DRE 1019 Digital Research Methods
DRE 1019 Digital Research Methods
Broadly defined digital research methods refer to computer-assisted tools and ways to research the internet. They include a wide array of software and appliances that give access to digital communities and transactions. This course aims to provide participants with an updated understanding of the specifics of digital research methods in two ways. First, some parts of the course have an introductory nature in the sense of providing an overview of the digital domain at large, including recent points of debate and controversy with regards to the researcher’s participation and validity and reliability of data. Second, other parts of the course will be more attuned to experimentation with digital research methods to learn by doing and assessing the method’s relative merit for the participants’ own PhD/research projects.
At the end of the course, the students should have developed
Knowledge of the special characteristics of key methods in digital research
Knowledge of strengths and weaknesses of key digital research methods
Knowledge of the variety of epistemological perspectives informing digital research methodologies
Knowledge of ethical aspects of the uses of digital research methods
The ability to choose an appropriate digital research method to answer a specific research question
The ability to identify research gaps by utilizing digital research methods to create new knowledge
The ability to design and conduct research using digital research methods
The ability to evaluate the validity and reliability of the empirical findings
The ability to assess the practical aspects of the uses of various digital research methods
The ability to think holistically about epistemological fault lines and one’s own positionality
Develop forward-thinking, creative capabilities
Data- and theory-driven reasoning
Session 1: Introduction to the course and digital research methods
In this session, we will first go through an overview of key digital research methods, criteria for comparing their strengths and weaknesses, and the research journey at large with examples of major challenges when using digital research methods.
There is a mass of digital platforms available, some limit their users to a particular software and hardware, others are open and can be combined with others. In this Introduction part, the students will get a non-exhaustive list of platforms, software and appliances. We will discuss and reflect their usability and possible applications for research projects.
Compulsory reading for session 1: see reading list
Session 2: Online Community Research: Research on “networked public spheres”
Online communities are particularly fruitful research contexts as they allow insight into naturally occurring large-scale online conversations surrounding a wide range of topics. Collecting, structuring, and analyzing such conversations allows researchers, among others, to understand which topics are being discussed, how they develop over time, which actors are driving a conversation, which viewpoints are (un)popular or controversial in a community and which sentiment a specific conversation or response may elicit. During this session, we will discuss how to collect as well as meaningfully structure and analyze large-scale conversation data. Furthermore, we will weigh potential implications and ethical boundaries of those methods.
Compulsory reading for session 2: see reading list
Session 3: Computational communication research: Machine learning for automated content analysis
Texts, audio, and images are used to persuade people and critical in media framing, agenda-setting, and propaganda. Traditionally, researchers adopt content analysis to study text, audio, and image data. One of the most obvious drawbacks of content analysis is the time, effort, and money needed to complete a reliable and valid study. Automated content analysis was developed and advanced by machine learning.
To equip students with advanced computational communication research skills, this session will briefly introduce the concept of machine learning (classification) and its application. In particular, we are interested in using machine learning for automated content analysis. This session will offer an in-depth overview of automated methods for large-scale text, audio and image analysis and explains their usage and implementation. Furthermore, we will weigh potential implications and ethical boundaries of those methods.
Compulsory reading for session 3: see reading list
Session 4: Digital ethnography and humanistic text-mining
With computational social science and digital humanities, large volumes of texts have become available as primary sources for empirical analysis. To research this material, digital ethnography has developed as a distinct methodology. Digital ethnography requires immersion in the online life of people, statements, and objects over an extended period. The research process is data driven. The field is read as a text and findings are communicated as factual narratives. The researcher may be an active participant or observe from the outside but is always a medium through which information passes.
In this session, students will be introduced to digital text-mining tools for understanding narrative conventions and discursive structures. Furthermore, we will weigh potential implications and ethical boundaries of those methods, particularly on the problem of the researcher’s positioning. In lieu of the extended period of study, historical sources will be introduced.
Compulsory reading for session 4: see reading list
Session 5: Digitalizing experiments. Experimenting with wearable as research tools
In the increasing digitalized world people interact with devices and AI for most of their daily tasks. The study of communication behavior is shifting towards digital environments, capturing human-machine interactions, and integrating the use of wearables. This also allows for interesting opportunities to integrate digital tools into experimental designs and methods. Researchers have done so in various ways, by using virtual reality for interventions and measurement of biases, by using eye tracking, or by immersing the participants even more into the digital setting and using wearables to track outcomes. We will discuss the methods and techniques, as well as the potential implications and ethical boundaries of those methods.
Compulsory reading for session 5: see reading list
Session 6: Summary and evaluation
In this session, we will discuss and reflection on our learning through round-table dialogue and presentations of potential applications on research, peer-review session of term paper outlines as the final learning activity.
The course takes place over two modules of in total 6 intensive days over 2 consecutive weeks. It includes a half-day summary session and individual feedback (36 teaching hours).
The course requires high level of investment from all participants. The design necessitates experimentation with various digital research methods, presentations of findings and reflection on the strengths and weaknesses in general and considering the development of a research project. The participants are expected to read and give comments to assigned papers before each day. There will be introductory lectures to all main topics, but the main part of the course will be the participants’ uses and reflection on the application of various digital research methods.
A compendium will be available on the net. An additional reading list for each session will be made available. Articles on google scholar.
Admission to a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of admission to a PhD Programme when signing up for a course with the doctoral administration. Other candidates may be allowed to sit in on courses by approval of the course leader. Sitting in on courses does not permit registration for courses, handing in exams or gaining credits for the course. Course certificates or conformation letters will not be issued for sitting in on courses
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 100 Grouping: Individual Duration: 3 Month(s) Comment: Term paper – 3 months – Pass/fail Exam code: DRE 10191 Grading scale: Pass/fail Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Student's own work with learning resources | 40 Hour(s) | |
Prepare for teaching | 20 Hour(s) | |
Examination | 64 Hour(s) |
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.