Excerpt from course description

Research Methodology for Business Communication

Introduction

Please note that this is a preliminary course description. The final version will be published in June 2026.

Solid methods are the foundation for solid science. The purpose of empirical research in the social sciences in general is to build theories that help us to understand the world. The purpose of empirical research is to understand the myriad issues that pertain to the survival and success of organizations. Good research is both theoretically interesting and persuasive. Persuasiveness depends on whether the empirical evidence presented in the research convinces readers that the author’s arguments and interpretations are likely to be valid.

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. 

Course content

This course consists of different modules

  • 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, Validity and reliability, Replications, Code books, Response rates)
  • Module on quantitative analysis and experiments (shared 3 sessions) Experimental setup, scenario and parallel design, AB testing, t-tests (comparisons of two groups), Anova, Ancova, Effect sizes. Regression analysis, Moderation analysis, Mediation analysis, Use of control variables 
  • Module on qualitative analysis (1-2 sessions) including: Interviews and coding (grounded theory, Coding at different levels), Digital ethnographies (roles and responsibilities of the researcher, different levels of engagement and integration in the community),
  • Module on machine learning and advanced textual analysis (shared 3-4 sessions) introduction to text analysis, large language modeling, network analysis – See description in Research Methodology for Entrepreneurship & Innovation
  • Advanced topics (to be integrated) Diary studies and interventions (Diary study methods, Multilevel designs), Critical research design discussion,

Disclaimer

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.