GRA 6141 Research Methodology for Communication and Digitalization

GRA 6141 Research Methodology for Communication and Digitalization

Course code: 
GRA 6141
Department: 
Communication and Culture
Credits: 
6
Course coordinator: 
Suzanne van Gils
Shubin Yu
Course name in Norwegian: 
Research Methodology for Communication and Digitalization
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

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 qualitative 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. 

Learning outcomes - Knowledge

At completion of this course, students should have knowledge of various research methods in the domain of communication and digitalization such as 

  • Experimental research design 
  • Single- and multi- source regression designs 
  • Diary studies 
  • Qualitative analysis – interviews and case studies 
  • Ethnography methods 
  • Network designs 
  • Online platform research 
Learning outcomes - Skills

After this course, students should be able to 

  • Prepare datasets for statistical analysis
  • Apply basic statistical techniques, specifically; 
  • Basic regression analysis 
  • ANOVA, ANCOVA 
  • Moderation and mediation analysis 
  • Multilevel analysis 
  • Understand core considerations for qualitative analysis  
  • Interview 
  • Making code books 
  • Ethnographies 
  • Social Network Analysis 
  • Read and modify r scripts 
  • Understand information harvesting strategies, including search techniques and critical evaluation of sources
General Competence

After the course students should be able to 

  • Critically assess and reflect on various research methods
  • Compare research methods and choose the method best fitting to answer the research question
  • General knowledge goals are:
  • Apply relevant methods, techniques, and tools to evaluate latest communication technologies and their ethical implications to advance business legitimacy and sustainability.​
  • Analyse existing relational, interactional and design theories, methods, and interpretations, and work independently on practical and theoretical communication management challenges. ​
  • Have thorough knowledge of communication dynamics expressed in quantitative and qualitative, offline and online data.​
  • Develop forward-thinking, creative capabilities, and data-driven reasoning.​
  • Arbitrate different opinions about the use and appropriateness of different methods and negotiate solutions between parties.
Course content

The course will cover the following topics (in no particular order) as well as consolidation sessions to practice and reflect on the different skills.

  1. Introduction to the course: Different methods in the field and how to critically assess them
  2. Data preparation and critical design decisions: Data quality, Variable type, Experimental vs. non-experimental designs, Power and reliability, Replications, Code books, Response rates
  3. Experimental statistics: T-tests (comparisons of two groups), Anova, Ancova, Effect sizes 
  4. Regression analysis: Basic: Regression analysis 3, Moderation analysis, Mediation analysis, Use of control variables 
  5. Diary studies and interventions: Diary study methods, Multilevel designs
  6. Library session on paper writing: Systematic literature review
  7. Qualitative analysis: Interviews and coding, Coding at different levels, case study methods
  8. Digital ethnographies and case study methods:
  9. Scraping and analyzing social media/platform data: Platform choice and data selection, Scraping and analysis
  10. Social Network Analysis: Social media, bibliometric representation
Teaching and learning activities

The course will consist of  

  • Lectures including interactive exercises 
  • Weekly excercises with video support that will build up to the portfolio (see examination) 
  • Support workshops when necessary
Software tools
Gephi
Nvivo
Qualtrics
R
R/R-Studio
SPSS
Tableu
Zotero
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

This is a course with a portfolio assessment. The portfolio consists of a selection of weekly assignments, and will be graded in its totality at the end of the course.

All parts of the assessment must be passed in order to get a grade in the course.

The examination for this course has been changed. Continuous assessment will no longer exist as an examination form from autumn 2023. For questions regarding previous results, please contact InfoHub.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 61412
Grading scale:
Pass/fail
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
0No1 Week(s)Group/Individual (1 - 3)Library assignment
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 61413
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
100No 1 Semester(s)Individual Portfolio assessment. A paper consisting of exercises that are completed throughout the semester.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:0
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration:1 Week(s)
Comment:Library assignment
Exam code:GRA 61412
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:No
Grouping (size):Individual
Duration: 1 Semester(s)
Comment:Portfolio assessment. A paper consisting of exercises that are completed throughout the semester.
Exam code:GRA 61413
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
24 Hour(s)
Lectures
Student's own work with learning resources
48 Hour(s)
Readings and instructional videos
Examination
82 Hour(s)
Writing a portfolio consisting of exercises and reflections corresponding to the weekly class topics, as well as improving the writing based on lecturer feedback
Sum workload: 
154

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.