| Learning objectives |
- Formulate a research questions and identify appropriate data
for the research question
- Devise a data management and analysis plan
- Understand data structure and programming fundamentals
- Construct and manage research data in R: clean, merge,
restructure, and document data for analysis
- Write and document a program in R to conduct the descriptive
analysis and visualization of data
- Present data and analysis result
|
| Examination |
|
Digital
Society C. Computing in Social Science Research - Introduction to
R:
|
| Exam
ECTS |
7,5 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Individual exam |
| Size of written product |
Max. 20 pages |
|
The exam essay is a presentation of research
questions, data, and analysis results. It consists of text, tables,
and graphs.
Students are required to electronically upload their data and
program codes that they used to prepare the exam essay. The data
and codes will be graded as part of the exam but not count toward
the page limit. |
| Assignment type |
Report |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-point grading scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Winter |
| Make-up exam/re-exam |
Same examination form as the ordinary exam
|
Description of the exam
procedure
Students will use the data and code templates that the
instructor provides to conduct descriptive data analysis and data
visualization for their research questions. Students will be
evaluated based on their data management and computing
skills demonstrated in the research project report, data, and
codes. The research project report will be examined for the
research question, selection and construction of data and analysis,
and presentation of data and analysis results. The data files and
codes will be evaluated for the students’ skills in data
preparation, data management, computing, and
documentation.
|
|
| Course content, structure and pedagogical
approach |
|
R is a free data analysis software that has been gaining
popularity among social scientists and industry practitioners due
to its support of statistical analysis, data visualization, text
analysis, machine learning, and artificial intelligence. This
course aims to help students to develop understanding in the
digital society and to learn the necessary skills in R to adapt to
the changes in social science research that reflect the societal
changes. The course is designed to provide basic competency in
digital data and computing in R that can be useful for both social
science research projects and real-world applications.
This course includes demonstration and hands-on exercises from
which students will get basic training in digital social science
research skills and practical skills. Students will learn
fundamentals in data and programming for social science research:
data types, data structure, variables, syntax, and functions. They
will gain skills in applying those fundamentals in R to conduct
descriptive analysis and visualize data. Besides, students will
learn practical skills for the presentation of data and analysis
results and project management including data management and
documentation.
|
| Description of the teaching methods |
The course consists of lectures, demonstrations,
and hands-on exercises. Lectures will provide students with
background knowledge in social science research and fundamentals in
digital data and computing. Demonstrations include the skills and
codes for data management, analysis, data visualization, and
documentation. In the exercises, students will have hands-on
experience in applying their data management and computing skills.
As a blended learning course, the course website will provide
learning resources that students can use for their research
projects. |
| Feedback during the teaching period |
| During exercises, the instructor will provide
face-to-face assistance and feedback to students. Students will
also get peer-to-peer feedback in the exercise classes. The
instructor will give written or electronic feedback, such as
additional data and codes, for students’ research projects. The
instructor will be available for individual feedback during office
hours and may prompt students to attend office hours based on the
learning progress. |
| Student workload |
| Class teaching |
36 hours |
| Preparation for classes |
96 hours |
| Exam |
74 hours |
|
| Expected literature |
|
Main textbook: Richard Cotton, Learning
R, O'Reilly.
|
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