Course Catalog

MET: Crafting Social Science Research

Lecturer(s)

Emre Can Oral

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

It is increasingly important for modern social scientists to have a level of mathematical literacy, as mathematical research methods such as statistics and formal modelling have entered the main stream. This course is intended to provide an introduction to mathematical logic and rigour, and to some fundamental mathematical concepts that form the foundation of the modern subject. The course covers introductory set and function theory, including analysis of functions, and includes sections on both probability and linear algebra, which together are the basis of data analysis.

The exam is scheduled for tbc

Basic readings:

Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall

Additional readings:

Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Workshop
biweekly	13.09.24 – 06.12.24	Friday	13:45 – 17:00	B 317 in A5, 6 entrance B	Link

RES: CDSS Workshop: Sociology

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6

Course Content

All researchers face similar challenges with core issues of research design. A research design is a plan that specifies how you are going to carry out a research project and, particularly, how to use evidence to answer your research question. The goal of this course is to jump-start students with their dissertation proposal. This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.

The goal of this course is to jump-start students with their dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions, the development of a theoretical framework, the specification of the methodology and planned empirical analysis. You should be prepared to address the following questions: What makes that an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?

This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently, students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.

Course requirements & assessment

Mandatory readings, active participation in class, homework assignments, presentation of research proposal and performing as a discussant of proposals of peers in a workshop format, research proposal term paper (circa 10 pages, graded)

Schedule

Workshop
	03.09.24 – 03.12.24	Tuesday	10:15 – 11:45	211 in B6, 30–32	Link

Lecturer(s)

Florian Keusch

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

RES: Colloquia

MET: Computational Social Science Methods and Digital Behavioral Data

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.

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

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
	04.09.24 – 04.12.24	Wednesday	15:30 – 17:00	B317 in A5, 6 entrance B

MET: Machine Learning for Social Scientists (CDSS doctoral students only)

Lecturer(s)

Thomas Gautschi

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

Course Content

The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to a social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to rst the maximum likelihood estimator and second to generalized linear models (GLS) for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes. We will use the statistical package Stata.

Course requirements & assessment

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.
written exam (graded, 90 min)

Credits (9 ECTS for lecture & tutorial) will be awarded based on a passed written exam. Participation in the final exam is subject to having passed all course requirements as stated above.

Schedule

Lecture
	03.09.24 – 03.12.24	Tuesday	13:45 – 15:15	A103 in B6, 23–25
Tutorial
Danielle Martin	03.09.24 – 03.12.24	Tuesday	15:30 – 17:00	C116 in A5, 6 entrance C
Sandra Morgenstern	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	B318 in A5, 6 entrance B

MET: Practical Machine Learning and AI for Social Scientists

Lecturer(s)

Ruben Bach

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Participants must understand basic linear algebra, graduate-level statistics, and quantitative methods. Furthermore, they should be familiar with at least one programming language (R / Python).

Course Content

Machine learning algorithms – the backend of computational programs that can learn to perform tasks from data – already permeate many spheres of life. Next to its omnipresence in everyday technology, machine learning has become an important toolbox for research and decision-making in academia, government, business, and civil society – and its importance continues to grow by the day. This course introduces participants to popular topics in machine learning. We will cover the mathematical foundations, algorithmic mechanics, and the applied use of pertinent machine-learning techniques.

Course structure

The course will meet bi-weekly, starting Sep 9, 2024. The last session will be on Dec 2, 2024. We will meet from 12:00 to 15:15 in room 211 in B6, 30–32, and include breaks as necessary. The course is divided into three blocks. Block A: Foundations offers a general overview of the machine learning landscape, presents a generalized overview of machine learning projects, and provides a first introduction to important mathematical foundations of machine learning algorithms. Block B: Statistical Learning covers a range of popular methods for supervised and unsupervised learning, including classification, regression, and dimensionality reduction. Lastly, Block C: Preview, provides some introductory snapshots of important techniques along the machine learning frontier, such as the analysis of textual and audio-visual data, deep learning, and algorithmic fairness. The topics in this block will be determined by participants’ interests.

Course requirements and assessment

Participants can obtain 6 ECTS points by submitting a short paper that presents an application of ML to a research problem rooted in the social sciences by January 31, 2025. The paper should be written in a research note format and should not exceed 3,000 words in length. The word count must be indicated on the title page. The title page, a short abstract of up to 100 words, and all figures and tables count towards the word limit, references and appendices do not.

Schedule

Seminar
	09.09.24 – 02.12.24	Monday	12:00 – 475622:59	211 in B6, 30–32

MET: Quantitative Methods (formerly Multivariate Analyses (Theory + Lab Course))

Lecturer(s)

Alexander Scherf

Course Type: elective course

Course Number: MET

Credits: 5

Course Content

This course is designed to guide you through the key steps involved in conducting a comprehensive statistical analysis within the realms of exploratory data analysis with Python.

The course is entirely based on Python and covers statistical modeling, as well as supervised and unsupervised machine learning techniques, as well as neural networks. Throughout the course, various statistical problems will be addressed using regression, classification, and clustering models.

The course is structured into three main parts:

Introduction to Python and Essential Machine Learning Libraries: We will start with an overview of Python and the most critical libraries for data science.
Statistics and Statistical Modeling: Next, we will delve into statistics and statistical modeling techniques. Starting at simple regressions and ensemble modeling and working our way through to perceptrons, long-short-term-memory networks, decoders, encoders and transformer architectures.
Principles of Machine Learning and Neural Networks: Finally, we will explore hands-on the core concepts of machine learning and neural networks.

All attendees need a laptop capable of running comprehensive statistical analyses (>2GB RAM, optional NVIDIA GPU for tensor calculations).

Course requirements & course assessment

This Seminar is pass/fail without an assignment, but regular attendance is mandatory. Absence can be compensated for by the submission of a Python script.

Schedule

Seminar
	04.10.24 – 08.11.24	Friday	09:00 – 13:00	310 in B6, 30–32

MET: Research Design (Lecture + Tutorial)

Lecturer(s)

Thomas Gschwend

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.

The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected

Course requirements & assessment

Homework, participation, take-home exam (graded)

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	08:30 – 10:00	B244 in A5, 6 entrance B	Link
Tutorial
Domantas Undzenas	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	A102 in B6, 23–25	Link
	06.09.24 – 06.12.24	Friday	10:15 – 11:45	B143 in A5, 6 entrance B	Link

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

Lecturer(s)

Florian Keusch

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Course Content

How do we know which research design fits best our research question? What requirements must be in place for good descriptive, causal and predictive inference? How do we estimate causal effects? How do we design and analyze experiments? Can we make causal claims from observational data? Researchers in the social sciences must be able to answer all of these questions.
This course teaches the fundamental concepts behind the estimation of causal effects, including potential obstacles to causal inference. Real-world examples will be discussed in detail and students will apply the techniques learned with real datasets in R. Students will come away with an understanding of how to estimate causal effects in both randomized and observational settings, with a particular focus on the careful design of both types of studies.

Tutorial

In the practice sessions, students will learn how to implement causal inference methods in R. Students should bring their own laptop for the all practice sessions. Previous knowledge in R is not necessary although advantageous. Please make also sure to install R and R studio before the first practice session.

Course requirements & assessment

Lecture: Participation, written exam (graded, 90 minutes)
Tutorial: Homework, oral participation, presentation

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	12:00 – 13:30	C217 in A5, 6 entrance C	Link
Tutorial
Danielle Martin	04.09.24 – 04.12.24	Wednesday	13:45 – 15:15	A102 in B6, 23–25	Link

SMiP courses open to CDSS doctoral students

Course Type: elective course

Course Number: MET

Course Content

Please register online by tbc and check the SMiP pages for continuous updates.

MET: SMiP Foundations of Statistical Modeling (CDSS only)

MET/POL: Advanced Topics in Comparative Politics: Formal Models in CP (and some in IR)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

This course gives an advanced overview of standard multivariate methods and current developments in multivariate modeling. The topics include general and generalized linear models, structural equation models, multilevel modeling and specific models for the analysis of time-related effects. In the morning sessions, we will provide the formal foundations and theoretical background of the model classes for continuous and discrete observations. The afternoon sessions focus on empirical applications and hands-on exercises in data analysis.
The goals are to bring the PhD candidates to a common level of statistical knowledge and data analytic skills and to set the stage for the more specialized topics in the Foundations 2 course and workshops in the following semesters.

Dates tbc

Please register online by tbc and check the SMiP pages for continuous updates.

CDSS doctoral students in psychology only who attend all three modules of this course can:

- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.

- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.

Please contact the CDSS Center Manager to discuss the above mentioned possibilities.

MET/POL: Game Theory (Theory + Tutorial)

Lecturer(s)

Thomas Bräuninger

Course Type: elective course

Course Number: MET/POL

Credits: 8

Prerequisites

Topics covered in introductory Game Theory class

Course Content

This course is a continuation of the intro into Game Theory and surveys key applications of game theory with a particular emphasis on the link of theories, methods and empirics. Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. Topics covered include electoral competition, delegation, political agency, governmental veto players, authoritarian politics, manipulation, war and crisis bargaining. While the focus is on understanding applied work, previous training in game theory is required. Students will build upon their previous game theory training to become informed consumers of scholarship utilizing the methodology and begin to learn how to apply game-theoretic logic to their own work. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

Course requirements & assessment

Class discussion, paper presentation, participation, term paper (graded)

Literature

Various chapters of Scott Gehlbach's Formal Models of Domestic Politics (CUP) and journal articles from different fields

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	13:45 – 15:15	B317 in A5, 6 entrance B

RES (Bridge course): Lecture series “Data Science in Action”

Lecturer(s)

Thomas Bräuninger

Carlos Gueiros

Course Type: elective course

Course Number: MET/POL

Credits: 6 + 2

Course Content

The objective of this course is to provide students with the basics of formal modeling in political science. The course has some breadth in coverage in the sense that it provides a graduate-level introduction and overview to di erent areas in game theory. It is also narrow in the sense that the emphasis is not on application and model testing but getting trained in reading and writing down formal models. At the conceptual level the course will cover the following topics: normal form games, Nash equilibria, extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete and imperfect information, Bayesian perfect equilibria, signaling games, preferences and individual choices, basics of decision theory and social choice. At the substantial level, we will use these concepts to study, as examples, candidate competition, political lobbying, and war and deterrence.

Literature

McCarty, Nolan and Adam Meirowitz. 2007. Political Game Theory. Cambridge: Cambridge University Press.
Tadelis, Steven. 2013. Game Theory: An Introduction. Princeton: Princeton University Press.
Osborne, Martin. 2003. An Introduction to Game Theory. Oxford: Oxford University Press.
Morrow, James. 1994. Game Theory for Political Scientists. Princeton, NJ: Princeton University Press.
Dixit, Avinash K., Susan Skeath, and David H. Reiley. 2009. Games of Strategy. 3. ed. New York: Norton.
Hinich, Melvin J. and Michael C. Munger. 1997. Analytical Politics. Cambridge: Cambridge University Press.
Osborne, Martin and Ariel Rubinstein. 2020. Models in Microeconomic Theory. Open Book Publishers.

Course requirements & assessment

Working in small groups on the assignments, online meetings on Zoom in groups, final exam (graded)

Tutorial

This tutorial accompanies the graduate-level introductory lecture in game theory. Its main objective is to practice solution concepts for static and dynamic games of complete and incomplete information.
The contents are centered on the material covered in the lecture. Thus, the following key areas will be discussed: preferences and individual choices, decision theory, normal form games, Nash equilibria, extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete and imperfect information, Bayesian perfect equilibria, signaling games. At the substantial level, we will use these concepts to study, for instance, candidate competition, political lobbying, and war and deterrence. Students are required to submit four problem sets. Moreover, it is essential for students to prepare thoroughly for all sessions using online tutorials. Active participation in class discussions is expected.

Course requirements: Four problem sets.

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	10:15 – 11:45	B244 in A5, 6 entrance B
Tutorial
	06.09.24 – 06.12.24	Friday	12:00 – 13:30	B317 in A5, 6 entrance B

and:
https://www.linkedin.com/posts/mannheimdatascience_the-fall-semester-is-approaching-and-with-activity-7221818722364080128-_JW7?utm_source=share&utm_medium=member_desktop

Course Type: elective course

Course Number: RES (Bridge course)

Credits: 5

Course Content

Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on tbc.

GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).

For more information and registration, please visit the website: https://www.uni-mannheim.de/datascience/details/ringvorlesung-data-science-in-action-hws-2024-donnerstags-12-1330-uhr-online-via-zoom/

RES (bridge course): Mental health during dissertations (GESS doctoral students only)

SOC: Muslim Immigration and Integration

Course Type: elective course

Course Number: RES (bridge course)

Credits: 5

Course Content

tbc

The course will be taught by Dr. Julia Holl, University of Heidelberg

Schedule

Seminar
bi-weekly	18.09.24 – 27.11.24	Wednesday	10:15 – 11:45	tbc
	04.12.24	Wednesday	10:15 – 11:45	tbc

SOC: Organizational Theory

Lecturer(s)

Marc Helbling

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Up to the mid-1980s immigration was one of the least politicized issues on the political agenda of European countries. Since then, however, it has become one of the most important topics on the political agenda. Mass immigration has resulted in widespread xenophobia and fierce debates on the difficulties of integrating new arrivals. Muslim migration in particular seems to pose a special challenge to Western Europe, leading to widespread Islamophobia throughout the region. In this seminar we will consider reactions to Muslim immigration in Western Europe at various levels. What kind of policies do the European states implement in order to regulate mass immigration and integration? How do nationals react to this and how can we explain Islamophobia?

Course requirements & assignments

Participation, weekly reading, presentation of an empirical study, term paper (graded)

Schedule

Seminar
	04.09.24 – 04.12.24	Wednesday	10:15 – 11:45	tbc

SOC: Poverty and Social Exclusion in Europe

Lecturer(s)

Henning Hillmann

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

This advanced seminar will explore classic and recent social science research that seeks to explain variation in organizational behavior and development. We will consider a variety of research questions that tap into both formal and informal ways of organizing: what kinds of institutions are necessary to make economic organization work? Where do such institutions come from? Why do we observe very different outcomes across contexts even though they share the same market-supporting institutions? Why do some organizations survive even though they face the most unfavorable environments? How do conditions at the time of an organization's birth shape its development? To address these and further questions, we will rely both on recent theoretical advances and on empirical studies in a various settings.

Course requirements & assessment

Presentation of required readings
Active participation
Term paper (graded)

Schedule

Seminar
	10.10.24 – 21.11.24	Thursday	13:45 – 17:00	C116 in A5, 6 entrance C

Dissertation Tutorial: Political Science

Lecturer(s)

Reinhard Pollak

Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Poverty and social exclusion are extreme forms of inequality in modern societies. In Europe, these phenomena show up in different forms and imply different consequences for the people at risk. The seminar will provide an introduction into various concepts, dimensions and measures of poverty and social exclusion. We will discuss theories on the causes of poverty and social exclusion, learn about most vulnerable groups in Europe and the variation of poverty and social exclusion in Europe. We focus on consequences of living in poverty and we discuss different policies throughout Europe to lower poverty.

Course requirements & assessment

Regular small assignments (developing research questions based on the readings); compulsory attendance; participating in active discussion. Term paper (max. 5000 words, graded)

Schedule

Seminar
bi-weekly	04.09.24 – 27.11.24	Wednesday	08:30 – 11:45	C216 in A5, 6 entrance C

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.

BAS: Current Research Perspectives

BAS: Mathematics for Social Scientists

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

Talk schedule

Schedule

Lecture
	12.09.24	Thursday	08:30 – 11:45	Online	Link
	13.09.24	Friday	08:30 – 11:45	online
	19.09.24	Thursday	08:30 – 00:00	online
	20.09.24	Friday	08:30 – 00:00	online

MET: Crafting Social Science Research

Lecturer(s)

Emre Can Oral

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The exam is scheduled for tbc

Basic readings:

Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall

Additional readings:

Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Workshop
biweekly	13.09.24 – 06.12.24	Friday	13:45 – 17:00	B 317 in A5, 6 entrance B	Link

MET: Quantitative Methods (formerly Multivariate Analyses (Theory + Lab Course))

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Course requirements & assessment

Schedule

Workshop
	03.09.24 – 03.12.24	Tuesday	10:15 – 11:45	211 in B6, 30–32	Link

MET/POL: Game Theory (Theory + Tutorial)

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6 + 2

Course Content

Course requirements & assessment

Homework, participation, take-home exam (graded)

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	08:30 – 10:00	B244 in A5, 6 entrance B	Link
Tutorial
Domantas Undzenas	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	A102 in B6, 23–25	Link
	06.09.24 – 06.12.24	Friday	10:15 – 11:45	B143 in A5, 6 entrance B	Link

RES: CDSS Workshop: Political Science

Lecturer(s)

Thomas Bräuninger

Carlos Gueiros

Course Type: core course

Course Number: MET/POL

Credits: 6 + 2

Course Content

McCarty, Nolan and Adam Meirowitz. 2007. Political Game Theory. Cambridge: Cambridge University Press.
Tadelis, Steven. 2013. Game Theory: An Introduction. Princeton: Princeton University Press.
Osborne, Martin. 2003. An Introduction to Game Theory. Oxford: Oxford University Press.
Morrow, James. 1994. Game Theory for Political Scientists. Princeton, NJ: Princeton University Press.
Dixit, Avinash K., Susan Skeath, and David H. Reiley. 2009. Games of Strategy. 3. ed. New York: Norton.
Hinich, Melvin J. and Michael C. Munger. 1997. Analytical Politics. Cambridge: Cambridge University Press.
Osborne, Martin and Ariel Rubinstein. 2020. Models in Microeconomic Theory. Open Book Publishers.

Course requirements & assessment

Working in small groups on the assignments, online meetings on Zoom in groups, final exam (graded)

Tutorial

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	10:15 – 11:45	B244 in A5, 6 entrance B
Tutorial
	06.09.24 – 06.12.24	Friday	12:00 – 13:30	B317 in A5, 6 entrance B

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
	04.09.24 – 04.12.24	Wednesday	12:00 – 13:30	211 in B6, 30–32

RES: Colloquia

MET: Computational Social Science Methods and Digital Behavioral Data

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.

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

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
	04.09.24 – 04.12.24	Wednesday	15:30 – 17:00	B317 in A5, 6 entrance B

MET: Machine Learning for Social Scientists (CDSS doctoral students only)

Lecturer(s)

Thomas Gautschi

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

Course Content

Course requirements & assessment

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.
written exam (graded, 90 min)

Credits (9 ECTS for lecture & tutorial) will be awarded based on a passed written exam. Participation in the final exam is subject to having passed all course requirements as stated above.

Schedule

Lecture
	03.09.24 – 03.12.24	Tuesday	13:45 – 15:15	A103 in B6, 23–25
Tutorial
Danielle Martin	03.09.24 – 03.12.24	Tuesday	15:30 – 17:00	C116 in A5, 6 entrance C
Sandra Morgenstern	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	B318 in A5, 6 entrance B

MET: Practical Machine Learning and AI for Social Scientists

Lecturer(s)

Ruben Bach

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Participants must understand basic linear algebra, graduate-level statistics, and quantitative methods. Furthermore, they should be familiar with at least one programming language (R / Python).

Course Content

Course structure

Course requirements and assessment

Schedule

Seminar
	09.09.24 – 02.12.24	Monday	12:00 – 475622:59	211 in B6, 30–32

MET: Research Design (Lecture + Tutorial)

Lecturer(s)

Alexander Scherf

Course Type: elective course

Course Number: MET

Credits: 5

Course Content

This course is designed to guide you through the key steps involved in conducting a comprehensive statistical analysis within the realms of exploratory data analysis with Python.

The course is structured into three main parts:

Introduction to Python and Essential Machine Learning Libraries: We will start with an overview of Python and the most critical libraries for data science.
Statistics and Statistical Modeling: Next, we will delve into statistics and statistical modeling techniques. Starting at simple regressions and ensemble modeling and working our way through to perceptrons, long-short-term-memory networks, decoders, encoders and transformer architectures.
Principles of Machine Learning and Neural Networks: Finally, we will explore hands-on the core concepts of machine learning and neural networks.

All attendees need a laptop capable of running comprehensive statistical analyses (>2GB RAM, optional NVIDIA GPU for tensor calculations).

Course requirements & course assessment

This Seminar is pass/fail without an assignment, but regular attendance is mandatory. Absence can be compensated for by the submission of a Python script.

Schedule

Seminar
	04.10.24 – 08.11.24	Friday	09:00 – 13:00	310 in B6, 30–32

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

Lecturer(s)

Florian Keusch

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Course Content

Tutorial

Course requirements & assessment

Lecture: Participation, written exam (graded, 90 minutes)
Tutorial: Homework, oral participation, presentation

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	12:00 – 13:30	C217 in A5, 6 entrance C	Link
Tutorial
Danielle Martin	04.09.24 – 04.12.24	Wednesday	13:45 – 15:15	A102 in B6, 23–25	Link

SMiP courses open to CDSS doctoral students

Course Type: elective course

Course Number: MET

Course Content

Please register online by tbc and check the SMiP pages for continuous updates.

MET: SMiP Foundations of Statistical Modeling (CDSS only)

MET/POL: Advanced Topics in Comparative Politics: Formal Models in CP (and some in IR)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Dates tbc

Please register online by tbc and check the SMiP pages for continuous updates.

CDSS doctoral students in psychology only who attend all three modules of this course can:

- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.

- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.

Please contact the CDSS Center Manager to discuss the above mentioned possibilities.

POL: Advanced Topics in Comparative Politics:

Lecturer(s)

Thomas Bräuninger

Course Type: elective course

Course Number: MET/POL

Credits: 8

Prerequisites

Topics covered in introductory Game Theory class

Course Content

Course requirements & assessment

Class discussion, paper presentation, participation, term paper (graded)

Literature

Various chapters of Scott Gehlbach's Formal Models of Domestic Politics (CUP) and journal articles from different fields

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	13:45 – 15:15	B317 in A5, 6 entrance B

POL: Advanced Topics in Comparative Politics: Political Behavior in Context

Course Type: elective course

Course Number: POL

Course Content

Tbc

This course will be taught by Nan Zhang, PhD

Schedule

Seminar
	06.09.24 – 06.12.24	Friday	13:45 – 15:15	C116 in A5, 6 entrance C	Link

POL: Advanced Topics in International Politics: the Politics of Human Rights

Lecturer(s)

Harald Schoen

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

Political behavior takes place in context. This statement is a truism and implies several challenges at the same time. Context is a multidimensional concept comprising – inter alia – social, political, and institutional features. At the conceptual and theoretical level, the diversity of dimensions requires careful consideration of how to integrate contextual features into individual-level models of political behavior. Moreover, combining data from different levels of aggregation to examine the role of contexts in individual-level behavior raises several methodological issues. In this seminar, we will address the conceptual, theoretical, and methodological issues in the analysis of contextual effects on individual-level political behavior. Students will review empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.

Course requirements & assessment

Oral presentation of a literature review and active participation during the sessions, term paper (ca. 8.000 words, graded)

Schedule

Seminar
	02.09.24 – 02.12.24	Monday	13:45 – 15:15	B318 in A5, 6 entrance B	Link

RES (Bridge course): Lecture series “Data Science in Action”

Lecturer(s)

Sabine C. Carey

Course Type: elective course

Course Number: POL

Credits: 8

Course Content

tbc

Schedule

Seminar
	03.09.24 – 03.12.24	Tuesday	13:45 – 15:15	B143 in A5, 6 entrance B	Link

and:
https://www.linkedin.com/posts/mannheimdatascience_the-fall-semester-is-approaching-and-with-activity-7221818722364080128-_JW7?utm_source=share&utm_medium=member_desktop

Course Type: elective course

Course Number: RES (Bridge course)

Credits: 5

Course Content

RES (bridge course): Mental health during dissertations (GESS doctoral students only)

BAS: Current Research Perspectives

Course Type: elective course

Course Number: RES (bridge course)

Credits: 5

Course Content

tbc

The course will be taught by Dr. Julia Holl, University of Heidelberg

Schedule

Seminar
bi-weekly	18.09.24 – 27.11.24	Wednesday	10:15 – 11:45	tbc
	04.12.24	Wednesday	10:15 – 11:45	tbc

BAS: Mathematics for Social Scientists

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

Talk schedule

Schedule

Lecture
	12.09.24	Thursday	08:30 – 11:45	Online	Link
	13.09.24	Friday	08:30 – 11:45	online
	19.09.24	Thursday	08:30 – 00:00	online
	20.09.24	Friday	08:30 – 00:00	online

MET: Crafting Social Science Research

Lecturer(s)

Emre Can Oral

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The exam is scheduled for tbc

Basic readings:

Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall

Additional readings:

Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Workshop
biweekly	13.09.24 – 06.12.24	Friday	13:45 – 17:00	B 317 in A5, 6 entrance B	Link

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Course requirements & assessment

Schedule

Workshop
	03.09.24 – 03.12.24	Tuesday	10:15 – 11:45	211 in B6, 30–32	Link

RES: AC2/BC3 Colloquia I

RES: CDSS Workshop: Research in Clinical Psychology

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

Please check with individual chairs in the Psychology Department for dates and times of research colloquia as well as registration.

All 2nd and 3rd year doctoral students must attend the colloquia in order to receive the 2 ECTS.

RES: CDSS Workshop: Research in Cognitive Psychology

Lecturer(s)

Georg W. Alpers

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

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.

Each spring term there will be a joint CDSS Workshop that all CDSS doctoral students of psychology attend. Each autumn term you will have the choice between three CDSS Workshops with a focus on either clinical, cognitive or social research.

Course Content

Research in Clinical Psychology: We invite CDSS candidates to discuss their research with experts in the field. The chair of Clinical Psychology and Biological Psychology and Psychotherapy pursues a wide range of topics and brings together a large spectrum of research approaches. We address open questions regarding each step of creative research and prolific publication of our scientific results. Each week we select one or two of our own projects for our discussion.

Literature: References will be given during the course.

Competences acquired

Improvement in research skills and communication of research results.

Schedule

Workshop
	05.09.24 – 05.12.24	Thursday	13:00 – 14:00	016–017 in L 13, 15–17

RES: CDSS Workshop: Research in Social Cognition

Lecturer(s)

Arndt Bröder

Edgar Erdfelder

Beatrice G. Kuhlmann

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

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.

Course Content

Research in Cognitive Psychology: Research projects in 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.

Open office hours:
Prof. Dr. Erdfelder: Thursday, 10.15h – 11.45h.

Literature: References will be given during the course.

Competences acquired

Improvement in research skills and communication of research results.

Schedule

Workshop
	02.09.24 – 02.12.24	Monday	15:30 – 17:00	A 102 in B6, 23–25	Link

MET: Computational Social Science Methods and Digital Behavioral Data

Lecturer(s)

Herbert Bless

Michaela Wänke

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

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.

Course Content

This seminar has a particular focus on research activities in social psychology. Unlike seminars that concentrate on one core thematic topic, this seminar will address a selected variety of different research topics in current social psychology. In each seminar session we will have a presentation either by participating doctoral students or by members of the social psychology group. Each presentation will address a current research topic in social psychology. The seminar provides the opportunity to actively discuss methodological, theoretical, and applied implications of the presented research. A particular focus will rest on the discussion of general methodological aspects.

Literature: Will be announced in the seminar

Schedule

Workshop
	02.09.24 – 02.12.24	Monday	10:15 – 11:45	C217 in A5, 6 entrance C	Link

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

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
	04.09.24 – 04.12.24	Wednesday	15:30 – 17:00	B317 in A5, 6 entrance B

MET: Machine Learning for Social Scientists (CDSS doctoral students only)

Lecturer(s)

Thomas Gautschi

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

Course Content

Course requirements & assessment

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.
written exam (graded, 90 min)

Credits (9 ECTS for lecture & tutorial) will be awarded based on a passed written exam. Participation in the final exam is subject to having passed all course requirements as stated above.

Schedule

Lecture
	03.09.24 – 03.12.24	Tuesday	13:45 – 15:15	A103 in B6, 23–25
Tutorial
Danielle Martin	03.09.24 – 03.12.24	Tuesday	15:30 – 17:00	C116 in A5, 6 entrance C
Sandra Morgenstern	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	B318 in A5, 6 entrance B

MET: Practical Machine Learning and AI for Social Scientists

Lecturer(s)

Ruben Bach

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Participants must understand basic linear algebra, graduate-level statistics, and quantitative methods. Furthermore, they should be familiar with at least one programming language (R / Python).

Course Content

Course structure

Course requirements and assessment

Schedule

Seminar
	09.09.24 – 02.12.24	Monday	12:00 – 475622:59	211 in B6, 30–32

MET: Quantitative Methods (formerly Multivariate Analyses (Theory + Lab Course))

Lecturer(s)

Alexander Scherf

Course Type: elective course

Course Number: MET

Credits: 5

Course Content

This course is designed to guide you through the key steps involved in conducting a comprehensive statistical analysis within the realms of exploratory data analysis with Python.

The course is structured into three main parts:

Introduction to Python and Essential Machine Learning Libraries: We will start with an overview of Python and the most critical libraries for data science.
Statistics and Statistical Modeling: Next, we will delve into statistics and statistical modeling techniques. Starting at simple regressions and ensemble modeling and working our way through to perceptrons, long-short-term-memory networks, decoders, encoders and transformer architectures.
Principles of Machine Learning and Neural Networks: Finally, we will explore hands-on the core concepts of machine learning and neural networks.

All attendees need a laptop capable of running comprehensive statistical analyses (>2GB RAM, optional NVIDIA GPU for tensor calculations).

Course requirements & course assessment

This Seminar is pass/fail without an assignment, but regular attendance is mandatory. Absence can be compensated for by the submission of a Python script.

Schedule

Seminar
	04.10.24 – 08.11.24	Friday	09:00 – 13:00	310 in B6, 30–32

MET: Research Design (Lecture + Tutorial)

Lecturer(s)

Thomas Gschwend

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

Course requirements & assessment

Homework, participation, take-home exam (graded)

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	08:30 – 10:00	B244 in A5, 6 entrance B	Link
Tutorial
Domantas Undzenas	05.09.24 – 05.12.24	Thursday	10:15 – 11:45	A102 in B6, 23–25	Link
	06.09.24 – 06.12.24	Friday	10:15 – 11:45	B143 in A5, 6 entrance B	Link

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

Lecturer(s)

Florian Keusch

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Course Content

Tutorial

Course requirements & assessment

Lecture: Participation, written exam (graded, 90 minutes)
Tutorial: Homework, oral participation, presentation

Schedule

Lecture
	04.09.24 – 04.12.24	Wednesday	12:00 – 13:30	C217 in A5, 6 entrance C	Link
Tutorial
Danielle Martin	04.09.24 – 04.12.24	Wednesday	13:45 – 15:15	A102 in B6, 23–25	Link

SMiP courses open to CDSS doctoral students

Course Type: elective course

Course Number: MET

Course Content

Please register online by tbc and check the SMiP pages for continuous updates.

MET: SMiP Foundations of Statistical Modeling (CDSS only)

MET/POL: Advanced Topics in Comparative Politics: Formal Models in CP (and some in IR)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Dates tbc

Please register online by tbc and check the SMiP pages for continuous updates.

CDSS doctoral students in psychology only who attend all three modules of this course can:

- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.

- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.

Please contact the CDSS Center Manager to discuss the above mentioned possibilities.

MET/POL: Game Theory (Theory + Tutorial)

Lecturer(s)

Thomas Bräuninger

Course Type: elective course

Course Number: MET/POL

Credits: 8

Prerequisites

Topics covered in introductory Game Theory class

Course Content

Course requirements & assessment

Class discussion, paper presentation, participation, term paper (graded)

Literature

Various chapters of Scott Gehlbach's Formal Models of Domestic Politics (CUP) and journal articles from different fields

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	13:45 – 15:15	B317 in A5, 6 entrance B

PSY: Advanced topics in Cognitive Psychology

Lecturer(s)

Thomas Bräuninger

Carlos Gueiros

Course Type: elective course

Course Number: MET/POL

Credits: 6 + 2

Course Content

McCarty, Nolan and Adam Meirowitz. 2007. Political Game Theory. Cambridge: Cambridge University Press.
Tadelis, Steven. 2013. Game Theory: An Introduction. Princeton: Princeton University Press.
Osborne, Martin. 2003. An Introduction to Game Theory. Oxford: Oxford University Press.
Morrow, James. 1994. Game Theory for Political Scientists. Princeton, NJ: Princeton University Press.
Dixit, Avinash K., Susan Skeath, and David H. Reiley. 2009. Games of Strategy. 3. ed. New York: Norton.
Hinich, Melvin J. and Michael C. Munger. 1997. Analytical Politics. Cambridge: Cambridge University Press.
Osborne, Martin and Ariel Rubinstein. 2020. Models in Microeconomic Theory. Open Book Publishers.

Course requirements & assessment

Working in small groups on the assignments, online meetings on Zoom in groups, final exam (graded)

Tutorial

Schedule

Lecture
	02.09.24 – 02.12.24	Monday	10:15 – 11:45	B244 in A5, 6 entrance B
Tutorial
	06.09.24 – 06.12.24	Friday	12:00 – 13:30	B317 in A5, 6 entrance B

4 ECTS

Lecturer(s)

Arndt Bröder

Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

This Lecture provides an advanced treatment of research methods in cognitive psychology as well as an overview of research topics of Cognitive Psychology in Mannheim.

Exemplary Topics

Basic methodology of Cognitive Psychology
Stochastic Modeling of Cognitive Processes
Model selection
Information Search in Decision Making
Visual short-term memory
Investigating cognitive processes using mouse-tracking
Strategy Contributions to Cognitive Aging
The Truth Effect

Literature

Farrell, S. & Lewandowsky, S. (2018). Computational modeling of cognition and behavior. Cambridge, UK: Cambridge University Press. (Chapters 1–5, 10, 12)

Quinlan, P. & Dyson, B. (2008). Cognitive psychology. Harlow, UK: Pearson.(Chapters 1 & 2)

Course requirements & assessment:

Active participation, final written exam (90 mins, graded)

Competences acquired

Knowledge of the main research strategies and theoretical developments in the study of memory; ability to discuss empirical studes critically

Schedule

Lecture
	05.09.24 – 05.12.24	Thursday	15:30 – 17:00	B144 in A5, 6 entrance B	Link

PSY: Advanced Topics in Work and Organizational Psychology

4 ECTS

Lecturer(s)

Sabine Sonnentag

Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

Knowledge in work and organizational psychology. It is expected that students know the content of a text book such as Spector (2008) or Landy & Conte (2010).

Course Content

This course provides an overview of core topics within work and organizational psychology. We will focus on recent theoretical approaches and empirical research findings (meta-analyses). In addition, we will discuss practical implications of core research findings. Topics include: Work motivation, stress and health, leadership, teams, personnel selection.

Methods comprise: Lecture, reading (as homework), teamwork assignments during class.

Course requirements and assessment
Graded homework assignment

Literature

Journal papers; reading assignments will be given at the beginning of the semester.

Schedule

Lecture
	05.09.24 – 05.12.24	Thursday	17:15 – 18:45	B244 in A5, 6 entrance B

RES (Bridge course): Lecture series “Data Science in Action”

and:
https://www.linkedin.com/posts/mannheimdatascience_the-fall-semester-is-approaching-and-with-activity-7221818722364080128-_JW7?utm_source=share&utm_medium=member_desktop

Course Type: elective course

Course Number: RES (Bridge course)

Credits: 5

Course Content

RES (bridge course): Mental health during dissertations (GESS doctoral students only)