Multiple Regression Analysis and Fundamentals of Causal Inference

Course content

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 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. 

Learning outcome


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,

- Polynomials,

- 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.

The three-hour weekly lecture contains two conventional lectures of 45 minutes, and two 20 minutes exercises where students solve tasks using R. In addition to the weekly lecture, there are also weekly tutorials taught by student instructors where lecture materials and exercises are reviewed in smaller groups. On top of that there are weekly online quizzes on Absalon that give automated feedback.

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.

Students should have a completed “Intro til R” and “Basic Statistics”, or other courses that cover the same content.

Requirement: Own laptop with running and updated versions of RStudio and R.

Peer feedback (Students give each other feedback)

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.

7,5 ECTS
Type of assessment
On-site written exam
Type of assessment details
The exam is integrated with Velfærd, Uligehed og Mobilitet.

The students are required to formulate their own exam questions based on pre-defined guidelines provided by the teacher. Students will receive the exam guidelines for formulating exam questions during the ongoing semester. The teacher is required to provide at least two exemplary exam questions that adhere to the guidelines.

The exam can be written individually or in groups of max. 4 students.
Length of the exam is 15 pages + 5 pages pr. extra group member.
Exam registration requirements

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.


Sociology students must be enrolled under BSc Curriculum 2016 to take this exam.

Credit students must be at bachelor level.


Policy on the Use of Generative AI Software and Large Language Models in Exams

The Department of Sociology prohibits the use of generative AI software and large language models (AI/LLMs), such as ChatGPT, for generating novel and creative content in written exams. However, students may use AI/LLMs to enhance the presentation of their own original work, such as text editing, argument validation, or improving statistical programming code. Students must disclose in an appendix if and how AI/LLMs were used; this appendix will not count toward the page limit of the exam. This policy is in place to ensure that students’ written exams accurately reflect their own knowledge and understanding of the material.

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; and


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:

Students have to write a new essay using the guidelines provided by the teacher.


Mandatory Course

NB! All exams (both ordinary and re-exams) will take place at the end of the fall semester only, as the course is not offered in the spring.

Criteria for exam assessment

Please see the learning outcome.

  • Category
  • Hours
  • Lectures
  • 42
  • Preparation
  • 116
  • Exam Preparation
  • 48
  • English
  • 206


Course number
7,5 ECTS
Programme level

1 semester

See timetable
Vejl. 110
Department of Sociology, Study Council
Contracting department
  • Department of Sociology
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Merlin Schaeffer   (4-746c7a6a477a766a35727c356b72)

Merlin Schaeffer

Saved on the 01-05-2024

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