text using dictionaries and dimensionality reduction techniques, and use machine learning techniques to automate text annotation. The course relies on the R language.
Learning outcomes
The participants will learn how to automate document collection and processing, scale text using dictionaries and dimensionality reduction techniques, and use machine learning techniques to automate text annotation.
Syllabus
  • Day 1:
  • automated text collection from web pages and pdf documents in R
  • text preprocessing for analysis in R
  • text scaling based on dictionaries
  • Day 2:
  • text scaling based on correspondence analysis, Wordfish, and Wordscores
  • Text categorization by machine learning techniques and by mixed-models
  • Course information will be updated in the syllabus
Literature
  • Lowe, W. (2016). Scaling things we can count. Available online.
  • Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297.
  • KRIPPENDORFF, Klaus. Content analysis : an introduction to its methodology. 3rd ed. London: SAGE, 2013, xiv, 441. ISBN 9781412983150. info
Teaching methods
lectures, workshops, assignments
Assessment methods
assignments grading
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: in blocks.
The course is also listed under the following terms Autumn 2017.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fss/autumn2018/MVZ016