Methodology for Communication and Digitalization Research
Solid methods are the foundation for solid science. In this course we focus not only on how to conduct research, but also increase your awareness of why certain methods and tests are relevant, and how they relate to your research question. The course will help you reflect on the different qualitative and quantitative methods that are most commonly used in digital communication research. You will learn how to choose the method best fitting to your research question and design. We will develop a portfolio that you can use as a resource when conducting research projects in other courses, or in your thesis.
The course will cover most of the basic statistical analyses, starting with data handling and recoding, and covering experimental statistics (ANOVA, ANCOVA) and statistics fitting survey research (regression). In addition to the quantitative approach, this course will also cover qualitative research methods such as interview and coding, and analysis of digital and social network data. Upon completion of this course, students will be able to conduct quantitative and qualitative research independently.
- Introduction to the course: Different methods in the field and how to critically assess them
- Data preparation and critical design decisions: Data quality, Variable type, Experimental vs. non-experimental designs, Power and reliability, Replications, Code books, Response rates
- Experimental statistics: T-tests (comparisons of two groups), Anova, Ancova, Effect sizes
- Regression analysis: Basic: Regression analysis 3, Moderation analysis, Mediation analysis, Use of control variables
- Diary studies and interventions: Diary study methods, Multilevel designs
- Consolidation session 1: What test in what situation? Recap
- Library session on paper writing: Systematic literature review
- Qualitative analysis: Interviews and coding, Coding at different levels, case study methods
- Digital ethnographies and case study methods:
- Scraping and analyzing social media/platform data: Platform choice and data selection, Scraping and analysis
- Social Network Analysis: Social media, bibliometric representation
- Consolidation session 2. Recap
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.