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ASOB16112U Multiple Regression Analysis and Fundamentals of Causal Inference
The schedule can be found here
Compulsery course on the 3rd semester BSc in Sociology.
(Former: Advanced Quantitative Methods)
Credit and exchange students must be at bachelor level.
This course will provide students with a comprehensive understanding of the theory and practice of multiple regression analysis, including how to interpret regression coefficients and how to use regression analysis to test hypotheses and make predictions. In addition to learning multiple regression analysis, students in this course will also gain an understanding of the fundamental principles of causal inference. This includes distinguishing between correlation and causation, recognizing the impact of confounding variables, and learning techniques to establish causality. Students will also come to appreciate the critical role that multiple regression plays as a fundamental statistical tool in causal inference. The focus on multiple regression and causal inference entails a strong focus on different research designs, including randomized controlled trials, quasi-experimental designs, and observational studies, and how to choose the appropriate design for a given research question. To gain hands-on experience, students will learn how to use R to conduct multiple regression analysis and causal inference techniques. Thereby the students will explore how multiple regression analysis and causal inference can be applied to sociological research questions, such as the effects of social class on educational attainment or the impact of discrimination on health outcomes. Overall, this course will provide students with a strong foundation in statistical analysis and causal inference, which are essential skills for conducting sociological research.
The course introduces students to multiple regression analysis, the fundamentals of causal inference, and their application to sociological research questions. Thereby, students gain knowledge of:
- Multiple linear regression and all their important parameters,
- Statistical hypothesis tests and their use in multiple regression analysis,
- Assumptions of multiple OLs regression,
- Randomized controlled trials,
- Instrument variable regression,
- Interaction effects,
- Regression discontinuity designs.
The course gives students the opportunity for practical mastery of regression analysis in R. Accordingly, upon completion of the course students will be able to:
- Conduct multiple regression analysis in R,
- Correctly interpret regression coefficients,
- Use statistical hypothesis tests to answer sociological questions,
- Select relevant control variables for a multiple regression analysis,
- Specify and correctly interpret interaction effects,
- Specify and correctly interpret polynomials to specify non-linear relationships,
- Conduct instrument variable regression in R,
- Conduct regression discontinuity designs in R,
- Compare model fit,
- Conduct model checks,
- Visualize and communicate multiple regression results,
- Critically evaluate the results of multiple regression analysis.
After completing this course, students will be able to:
- Evaluate the effects of policies, organizational innovations, and so on.
- Identify different causal factors explaining a phenomena,
- Distinguish mere correlations from actual causal relationships,
- Use knowledge and skills for research and consulting,
- Analyze various types of data and quickly gain a working understanding of new data sources,
- Write reports that involve advanced statistical data analysis,
- Acquire further advanced quantitative methods training in factor analysis, multilevel modelling, panel data analysis, or computational sociology.
De Veaux, Richard, Paul F. Velleman, and David E. Bock. 2021. Stats. Data and Models. Boston: Pearson & Addison Wesley.
Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.
- Exam Preparation
Weekly mandatory online quizzes will give students automated feedback on single tasks. Moreover, students will get further feedback in weekly tutorials given by student instructors. The latter will also be helping with feedback throughout the exam period up until submission of their final integrated exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
- 7,5 ECTS
- Type of assessment
- Written examination
- Type of assessment details
- To get qualified for the integrated written take-home exam, the
students should hand in at least 10 of the weekly Absalon online
quizzess. The quizzes need to be handed in within two weeks after
they are made available online. Note that the quizzes are not
graded and thus there is also no pass-threshold. Students simply
need to complete 10 quizzes.
The written take-home assignment is integrated with the exam in “Velfærd, ulighed og mobilitet” and based on a set of questions that need to be answered individually or in a group of max four students. The scope of the written take-home essay is a maximum of 10 pages. For group assignments, an extra 5 pages are added per additional student.
- Exam registration requirements
Sociology students must be enrolled under BSc Curriculum 2016 to take this exam.
Credit students must be at bachelor level.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
Find more information on your study page at KUnet.
Exchange students and Danish full degree guest students please see the homepage of Sociology; http://www.soc.ku.dk/english/education/exams/ and http://www.soc.ku.dk/uddannelser/meritstuderende/eksamen/
If the re-exam is taken during the ordinary exam period: see ordinary exam form
If the re-exam is taken during the re-exam period:
Written take-home essay based on one or more NEW formulated questions.
Abovementioned applies to course registrations in Spring 2023 and onward.
If you have been registered for the course before Spring 2023, please write to the study administration: email@example.com.
NB! All exams (both ordinary and re-exams) will take place at the end of the spring semester only, as the course is not offered in the fall.
Criteria for exam assesment
Please see the learning outcome.
- Course code
- 7,5 ECTS
- 1 semester
- See timetable
- Course capacity
- Vejl. 110
- Department of Sociology, Study Council
- Department of Sociology
- Faculty of Social Sciences
- Merlin Schaeffer (4-736b79694679756934717b346a71)