Introduction

What this course is about — and how to get set up

Merlin Schaeffer · Department of Sociology

2026-09-09

By the end of today you can …

1. say what an explanatory research question is — the kind this course teaches you to answer;

2. explain how the course works — the weekly rhythm, the quizzes, and what I assume you know;

3. run R code inside your own course project — set up today, together.

One part of today’s lecture per goal.

What is this course about?

Part 1 of 3

Sociology asks many kinds of questions.

This course is about one kind: what is the effect of X?

My research agenda

How does international immigration transform societies?

Some of my current work

Source: Schaeffer, Romarri, Rosenberg, and Krakowski (2025)

Some of my current work

Source: Schaeffer, Romarri, Rosenberg et al. (2025)

Some of my current work

Source: Wiedner, Schaeffer, Carol, and Böller (2025)

Some of my current work

Source: Schaeffer and Andersen (2025)

Some of my current work

Source: Schaeffer and Andersen (2025)

Some of my current work

Source: Schaeffer and Andersen (2025)

4 types of research questions

(1) Ontological: what is the nature of X?

• What is power?
• What is gender; how is it different from sex?
• What is a state? “A human community that (successfully) claims the monopoly of the legitimate use of physical force within a given territory.” — Max Weber, “Politics as a Vocation” (1918)

(2) Critical: is X (un-)just?

• Is home office exploitation?
• Is the colorblind ideology a new form of racism?

Discuss: think of the last exam you wrote — which type of research question did it engage with?

(3) Descriptive: what types of X exist, and how frequent are they?

• What are the survival strategies of the poor?
• How did Danes structure their days during the COVID-19 lockdowns?

(4) Explanatory: what is the effect of X?

• What are the consequences of anomie? Why does the suicide rate vary across societies?
• How does capitalism affect us?
• How does urbanization affect us?

This course is about explanatory questions: what is the effect of X?

Most of their sociology so far has been the first three types, so this slide locates the course rather than ranking the types. Let them place their last exam paper before you reveal — it is usually ontological or critical. Then the point: all four are legitimate sociology, but only the fourth needs the machinery we build this term, because “what is the effect of X” is a question about a world that did not happen.

Goal of empirical sociology

Use data to discover patterns,
and the social mechanisms that bring them about.

The stress belongs on the second half of the sentence. Finding a pattern is the easy part and any software will do it; explaining the mechanism that produces it is sociology. Keep this slide short — it is a statement of purpose, and it comes back at the start of Lecture 6.

Three learning goals

(1) Multiple OLS regression

• Multiple predictors & confounder adjustment (Frisch-Waugh),
• Interaction effects,
• Polynomials.

(2) Visualization of regression results

• Model predictions,
• Coefficient plots.

(3) Fundamentals of causal inference

• Potential outcomes & directed acyclical graphs (DAG),
• Randomised Controlled Trials (RCT),
• Intention to treat (ITT) & instrument variable (IV) regression,
• Multiple regression,
• 2-SLS instrument variable regression,
• Regression discontinuity designs (RDD).

Do not explain the terms — they are a preview, not today’s content, and the list is deliberately intimidating. Say that out loud: none of this is expected to mean anything yet, and by December all of it will. The little DAG is the one picture worth pointing at, since it turns up in almost every remaining lecture.

Example: regression discontinuity design

Example: regression discontinuity design

Municipalities where far-right candidates ran versus where they won

Source: Romarri (2020)

Example: regression discontinuity design

Source: Romarri (2020)

Discuss: why compare municipalities where the far-right barely won with those where it barely lost?

Near the threshold, winning is almost random — so the two groups are comparable, like in an experiment. The full logic comes in Lecture 13.

Put the real problem to them first: municipalities that elect the far right differ from those that don’t in every imaginable way, so a naïve comparison is worthless. Then the trick — among places where the race was nearly tied, who actually won is close to a coin flip. Let them react before you reveal; the idea is intuitive even without any machinery. Promise Lecture 13 and move on.

Example: regression discontinuity design

Source: Schaeffer, Romarri, Rosenberg et al. (2025)

Close the research section with work of our own, so the methods feel like something people here actually do rather than textbook exercises. Keep it to the question and the picture — no estimation details. The message is simply: this is where the course leads, and you will be able to read figures like this by the end.

Break

How the course works

Part 2 of 3

The weekly rhythm, the quizzes that qualify you for the exam, and what I assume you already know.

Course structure

TA	Fridays	Room
Sofie	8 am	CSS 2-2-49
Natalie	10 am	CSS 2-0-30
Joakim	10 am	CSS 2-0-42

Recommendations

• Activate email notifications on Absalon.
• Regularly check your KU email, or forward it to your personal email.
• Ask and answer questions in the t-R-ouble forum on Absalon.

Introduce the TAs by name and room, and tell students to pick a Friday slot and stick with it. Push the t-R-ouble forum hard — most R problems are shared, and answering someone else’s question is the best revision there is. Also ask them to check their KU mail or forward it; every year someone misses a deadline this way.

Weekly Absalon online quizzes

• 13 online quizzes on Absalon, designed by Epinion & us.

• Based on data Epinion collects for the municipality of Copenhagen, DR, Skat, etc.

• Made available online Wednesdays after the lecture.

Beware: submit at least 10 completed quizzes — individually, each within two weeks of release — or you do not qualify for the exam.

The single most important administrative slide of the term — do not hurry it. Say the rule twice: at least ten, individually, each within two weeks of release. Missing this means not qualifying for the exam, and every year a few people discover it too late. Also worth selling the upside: the quizzes use real Epinion data, so they are practice, not just a hurdle.

Consequently, this course assumes

1. You have basic R coding skills and know how to use RStudio.

2. You know basic statistics:
   • Descriptive statistics: \(\%\), \(\bar{x}\), \(\text{SD}(x)\).
   • Associations:
     • Correlation: \(r_{y,x}\),
     • OLS regression: \(y_{i} = \alpha + \beta x_{i} + \epsilon_{i}\).
   • Inference: confidence intervals, \(t\)-values & \(p\)-values.

Rusty on any of this? Part 3 of today gets your tools ready, and the first weeks refresh the statistics.

Expect visible anxiety here, so deliver the green box warmly and mean it: Lectures 2 and 3 genuinely re-teach correlation, regression and inference, and nobody is assumed to remember it all from last term. What they do need is the willingness to type code and get error messages. Point them at the exercise classes and the forum now, before anyone falls behind quietly.

Two textbooks

\(\rightarrow\) Available at Academic Books on City Campus and at the university library.

Carried over from last semester; serves as a reference for regression and statistical inference.

Veaux, Velleman, and Bock (2021)

To learn how to identify causal effects, we use Angrist & Pischke’s textbook.

Angrist and Pischke (2014)

Two very different books: De Veaux is the reference they already own and will look things up in; Angrist & Pischke is the one to actually read, and it is unusually enjoyable for a methods text. Stress that readings are per lecture and listed on the website, and that doing them beforehand is what makes the lecture land.

AI (like ChatGPT)

1. Privacy: don’t use AI/LLMs to process sensitive information!

2. Learning:

   • Use AI to explain, comment, debug, and improve R code while you draft it.
   • Use AI to get summaries of text passages and concepts that you do not understand.
   • Use AI to discuss your understanding of the course materials and ask about things that confuse you.

3. Generation: don’t use AI to generate, draft, and write your R code. You need to learn how to code to assess whether the AI coded what you asked for.

Be straightforward rather than prohibitionist — they will use it either way. The line is between using AI to understand (explain this error, why does this function want a factor) and using it to produce the code they were meant to write. Give the honest reason: an LLM will hand you a confident, wrong regression, and only someone who can read the code will catch it. And repeat the privacy rule — never paste sensitive or personal data into a chatbot.

Questions?

Get your tools ready

Part 3 of 3

Everyone leaves today with a working R setup — we do this together, now.

Task 1: install R & RStudio

1. Get a recent version of R (≥ 4.6.0).

2. Install RStudio, or update to the most recent version.

The same steps, with more detail, are on the course website under About & setup.

Do this live, in the room, and walk around — the goal is that nobody leaves with a broken setup. Two things that trip people up every year: R and RStudio are separate installs, and an old R version will fail on some packages later. Point at the website page so they can redo it at home if their laptop misbehaves.

Task 2: get to know RStudio

Task 3: set RStudio preferences

Set these preferences to improve reproducibility!

\(\rightarrow\) RStudio / Tools / Global options.

Explain why, or they will undo it: never saving or restoring the workspace forces every analysis to run from the script, top to bottom. If your results only exist because of something you typed three weeks ago and forgot, they are not reproducible. This one checkbox prevents a whole category of misery later in the term.

RStudio workflow

1. Use Projects to manage all files (scripts, data, figures, and tables) belonging to the same project.

2. Always start your RStudio session by clicking on the Project for this course.

3. Use the editor to write as many R scripts as you like, saved in the same folder as your Project. Thereby you can reproduce your results.

The habit that saves them all term: open the Project first, every session. Projects handle the working directory, which is the single most common source of “it worked yesterday” — no more setwd() with a path that only exists on one laptop. Say that data files for this course go in that same folder, because the exercises assume it.

Set up your “Stats_II” project

• Click on the Project button.

Set up your “Stats_II” project

Set up a new project in RStudio:

1. “New Directory”
2. “New Project”
3. Finally, a name for the folder and — under “Browse” — where you want that folder to be located. I call mine Stats_II. This also generates a folder with the name you chose. Use that folder for everything related to this course.

Done! Now you should find an empty folder under the path where you wanted it set up.

Beware: in the future, always start RStudio by double-clicking the project icon in your folder for this course.

Set up your “Stats_II” project

Set up a new project in RStudio:

1. “New Directory”
2. “New Project”
3. Finally, a name for the folder and — under “Browse” — where you want that folder to be located.

Set up your “Stats_II” project

Set up a new project in RStudio:

1. “New Directory”
2. “New Project”
3. Finally, a name for the folder and — under “Browse” — where you want that folder to be located.

Set up your “Stats_II” project

Done! Now you should find an empty folder under the path where you wanted it set up.

R scripts

1. Open a new R script.
2. Save it in your new folder. Find it under “Files”.

R scripts

1. Type the lines shown in the screenshot on the right.
2. Save the file by clicking on the floppy-disc icon. I called mine “Lecture.R”.
3. Mark all code and click “Run”.

R scripts

OK, you are good to go!

Pro tip: use the R script to take notes during the lecture!

A # defines a comment. So use it to take notes and code in one single document per lecture.

Sell the pro tip properly, because the students who adopt it do noticeably better: one R script per lecture, code and notes together, comments after #. When they revise, the note sits right next to the code it explains. Check the room before moving on — everyone should have a project, a saved script, and one line of code that ran.

Check yourself: today’s goals

Look back at the goals from the start of the lecture. Can you tick all three?

• Explain to your neighbour what an explanatory research question is — and give one example.
• Name what it takes to qualify for the exam — how many quizzes, submitted how?
• Open your Stats_II project in RStudio and run a line of R code from a saved script.

Anything feel shaky? That is what this week’s Absalon quiz and the Friday exercise class are for.

References

Angrist, J. D. and J. Pischke (2014). Mastering ’Metrics: The Path from Cause to Effect. Princeton University Press.

Romarri, A. (2020). Do Far-Right Mayors Increase the Probability of Hate Crimes? Evidence From Italy. SSRN Scholarly Paper ID 3506811. Rochester, NY: Social Science Research Network.

Schaeffer, M. and S. Andersen (2025). “Unveiling the Price of Green”. In: Unpublished manuscript.

Schaeffer, M., A. Romarri, D. Rosenberg, et al. (2025). “When Politics Enters the Waiting Room: Far-Right Electoral Victories Exacerbate Discrimination in Access to Healthcare”. In: American Political Science Review. Forthcoming.

Veaux, D., Velleman, and Bock (2021). Stats: Data and Models, Global Edition. Pearson Higher Ed.

Wiedner, J., M. Schaeffer, S. Carol, et al. (2025). “Local Communities, Distant Origins: How Cultural Distance and Local Context Shape Immigrant Ethno-Religious Infrastructures”. In: American Journal of Sociology.