Course Catalog

RES: CDSS Workshop: Sociology

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Questions of cause and effect are at the heart of social science. And yet, establishing credible causal effects in empirical analyses is a difficult enterprise. This course will introduce some of the key conceptual and methodological approaches to tackle the causal inference problem: the potential outcomes model of causal inference, experimental designs, matching and regression, instrumental variables, regression discontinuity designs as well as difference-in-differences and fixed effects.

The course will be taught by Prof. Marc Ratkovic, PhD

Prof. Ratkovic will be blocking off some hours for open consulting, conversation, and research discussions on my calendly. Please feel free to sign up and set up a time. He's left the times in 15 minute blocks, but if you would like more time, you can sign up for multiple blocks. Please call up the course via Portal to to access the calendly link.

Course requirements & assessment

Active participation, term paper (graded)

Schedule

Workshop
	11.02.25 – 27.05.25	Tuesday	08:30 – 10:00	211 in B6, 30–32

Lecturer(s)

Florian Keusch

Lars Leszczensky

Course Type: core course

Course Number: RES

Credits: 2

Course Content

Participation is mandatory for first to third year CDSS Sociology students. Participation is recommended for later CDSS doctoral candidates, but to no credit.

The goal of this course is to provide support and crucial feedback for CDSS doctoral candidates in sociology on their ongoing dissertation project. In this workshop CDSS students are expected to play two roles. They should provide feedback to their peers as well as present their own work in order to receive feedback.

Dates tbd

Schedule

Workshop
	11.02.25 – 27.05.25	Tuesday	17:15 – 18:45	209 in B6, 30–32	Link

RES: Colloquia

RES: English Academic Writing

Course Type: core course

Course Number: RES

Credits: 2

Course Content

CDSS doctoral students in political science and sociology can choose freely which weekly colloquium to attend. Colloquia must be attended regularly in year two and three of doctoral studies.

Please choose from

MZES Colloquium A “European Societies and their Integration”

MZES Colloquium B “European Political Systems and their Integration”

Please refer to the MZES web page for all further details. The talk announcements will be communicated via the CDSS mailing list as well.

Alternatively you can attend the Mannheim Research Colloquium on Survey Methods (MaRCS) or the MZES Social Science Data Lab, which will be announced through the Faculty of Social Sciences mailing list.

3 ECTS

Lecturer(s)

Sean Carey

Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review

Course Content

The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.

Course requirements & assessment

Term paper

Schedule

Workshop
	13.02.25 – 22.05.25	Thursday	12:00 – 13:30	B 317 in A5, 6 entrance B	Link

MET: 14th GESIS Summer School in Survey Methodology & GESIS Seminars

up to 12 ECTS

Course Type: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

The GESIS summer school takes place in Cologne from XXXXX. Detailed information about the summer school program is available on the GESIS website.

*According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

MET: Advanced Quantitative Methods

6+2 ECTS

Lecturer(s)

Thomas Gschwend

Course Type: elective course

Course Number: MET

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis

Course Content

The goal of this course is to provide an introduction into maximum-likelihood estimation.

Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is mandatory for the assignments which complement the lecture (6 ECTS).

Literature

Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Course requirements & assessment

Homework assignements, research paper (all graded)

Tutorial

The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

Schedule

Lecture
	12.02.25 – 28.05.25	Wednesday	08:30 – 10:00	B 244 in A5, 6 entrance B	Link
Tutorial
Undzenas	13.02.25 – 22.05.25	Thursday	10:15 – 11:45	A 102 in B6, 23–25	Link
	14.02.25 – 30.05.25	Friday	10:15 – 11:45	tbc	Link

MET: Bayesian Statistics for Social Scientists

MET: Computational Social Science Methods and Digital Behavioral Data

Lecturer(s)

Richard Traunmüller

Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

You should be familiar with the basics of regression models and maximum likelihood estimation. No previous knowledge of software for Bayesian inference is required. However, we will use R as a front-end to JAGS and for graphical displays. Resources to learn R basics are UCLA’s Stat Consulting Site as well as the official manuals at http: //www.r-project.org/.

Course Content

This course is intended as an introduction to applied Bayesian statistics for social scientists. Bayesian analysis involves two key aspects – inference based on probability theory and estimation using stochastic simulation. The course will spend some time on the basic principles of both aspects (Where do Bayesian priors come from? How does MCMC work?) and then apply them to some of the workhorse models of the social sciences: linear and discrete outcome models as well as their hierarchical counterparts. We will also discuss practical issues of applied Bayesian analysis, such as MCMC convergence diagnostics as well as Bayesian model checking and parameter summary.

Course readings

Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412–423.
Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Course assessment
Take home exam

Schedule

Workshop
	13.02.25 – 27.03.25	Thursday	08:30 – 11:45	211 in B6, 30–32	Link

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Please bring your own laptops for use in the course. At least basic knowledge of R is required.

Course Content

Computational Social Science is a young research field at the intersection of various social science disciplines, data science and computer science. The goal is to gain new insights into society through large amounts of data and the direct observation of human behavior. CSS relies on two cornerstones: digital behavioral data, which can be collected from online platforms or sensors like smartphones, and computer science methods such as automated text analysis to create appropriate measures for social science research questions. In the course, students will get to know foundational studies, theories and methods used in the field of CSS. We will discuss infrastructural, ethical and legal challenges and how to navigate these to devise appropriate research designs in CSS.
The course will be application oriented. Students will familiarize themselves with the main applications of CSS methods and implement them in R. The range of applications will cover data management and preprocessing, the application of machine learning, data and results visualization, statistical data analysis and the validation of results. The hands-on application examples will cover questions from various research fields and different data types like social media data or web browsing histories. Equipped with this theoretical and methodological toolkit, students will develop their own CSS research projects.

The course will be taught by Prof. Sebastian Stier.

Course requirements & assessment

Regular small assignments (programming homework, developing research questions and your own project); compulsory attendance; participating in discussions. Written term paper based on an analysis in R graded, (max. 5000 words), deadline: July 31, 2024

Schedule

Seminar
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc	Link
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc

MET: Experimental Design

MET: Fundamentals in Survey Design

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Experimental research designs are called the silver bullet or ‘Königsweg’ for causal identification. In recent years, the growing interest in causal identification and mechanism testing made experimental designs a regular empirical research tool in the social sciences – most recently in political science and sociology. This seminar shall give a broad overview of the range of experimental methods such as survey, field, lab-in-the-field, and laboratory experiments. We will discuss classical and recent work, including shortcomings and best practices like transparency (open science) and ethical considerations in experimental research methods. In addition, students will learn to think critically about different (experimental) research designs and design their own experiment to answer a research question they have developed.

The course will be taught by Dr. Sandra Morgenstern

Course requirements & assessment

Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar

Graded – Presentation of the Exposé of the seminar paper (incl. peer-feedback), research design seminar paper

Schedule

Seminar
	12.02.25 – 28.05.25	Wednesday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Generative AI in the Social Sciences

Lecturer(s)

Tobias Gummer

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of survey design. The course covers the major steps of implementing and conducting a survey and design decisions at each step. In addition, sources of error at each step are discussed. For illustration purposes and exercise, the course will draw on well-known large-scale surveys such as the German General Survey (ALLBUS), European Social Survey (ESS), European Values Study (EVS), and the German Socio-economic Panel (SOEP).

Course requirements & assessment

Active participation, homework assignments/oral presentations, term paper (graded)

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Longitudinal Data Analysis (Lecture + Tutorial)

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Selected topics relating to Generative AI in the social sciences are introduced in this seminar. Assigned readings and in-class activities will impart a deeper insight into the current status of research in this field, which is used to determine open questions and perspectives for further research.

Course taught by Prof. Joe Sakshaug.

Course requirements & assessment

For the examination, students write a term paper (5,000 words max.) where they either
1) carry out an empirical study in a focus area of social science research using Generative AI methods, OR
2) conduct a critical literature review of Generative AI used in the social sciences.

Competences acquired

Upon completion of the module, students are able to:
• present their basic knowledge in Generative AI applied to social science research fields
• name the latest Generative AI developments in social science research
• describe their in-depth knowledge of empirical approaches to Generative AI in the social science research fields covered
• critically evaluate the empirical literature and applications of Generative AI in the social science research fields covered

Schedule

Seminar
	14.02.25 – 30.05.25	Friday	08:30 – 10:00	Online	Link

6+3 ECTS

Course Type: elective course

Course Number: MET

Credits: 6+3

Prerequisites

Some basic knowledge of statistical inference and R is required

Course Content

Lecture

The course provides a broad overview of methods used in longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-section data, using measurements of the same individuals taken repeatedly through time can lead to better causal inferences in some cases, and can also give the possibility to learn more about the dynamics of individual behavior. The first objective of this course is to discuss the advantages of panel data, and the characteristics of the structure of panel data. Then, the course will give an overview of the main models (pooled OLS, fixed effects, random effects, first-differences) and provide the tools to choose betwen these models. The course will also discuss panel generalized linear models. Finally, an overview of event history analysis will be presented.

Tutorial

Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

The course will be taught by Dr. Danielle Martin

Course requirements & assessment

Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

Tutorial -

Homework: students must complete and submit the eight homework and pass at least six.
Assignments: To be completed during the session and submitted by the end of the session.

Schedule

Lecture
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	B 143 in A5, 6 entrance B
Tutorial
	11.02.25 – 27.05.25	Tuesday	15:30 – 17:00	B 143 in A5, 6 entrance B

MET: Regression & classification: Basic & advanced topics with illustrations in R

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The seminar gives an overview of

standard and advanced linear models (incl. multiple regression with continuous and categorical predictors, product terms, regularization methods, and nonlinear regression),
generalized linear models (incl. logistic regression, Poisson models, and log-linear models), and
supervised and unsupervised classification methods (incl. discriminant analysis, clustering methods, regression trees, and mixture models).

Regression and classification models are essential in many fields of psychological research as well as in clinical and epidemiological contexts. In this seminar, the models are introduced with their mathematical and statistical foundations, including model equations, methods of parameter estimation, and criteria of statistical inference. Statistical concepts and model applications are illustrated with simulations and through analyses of real data with R.

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. New York: Springer.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An intro¬duction to statistical learning with applications in R. New York: Springer.

Course requirements & assessment

Participation and written exam (graded)

Schedule

Seminar
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	108 CIP Pool in B6, 30–32

ECTS

Course Type: elective course

Course Number: MET

Course Content

SMiP course catalogue

Please register for the SMiP course program via their online registration tool by 15 February.

MET: Workshop IRT Modeling – Theory and Applications in R

SOC: Digital Transformations of Work

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

This workshop provides an introduction to Item Response Theory (IRT) with basic and advanced models for dichotomous and polytomous items. The topics include the Rasch model and extensions with two, three and four item parameters for dichotomous items. Concerning polytomous items, we discuss the partial credit and rating scale model, generalized partial-credit model and graded response model for items with ordinal response format, and the nominal response model for items with categorical response format. In addition, multidimensional IRT models for response styles and IRTree models for multiple response processes are presented.
The IRT models are outlined with their formal model equations, theoretical assumptions and implications, estimation techniques, and statistical testing procedures. Applications to simulated and real data sets illustrate the use of IRT models for the analysis of individual differences in basic and applied research.
The workshop includes practical exercises of IRT modeling and analysis with current R packages. Basic knowledge and experience in R, including data management and use of R packages, are required for participation in this workshop.
The language of instruction is English. The course program includes online meetings, videos and analysis projects as homework.

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
Course Credit/Exam: Presentation of IRT analysis

Schedule

Workshop
block seminar			480492:55 – 480492:55

SOC: Gender, Migration and Integration

Lecturer(s)

Reinhard Pollak

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Digital transformations in companies, in sectors of the economy, in the labor force, and in the world of work in general are one of the most fundamental societal transformations in contemporary history. Digital transformations of work and beyond shape our daily lives and might trigger fundamental challenges to the organization of work and beyond. How do we conceptualize these digital transformations? Are these rather social or rather technical transformations? What are the main characteristics of these transformations? How does digitalization permeate the world of work? Is it a perpetuating process? How can we measure digital transformations? What are the drivers of digital transformations? And what are the consequences for individuals and families? The seminar will address these questions and offers conceptual and empirical insights in the discussion of the digital transformations of work.

Course requirements & assessment

Regular small assignments (developing research questions based on the readings, short presentations); compulsory attendance; participating in active discussion.
Written term paper (graded, max. 5000 words), deadline: 15 July 2025

Schedule

Seminar
	12.02.25	Wednesday	08:30 – 11:45	tbc	Link
biweekly	26.04.25 – 09.04.25	Wednesday	08:30 – 11:45	tbc
biweekly	30.05.25 – 14.05.25	Wednesday	08:30 – 11:45	tbc

SOC: Housing and social inequalities over the life course

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Tbc

The course will be taught by Dr. Tamara Gutfleisch

Schedule

Seminar
	11.02.25 – 27.05.25	Tuesday	12:00 – 13:30	A102 in B6, 23–25

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Housing and social inequalities over the life course/for MA students
Have you heard about bthe housing crisis? Are you concerned about how housing prices will impact your own future? In the 21st century, housing reflects and amplifies social inequalities. While some people's housing needs are met, and others profit from the market,many struggle to afford a place to live, affecting their life courses. Housing intersects with dimensions of inequalitiy such as wealth, health, employment, education, and family life, manifesting across generations, locations, and social divisions like class and ethnicity. Understanding housing issues is therefore fundamental when considering social inequalities and the broader life course.

In this seminar we will read “Housing and Life Course Dynamics: Changing Lives, Places, and Inequalities”, by Rory Coulter, published by Policy Press in 2023. The book offfers a British perspective, we will therefore supplement it with materials on housing issues in other European countries, particularly Germany. This approach will deepen our understanding of how housing intertwines with life course progression and sustains social inequalities.

The course will be taught by Malgorzata Mikucka, PhD

Course requirements & assessment

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	15:30 – 17:00	B318 in A5, 6 entrance A

SOC: Political Networks

Dissertation Tutorial: Political Science

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

What explains the rise of the Medici in 15th century Florence? Why did thousands of women join the guerilla war in 1980s El Salvador? What can online book co-purchases tell us about ideological differences between Republicans and Democrats in contemporary America? These are some of the questions we will grapple with as we explore how social scientists have applied network analysis to the study of politics.

The course is designed as a general introduction to social network analysis, but it focuses heavily on examples from political sociology (and adjacent fields) as one area in which network theories and methodologies have had a great influence. We will treat network analysis both as a theoretical approach that regards relations as the basic building blocks of social life, and as a methodological toolkit for visualizing and analyzing the structure of relations. Many of these methods involve the quantitative measurement of network structures (e.g., the degree to which networks are clustered) and different positions within the network (e.g., central vs. peripheral actors). The course is organized around a set of key concepts and theoretical insights in network analysis – such as weak ties, brokerage, and diffusion – which we will apply to a variety of substantive issues ranging from recruitment into social movements to the emergence of new political identities to the nature of political action.

The best way to learn about social networks is to work with them, which is why the class has a large practical component. After developing the theoretical foundations in class discussions, students will learn how to analyze networks in a series of practical assignments. The final project will give students an opportunity to follow their own curiosity and apply the analytical tools introduced in class to an empirical context of their choosing.

This course will be taught by Benjamin Rohr

Course requirements & assessment

Regular & active participation, formulation of questions/comments to literature, term paper (graded)

Schedule

Seminar
	10.02.25 – 26.05.25	Monday	13:45 – 15:15	tbc	Link

0 ECTS

Course Type: core course

Course Content

Doctoral theses supervised by professors in the department of Political Science will be discussed.

Please check with individual chairs for dates and times.

DIS: Dissertation Proposal Workshop

2+8 ECTS

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

Crafting Social Science Research, Literature Review

Course Content

Nota bene: Further meeting dates will be determined during the first session. Only the first meeting will be held online, the remainder of the semester you'll meet in person.

Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.

Schedule

Workshop
further dates tbd	11.02.25	Tuesday	10:15 – 11:45	Zoom	Link

MET: Advanced Quantitative Methods

6+2 ECTS

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis

Course Content

The goal of this course is to provide an introduction into maximum-likelihood estimation.

Literature

Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Course requirements & assessment

Homework assignements, research paper (all graded)

Tutorial

The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

Schedule

Lecture
	12.02.25 – 28.05.25	Wednesday	08:30 – 10:00	B 244 in A5, 6 entrance B	Link
Tutorial
Undzenas	13.02.25 – 22.05.25	Thursday	10:15 – 11:45	A 102 in B6, 23–25	Link
	14.02.25 – 30.05.25	Friday	10:15 – 11:45	tbc	Link

MET: Theory Building and Causal Inference

RES: CDSS Workshop: Political Science

Course Type: core course

Course Number: MET

Credits: 6

Course Content

The course will be taught by Prof. Marc Ratkovic, PhD

Course requirements & assessment

Active participation, term paper (graded)

Schedule

Workshop
	11.02.25 – 27.05.25	Tuesday	08:30 – 10:00	211 in B6, 30–32

Lecturer(s)

Richard Traunmüller

Course Type: core course

Course Number: RES

Credits: 2

Course Content

Participation is mandatory for first to third year CDSS students of Political Science. Participation is recommended for later CDSS PhD candidates, but to no credit.

Other young researchers in the social sciences affiliated with the University of Mannheim (incl. MZES) are also invited to attend the talks.

The goal of this course is to provide support and crucial feedback for CDSS doctoral students on their ongoing dissertation project. In this workshop they are expected to play two roles – provide feedback to their peers as well as present their own work in order to receive feedback.

In order to receive useful feedback, participants are asked to circulate their paper and two related published pieces of research one week before the talk.

Schedule

Workshop
	12.02.25 – 28.05.25	Wednesday	12:00 – 13:30	211 in B6, 30–32

RES: Colloquia

RES: English Academic Writing

Course Type: core course

Course Number: RES

Credits: 2

Course Content

CDSS doctoral students in political science and sociology can choose freely which weekly colloquium to attend. Colloquia must be attended regularly in year two and three of doctoral studies.

Please choose from

MZES Colloquium A “European Societies and their Integration”

MZES Colloquium B “European Political Systems and their Integration”

Please refer to the MZES web page for all further details. The talk announcements will be communicated via the CDSS mailing list as well.

3 ECTS

Lecturer(s)

Sean Carey

Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review

Course Content

Course requirements & assessment

Term paper

Schedule

Workshop
	13.02.25 – 22.05.25	Thursday	12:00 – 13:30	B 317 in A5, 6 entrance B	Link

MET: 14th GESIS Summer School in Survey Methodology & GESIS Seminars

up to 12 ECTS

Course Type: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

The GESIS summer school takes place in Cologne from XXXXX. Detailed information about the summer school program is available on the GESIS website.

*According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

MET: Bayesian Statistics for Social Scientists

MET: Computational Social Science Methods and Digital Behavioral Data

Lecturer(s)

Richard Traunmüller

Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Course Content

Course readings

Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412–423.
Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Course assessment
Take home exam

Schedule

Workshop
	13.02.25 – 27.03.25	Thursday	08:30 – 11:45	211 in B6, 30–32	Link

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Please bring your own laptops for use in the course. At least basic knowledge of R is required.

Course Content

The course will be taught by Prof. Sebastian Stier.

Course requirements & assessment

Schedule

Seminar
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc	Link
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc

MET: Experimental Design

MET: Fundamentals in Survey Design

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The course will be taught by Dr. Sandra Morgenstern

Course requirements & assessment

Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar

Graded – Presentation of the Exposé of the seminar paper (incl. peer-feedback), research design seminar paper

Schedule

Seminar
	12.02.25 – 28.05.25	Wednesday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Generative AI in the Social Sciences

Lecturer(s)

Tobias Gummer

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course requirements & assessment

Active participation, homework assignments/oral presentations, term paper (graded)

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Longitudinal Data Analysis (Lecture + Tutorial)

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course taught by Prof. Joe Sakshaug.

Course requirements & assessment

Competences acquired

Schedule

Seminar
	14.02.25 – 30.05.25	Friday	08:30 – 10:00	Online	Link

6+3 ECTS

Course Type: elective course

Course Number: MET

Credits: 6+3

Prerequisites

Some basic knowledge of statistical inference and R is required

Course Content

Lecture

Tutorial

Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

The course will be taught by Dr. Danielle Martin

Course requirements & assessment

Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

Tutorial -

Homework: students must complete and submit the eight homework and pass at least six.
Assignments: To be completed during the session and submitted by the end of the session.

Schedule

Lecture
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	B 143 in A5, 6 entrance B
Tutorial
	11.02.25 – 27.05.25	Tuesday	15:30 – 17:00	B 143 in A5, 6 entrance B

MET: Regression & classification: Basic & advanced topics with illustrations in R

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The seminar gives an overview of

standard and advanced linear models (incl. multiple regression with continuous and categorical predictors, product terms, regularization methods, and nonlinear regression),
generalized linear models (incl. logistic regression, Poisson models, and log-linear models), and
supervised and unsupervised classification methods (incl. discriminant analysis, clustering methods, regression trees, and mixture models).

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Course requirements & assessment

Participation and written exam (graded)

Schedule

Seminar
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	108 CIP Pool in B6, 30–32

ECTS

Course Type: elective course

Course Number: MET

Course Content

SMiP course catalogue

Please register for the SMiP course program via their online registration tool by 15 February.

MET: Workshop IRT Modeling – Theory and Applications in R

POL: Advanced Topics in Comparative Politics: Coalition Politics

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
Course Credit/Exam: Presentation of IRT analysis

Schedule

Workshop
block seminar			480492:55 – 480492:55

POL: Advanced Topics in International Politics: Causal Interference in International Political Economy

Lecturer(s)

Marc Debus

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The way how parties agree to form stable multi-party cabinets is a key topic in the analysis of comparative politics. The recent rise in fragmentation of party systems in many countries puts coalition politics at the forefront. The recent success of left-wing and right-wing extremist and populist parties, often considered pariah parties, result in more complex government formation processes. Furthermore, the stability of coalitions is likely to decrease since recently formed coalitions tend to be ideologically more heterogeneous. Scientific interest in minority governments has also increased accordingly. The seminar addresses questions of coalition politics in the full life cycle such as coalition bargaining, coalition formation, the role of coalition agreements, portfolio

Course requirements & assessment

Active participation, oral presentation, term paper (graded)

Schedule

Seminar
	12.02.25 – 28.05.25	Wednesday	10:15 – 11:45	C116 in A5, 6 entrance C

POL: Advanced Topics in International Politics: Global Inequality

Lecturer(s)

Valentin Lang

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

In this seminar, we learn how to apply statistical methods for causal interference by studying recent research topics in the field of international political economy. In termns of methods, we will learn about experiments, natural experiments, difference-in-difference designs, regression discontinuity designs, and instrumental variables. In terms of research topics, we will study international migration, international organizations, and attitudes towards globalization. The seminar is structured such that for each method that we cover there is one session dedicated to learning the method itself and another session dedicated to a recent research paper that applies this method.

Course requirements & assessment

Active participation, term paper (graded)

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	12:00 – 13:30	A301 in B6, 23–25

POL: Advanced Topics in International Politics: UN Peacekeeping and the Protection of Civilians

Lecturer(s)

Valentin Lang

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

In this course, we study economic inequality from a political economy perspective. First, we will discuss various concepts of economic inequality and different ways to measure it. Then, we will investigate general trends in these various forms of economic inequality across the world. Second, we will discuss the scholarly literature on the determinants of economic inequality, focusing on both political and economic factors. In a third section, we will examine the literature on the implications of economic inequality as regards a variety of political and economic outcomes. The methodological focus of this seminar will be on quantative methods for causal inference.

Course requirements & assessment

Active participation, term paper (graded)

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	10:15 – 11:45	B 318 in A5, 6 entrance B

POL: Comparative Political Behavior

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

Are peacekeeping missions really keeping peace? The aim of this course is to examine the problems and possibilities of United Nations (UN) peace operations. The roles and responsibilities of peacekeepers are evolving as peacekeeping mandates become more complex and multidimensional. Peacekeeping operations have developed from simply monitoring ceasefires to protecting civilians, disarming ex-combatants, protecting human rights, promoting the rule of law, supporting free and fair elections, minimizing the risk of land-mines and much more. As of today, there are 12 active missions with over 90,000 personnel deployed. Civilians have increasingly become the victims of armed conflict. In response, the UN Security Council has made protecting civilians a focus of modern peacekeeping. The vast majority of peacekeepers today serve in missions with mandates that prioritize the protection of civilians (POC). The POC mandate is often the yardstick by which the success or failure of peacekeeping missions is assessed. But not only civilians are increasingly the target of violence. Tragically, over 3,500 peacekeepers have lost their lives, making many countries wary of contributing troops to the field.
This course is an introduction to the UN’s role in maintaining peace and international security. The subject is relevant for all those who want to focus on conflict or security studies, international organizations, global governance or other subfields in international relations, or are interested in pursuing a career working with a UN organization. The instructor not only focuses on civil-military coordination in her own research but has also practical work experience with a UN peacekeeping mission in the field.

The course will be taught by Prof. Melanie Sauter

Course requirements & assessment

Term paper (max. 8000 words, graded)

Schedule

Seminar
	12.02.25 – 28.05.25	Wednesday	10:15 – 11:45	C012 in A5, 6 entrance C

POL: International Politics

Lecturer(s)

Harald Schoen

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The main goal of this lecture is to present an advanced introduction to theoretical approaches, key concepts, and substantive issues in comparative political behavior. Building on a multi-level perspective, it will provide an overview of key concepts and theories in the analysis of micro-level processes of political behavior that are embedded in and feed into macro-level processes. Capitalizing on this analytical perspective, the lecture will also address major changes in the relationship between societal and political processes and institutions.

Course requirements & assessment

Regular participation is recommended, mandatory reading, term paper (graded)

Schedule

Lecture
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	B 244 in A5, 6 entrance B	Link

POL: Political Institutions and the Political Process

Lecturer(s)

Sabine C. Carey

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The security of individuals and states depends profoundly on international politics. Beyond the realm of security, structures and actors of “global governance” have been proliferating for many years. They influence crucial public policies in diverse ways. This lecture surveys academic debates on key topics of international politics, including: the sources of war, peace, and terrorism, the emergence and operation of international organizations and transnational civil society, and the making of key international policy outcomes including respect for human rights and climate policies.

Course requirements & assessment

Written exam (graded)

Schedule

Lecture
	10.02.25 – 26.05.25	Monday	13:45 – 15:15	B 143 in A5, 6 entrance B

DIS: Dissertation Proposal Workshop

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

This lecture gives an overview of selected theoretical concepts and the main research findings in the field of Comparative Government, specifically focusing on the role of political institutions and their impact for political decision-making at all stages in the political process. The course introduces a number of core themes in the comparative study of political institutions, such as electoral institutions and their effects on turnout, voting behaviour and party strategies. In addition, the lecture focuses on the impact of different institutional designs on patterns of party competition, government formation and coalition governance. In a third step, we discuss the effects of political institutions and of personal characteristics of legislators on various aspects of decision-making within parliaments and governments.

The course will be taught by Or Tuttnauer, PhD

Course requirements & assessment

Lecture of recommended texts, written exam (graded)

Schedule

Lecture
	11.02.25 – 27.05.25	Tuesday	10:15 – 11:45	C217 in A5, 6 entrance C	Link

2+8 ECTS

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

Crafting Social Science Research, Literature Review

Course Content

Nota bene: Further meeting dates will be determined during the first session. Only the first meeting will be held online, the remainder of the semester you'll meet in person.

Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.

Schedule

Workshop
further dates tbd	11.02.25	Tuesday	10:15 – 11:45	Zoom	Link

MET: Theory Building and Causal Inference

RES: AC3/BC4: Colloquia II

Course Type: core course

Course Number: MET

Credits: 6

Course Content

The course will be taught by Prof. Marc Ratkovic, PhD

Course requirements & assessment

Active participation, term paper (graded)

Schedule

Workshop
	11.02.25 – 27.05.25	Tuesday	08:30 – 10:00	211 in B6, 30–32

RES: CDSS Workshop: Research in Psychology

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

TCBI, CSSR, Dissertation Proposal

Course Content

Please check with individual chairs in the Psychology department for dates and times of research colloquia.

RES: English Academic Writing

Lecturer(s)

Arndt Bröder

Edgar Erdfelder

Course Type: core course

Course Number: RES

Credits: 2

Course Content

Participation is mandatory for first to third year CDSS doctoral students of Psychology. Participation is recommended for later CDSS doctoral students, but to no credit.

Research in Psychology: Research projects cognitive psychology and neuropsychology are planned, conducted, analyzed, and discussed.

Application via 'Studierendenportal' is necessary to have access to the course material provided in ILIAS.

Literature: References will be given during the course.

The workshop will be hosted by Prof. Arndt Bröder & Dr. Meike Kroneisen

Talk schedule

Competences acquired

Improvement in research skills and communication of research results.

Schedule

Workshop
	10.02.25 – 26.05.25	Monday	15:30 – 17:00	C 217 in A5, 6 entrance C

3 ECTS

Lecturer(s)

Sean Carey

Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review

Course Content

Course requirements & assessment

Term paper

Schedule

Workshop
	13.02.25 – 22.05.25	Thursday	12:00 – 13:30	B 317 in A5, 6 entrance B	Link

MET: 14th GESIS Summer School in Survey Methodology & GESIS Seminars

up to 12 ECTS

Course Type: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

The GESIS summer school takes place in Cologne from XXXXX. Detailed information about the summer school program is available on the GESIS website.

*According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

MET: Advanced Quantitative Methods

6+2 ECTS

Lecturer(s)

Thomas Gschwend

Course Type: elective course

Course Number: MET

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis

Course Content

The goal of this course is to provide an introduction into maximum-likelihood estimation.

Literature

Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Course requirements & assessment

Homework assignements, research paper (all graded)

Tutorial

The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

Schedule

Lecture
	12.02.25 – 28.05.25	Wednesday	08:30 – 10:00	B 244 in A5, 6 entrance B	Link
Tutorial
Undzenas	13.02.25 – 22.05.25	Thursday	10:15 – 11:45	A 102 in B6, 23–25	Link
	14.02.25 – 30.05.25	Friday	10:15 – 11:45	tbc	Link

MET: Bayesian Statistics for Social Scientists

MET: Computational Social Science Methods and Digital Behavioral Data

Lecturer(s)

Richard Traunmüller

Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Course Content

Course readings

Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412–423.
Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Course assessment
Take home exam

Schedule

Workshop
	13.02.25 – 27.03.25	Thursday	08:30 – 11:45	211 in B6, 30–32	Link

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Please bring your own laptops for use in the course. At least basic knowledge of R is required.

Course Content

The course will be taught by Prof. Sebastian Stier.

Course requirements & assessment

Schedule

Seminar
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc	Link
biweekly	12.02.25 – 09.04.25	Wednesday	09:15 – 12:30	tbc

MET: Experimental Design

MET: Fundamentals in Survey Design

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The course will be taught by Dr. Sandra Morgenstern

Course requirements & assessment

Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar

Graded – Presentation of the Exposé of the seminar paper (incl. peer-feedback), research design seminar paper

Schedule

Seminar
	12.02.25 – 28.05.25	Wednesday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Generative AI in the Social Sciences

Lecturer(s)

Tobias Gummer

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course requirements & assessment

Active participation, homework assignments/oral presentations, term paper (graded)

Schedule

Seminar
	13.02.25 – 22.05.25	Thursday	13:45 – 15:15	A103 in B6, 23–25	Link

MET: Longitudinal Data Analysis (Lecture + Tutorial)

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course taught by Prof. Joe Sakshaug.

Course requirements & assessment

Competences acquired

Schedule

Seminar
	14.02.25 – 30.05.25	Friday	08:30 – 10:00	Online	Link

6+3 ECTS

Course Type: elective course

Course Number: MET

Credits: 6+3

Prerequisites

Some basic knowledge of statistical inference and R is required

Course Content

Lecture

Tutorial

Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

The course will be taught by Dr. Danielle Martin

Course requirements & assessment

Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

Tutorial -

Homework: students must complete and submit the eight homework and pass at least six.
Assignments: To be completed during the session and submitted by the end of the session.

Schedule

Lecture
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	B 143 in A5, 6 entrance B
Tutorial
	11.02.25 – 27.05.25	Tuesday	15:30 – 17:00	B 143 in A5, 6 entrance B

MET: Regression & classification: Basic & advanced topics with illustrations in R

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The seminar gives an overview of

standard and advanced linear models (incl. multiple regression with continuous and categorical predictors, product terms, regularization methods, and nonlinear regression),
generalized linear models (incl. logistic regression, Poisson models, and log-linear models), and
supervised and unsupervised classification methods (incl. discriminant analysis, clustering methods, regression trees, and mixture models).

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Course requirements & assessment

Participation and written exam (graded)

Schedule

Seminar
	10.02.25 – 26.05.25	Monday	10:15 – 11:45	108 CIP Pool in B6, 30–32

ECTS

Course Type: elective course

Course Number: MET

Course Content

SMiP course catalogue

Please register for the SMiP course program via their online registration tool by 15 February.

MET: Workshop IRT Modeling – Theory and Applications in R

MET/PSY: Psychological interventions using diary designs

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Please speak to Prof. Meiser on what additonal work you need to submit in order to obtain 6 ECTS for this course.

Literature

Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
Course Credit/Exam: Presentation of IRT analysis

Schedule

Workshop
block seminar			480492:55 – 480492:55