A doctoral student is holding a laptop and is pointing out a course on the screen where the schedule for different doctoral courses can be seen.

Fall 2024

  • Sociology

    Dissertation Tutorial: Sociology
    0 ECTS
    Course Type: core course
    Course Content

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

    Please check with individual chairs for dates and times.

    BAS: Current Research Perspectives
    2 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: BAS
    Credits: 2
    Course Content

    Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will get exposure to the different faculty and have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

    Assignment: Come-up with a research project idea that is informed theoretically or methodologically by insights from one or several CDSS faculty presentations (outside of your particular field) in this class. Write-up your idea and describe a potential research design in a short 3-page paper. The paper is due October 31st.

    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
    BAS: Mathematics for Social Scientists
    2 ECTS
    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 tbc Link
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)

    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
    RES: CDSS Workshop: Sociology
    2 ECTS
    Lecturer(s)

    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
    2 ECTS
    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)
    6 + 3 ECTS
    Lecturer(s)

    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: Machine Learning for Social Scientists (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)

    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 Summary

    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))
    6 + 2 ECTS
    Lecturer(s)

    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: Research Design (Lecture + Tutorial)
    6 + 3 ECTS
    Lecturer(s)

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 10
    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
    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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
    SOC: Muslim Immigration and Integration
    6 ECTS
    Lecturer(s)

    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: Organizational Theory
    6 ECTS
    Lecturer(s)

    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
    SOC: Poverty and Social Exclusion in Europe
    6 ECTS
    Lecturer(s)

    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
  • Political Science

    Dissertation Tutorial: Political Science
    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
    2 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: BAS
    Credits: 2
    Course Content

    Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will get exposure to the different faculty and have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

    Assignment: Come-up with a research project idea that is informed theoretically or methodologically by insights from one or several CDSS faculty presentations (outside of your particular field) in this class. Write-up your idea and describe a potential research design in a short 3-page paper. The paper is due October 31st.

    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
    BAS: Mathematics for Social Scientists
    2 ECTS
    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 tbc Link
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)

    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
    MET: Quantitative Methods (formerly Multivariate Analyses (Theory + Lab Course))
    6 + 2 ECTS
    Lecturer(s)

    Course Type: core 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/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    Carlos Gueiros

    Course Type: core 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
    RES: CDSS Workshop: Political Science
    2 ECTS
    Lecturer(s)

    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
    2 ECTS
    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)
    6 + 3 ECTS
    Lecturer(s)

    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: Machine Learning for Social Scientists (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)

    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 Summary

    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: Research Design (Lecture + Tutorial)
    6 + 3 ECTS
    Lecturer(s)

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 10
    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
    POL: Advanced Topics in Comparative Politics:
    ECTS
    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 Comparative Politics: Political Behavior in Context
    10 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: POL
    Credits: 10
    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
    POL: Advanced Topics in International Politics:
    10 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: POL
    Credits: 10
    Course Content

    tbc

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

    BAS: Current Research Perspectives
    2 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: BAS
    Credits: 2
    Course Content

    Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will get exposure to the different faculty and have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

    Assignment: Come-up with a research project idea that is informed theoretically or methodologically by insights from one or several CDSS faculty presentations (outside of your particular field) in this class. Write-up your idea and describe a potential research design in a short 3-page paper. The paper is due October 31st.

    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
    BAS: Mathematics for Social Scientists
    2 ECTS
    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 tbc Link
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)

    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
    RES: AC2/BC3 Colloquia I
    2 ECTS
    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 Clinical Psychology
    2 ECTS
    Lecturer(s)

    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 Cognitive Psychology
    2 ECTS
    Lecturer(s)

    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 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 tbc
    RES: CDSS Workshop: Research in Social Cognition
    2 ECTS
    Lecturer(s)

    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

    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)
    6 + 3 ECTS
    Lecturer(s)

    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: Machine Learning for Social Scientists (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)

    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 Summary

    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))
    6 + 2 ECTS
    Lecturer(s)

    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: Research Design (Lecture + Tutorial)
    6 + 3 ECTS
    Lecturer(s)

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 10
    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
    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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
    PSY: Advanced topics in Cognitive Psychology
    4 ECTS
    Lecturer(s)

    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
    PSY: Advanced Topics in Work and Organizational Psychology
    4 ECTS
    Lecturer(s)

    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

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Social Sciences Fall 2024