363-1167-00L  Data Science for Social Challenges

SemesterAutumn Semester 2022
LecturersR. Roller, L. Brandenberger
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
363-1167-00 GData Science for Social Challenges3 hrs
Tue10:15-12:00WEV F 109 »
Fri09:15-10:00WEV H 326 »
23.09.09:15-10:00LEE F 118 »
25.11.09:15-10:00LEE F 118 »
R. Roller, L. Brandenberger

Catalogue data

AbstractMany of today's social challenges cannot be adequately grasped simply by observing human behavior. To make these challenges visible and address their causes, we can use advanced statistics to disentangle complex interdependencies between the driving factors.
In this course, we build up methodological skills and places a strong focus on interpretation and reflection of results.
Learning objectiveA successful participant of this course will be able to
- interpret the results of data analysis with regard to the methodological choices and the operationalization of theoretical concepts
- assess potential flaws in research designs that can lead to flawed interpretations of results
- apply a wide variety of statistical models (e.g., regressions, difference-in-difference, network models) to different data sources
- and name the difference between statistical models and the advantages (or drawbacks) they hold for different data types
- name the limitations of observational data analysis, especially with regard to causality
- explain the importance of sensitivity and robustness checks for statistical analyses

In summary, a successful participant is able to assess quantitative social science research with regard to its research design, the model choice as well as the interpretation drawn from the estimates and make suggestions for improvements.
ContentData Science for Social Challenges offers a practical approach to the quantitative analysis of human behavior and social interactions. While the course `Social Data Science' focuses on data retrieval and processing, this course focuses on data analysis and interpretation of results.

The course is organized in three blocks of increasing data complexity.
The first block tackles linear data analyses, where a dependent variable is modeled based on a set of independent and control variables.
The second block tackles causal inference, where experimental settings are approximated with observational data to allow for causal interpretation of results.
The third block tackles data sources where observations are not independent of each other and therefore defy most statistical models. Here, we examine how people interact with each other and how these interactions affect the people involved in turn.

The course covers various application of quantitative social sciences:
- measuring biases in societies
- analyzing behavior changes (due to internal or external events)
- studying deviant behavior and peer effects
- exploring coordination between people

The course makes the link to sociological theories and shows how they can be used to derive testable hypotheses. A strong focus is laid upon the operationalization of different concepts, such as finding an appropriate measure of deviant behavior or the level of animosity that exists between people at a given time. These measures are tested using appropriate statistical models. Here, the focus is put upon the interpretation (e.g., coefficient sizes and power) as well as the presentation of results (e.g., through marginal effects). Lastly, the course fosters critical thinking by discussing sensitivity and robustness tests. As such, the course offers insights into quantitative research design by following a hands-on approach to the study of societal challenges through social data science.

The course includes a lecture, student-led presentations and an accompanying exercise class. In the exercise class students get the opportunity to run through the whole data analysis process. Starting with data inspection, students operationalize theoretical concepts and test them on various statistical models. Strong focus is put on sensitivity checks, where the effect of changes to the model (i.e., adding another control variable) is assessed.
LiteratureInterested students can peruse:

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Hamburg: SAGE Publications.

Baur, N., & Blasius, J. (Eds.). (2019). Handbuch Methoden der empirischen Sozialforschung. Wiesbaden: Springer VS.

Angrist, J. D., & Pischke, J. S. (2008). Mostly Harmless Econometrics. Princeton: Princeton University Press.
Prerequisites / NoticeThe statistical analyses in the course exercises are performed in R. Students should be interested in learning R skills to run sophisticated quantitative analyses.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesfostered
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence fostered
Negotiationfostered
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersR. Roller, L. Brandenberger
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Admission requirementThe statistical analyses in the course exercises are performed in R. Students should be interested in learning R skills to run sophisticated quantitative analyses. A self-study tutorial for base R-skills (or as a short refresher) is provided at the start of the course.
Additional information on mode of examinationThis course does not have a final exam. The performance assessment consists of the following elements:

1. Students are in charge of presenting and discussion a research question taken from a research paper. Group presentations are possible. Students present the research question and are in charge of leading the discussion (30min in total). Students have to summarize the discussion in a small written report (max. 300 words). The written report (authored individually) makes up for 20% of the final grade.
2. During the lecture, students are presented a research design by an external researcher. After the lecture and discussion, students have to evaluate the research design. The evaluations consist of answering questions at home and uploading the answers to Moodle (max. 100 words per question). The best out of the 3 evaluations counts towards 20% of the final grade.
3. At the end of term, students have to hand in a written report on a research question of their choice. The report consists of a short research design surrounding a research question the respective students are interested in. The report is max. 3 pages long and consists of 60% of the final grade.

Additionally, students partake in weekly pop-quizzes (during the lecture, not graded) and weekly exercise classes (not graded).

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places20 at the most
Waiting listuntil 15.09.2022

Offered in

ProgrammeSectionType
Management, Technology and Economics MasterSystems Design and RisksWInformation