Content Analysis

The course will introduce participants to the family of methods known as ‘content analysis’ using a variety of examples from political and other social sciences. The course will cover the basic aspects of content analysis relating to creating coding schemes, document selection, coding, and scaling. Particular attention will be paid to evaluating measurement in the context of content analysis in terms of reliability and validity. The course will cover different approaches to both manual and computer-assisted coding in content analysis, and will be taught in a mix of lectures and seminars. After each lecture, participants will be asked to do some hands-on content analysis exercises (codebooks, textual material, and software where needed will be provided) and the results will be compared, discussed, and extended during the seminars. Prerequisite knowledge Participants are expected to be familiar with basic statistical concepts such as measures of central tendency (mean, median), dispersion (standard deviation), tests of association (Pearson’s r) and inference (χ2, t-test). These material are covered in the first few chapters of introductory statistics or data analysis textbooks. A useful example is Pollock P.H. III, The Essentials of Political Analysis, fourth edition (Washington, DC: CQ Press, 2012), Chapters 2, 3, 6, and 7. Some familiarity with SPSS and Stata statistical software is also desirable. Kostas Gemenis is Assistant Professor of Research Methods at the Department of Public Administration, University of Twente. His research interest include measurement in the social sciences, and content analysis with applications to estimating the policy positions of political actors. He is also currently involved in Preference Matcher , a consortium involving researchers who collaborate in developing e-literacy tools designed to enhance voter education.