Introduction to Digital Trace Data: Quality, ethics, and analysis (paid version for the professional)
Beschrijving
Course goals
Introducton to digital trace data provides students with foundational knowledge in digital behavioral data, focusing on collecting, analyzing, and interpretating such data. The course emphasizes various data types and methodologies, and the implications that data and algorithmic biases play on reinforcing inequalities.
Course aims:
a. Students develop fundamental knowledge and understanding of digital data collection and analysis (Knowledge and Understanding)
b. Students apply their knowledge in a multi-disciplinary context to contemporary problems (Applying)
c. Students are able to judge how data and algorithmic biases can affect study results (Judgment)
d. Students are able to determine the most effective research method(s) to address a research problem (Applying)
e. Students are capable of autonomous and responsible scholarly self-development (Learning skills)
f. Students are able to identify and critically evaluate ethical dilemmas (Judgment)
g. Students are able to present on research findings and insights to specialist and non-specialist audiences clearly and unambiguously in English (Communication)
Relation between Assessment and Objective
In a series of (group) assignments, students show their ability to collect and analyze digital behavioral data, and critically assess its quality and interpret the findings. Knowledge of different techniques and potential biasess is also tested with an exam.
Testing:
The final grade is based on two group assignments and an exam. Details on how the final grade is composed will be announced in the course manual.
Content
Over eight weeks, students will delve into the critical analysis of digital behavioral data. Weekly lectures cover theoretical and methodological background, while practical sessions involve hands-on data collection and analysis. Topics include social media data, web scraping, APIs, data donation, survey data, data quality frameworks, and ethical considerations in data collection and analysis. There are two types of practical meetings: one is a weekly meeting and another takes place every 2 weeks.
The course is structured as following:
- 1 lecture per week. Before the lecture, students are expected to read the assigned readings.
- 1 practical per week. The practicals are hands-on sessions where students will work collecting, analyzing and interpreting digital trace data. Make sure to bring a laptop to the practicals. After the practical, students are required to hand-in the answers. Questions about the practicals can be discussed in the next practical.
- 1 group project (60% of the grade) with two assignments (30% each).
- 1 final exam (40% of the grade) on Remindo, composed of multiple choice and open questions.
Assumed Prior Knowledge:
Regression analysis. Participants should be familiar with data analysis software (preferably R or another programming language/statistical analysis software e.g. Python, JASP, Stata, SPSS, SAS).
Note: Students who cannot comply with the entrance requirements mentioned above will be asked to provide further information on their eligibility. The course coordinator will decide if the student's prior experience is sufficient to follow the course.Reviews0 reviews
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