Fall 2022

  • Sociology

    Dissertation Tutorial: Sociology
    0 ECTS
    Lecturer(s)
    Course Type: core course
    Course Content

    Doctoral theses supervised by Henning Hillmann, Florian Keusch, Irena Kogan, Frauke Kreuter, and Katja Möhring respectively, will be discussed.

    Schedule
    Tutorial
    Kogan, biweekly 05.09.22 - 28.11.22 Monday 10:15 - 11:45
    Keusch 05.09.22 - 05.12.22 Monday 13:00 - 14:30
    Kalter 06.09.22 - 06.12.22 Tuesday 10:15 - 11:45
    Hillmann 06.09.22 - 06.12.22 Tuesday 15:30 - 17:00
    Ebbinghaus, biweekly 07.09.22 - 30.11.22 Wednesday 08:30 - 10:00
    BAS: Current Research Perspectives
    2 ECTS
    Lecturer(s)
    Course Type: core course
    Course Number: BAS
    Credits: 2
    Course Content

    The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science 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 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
    09.09.22 Friday 10:15 - 13:30 online
    23.09.22 - 07.10.22 Friday 10:15 - 13:30 online
    BAS: Mathematics for Social Scientists
    2 ECTS
    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
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 211 in B6, 30-32
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)
    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

    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
    06.09.22 - 06.12.22 Tuesday 12:00 - 13:30 B244 in A5, 6 entrance B
    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.

    RES: MZES A Colloquium "European Societies and their Integration"
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    Please refer to the MZES webpages for dates and times.

    Social Norms and Mechanisms of Normative Change
    6 ECTS
    Course Type: elective course
    Credits: 6
    Course Content

    tbc

     

    Course requirements & assessment

    Active participation, presentation, project proposal (graded)

    Schedule
    Seminar
    07.09.22 - 07.12.22 Wednesday 13:45 - 15:15 B318 in A5, 6 entrance B
    MET: Advanced Statistical Modeling with R and Stan (for CDSS doctoral students only, registration via MZES - closed)
    4 or 6 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4 or 6
    Course Content

    Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.

    To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.

    CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.

    Schedule
    Workshop
    22.09.22 - 20.10.22 Thursday 09:00 - 12:00 tbc
    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
    Tutorial
    Dr. Sandra Morgenstern 06.09.22 - 06.12.22 Tuesday 15:30 - 17:00 B 243 in A5 + Zoom
    Malte Grönemann 08.09.22 - 08.12.22 Thursday 12:00 - 13:30 tbc
    Lecture
    06.09.22 - 06.12.22 Tuesday 13:45 - 15:15 B 244 in A5 + Zoom
    MET: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6 + 2
    Course Content

    Summary

    This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

    Course assessment & requirements

    3 mock exam (pass/fail), active participation, term paper (graded)

    The course will be taught by Prof. Joseph Sakshaug

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 10:15 - 11:45 B 244 in A5, 6
    Lecture
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 B 244 in A5, 6
    MET: Machine Learning (IE675b)
    9 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 9
    Course Content

    tbc

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 A 101 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 A 101 in B6, 23-25
    MET: 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.

    In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

    Course requirements & assessment

    Take-home exam (graded)

    Schedule
    Tutorial
    Oliver Rittmann 07.09.22 - 07.12.22 Wednesday 12:00 - 13:30 A 103 in B6, 23-25
    Tbc 08.09.22 - 08.12.22 Thursday 10:15 - 11:45 C 217 in A5, 6
    Lion Behrens 09.09.22 - 09.12.22 Friday 10:15 - 11:45 A 102 in B6, 23-25
    Lecture
    07.09.22 - 07.12.22 Wednesday 08:30 - 10:00 B 244 in A5, 6
    MET: Regression & classification: Basic & advanced topics with illustrations in R
    4 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The seminar gives an overview of

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

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

    Literature

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

    Course requirements & assessment

    Participation and written exam (graded)

    Schedule
    Seminar
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 C216 in A5, 6 entrance C
    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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Tutorial
    07.09.22 - 07.12.22 Wednesday 13:45 - 15:15 A102 in B6, 23-25
    08.09.22 - 08.12.22 Thursday 15:30 - 17:00 A 103 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 13:45 - 15:15 A 103 in B6, 23-25
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    Various ECTS
    Course Type: elective course
    Course Number: MET
    Credits: Various
    Course Content

    Further SMiP courses open to CDSS doctoral students are:

    tbc

     

    Registration for SMiP courses must be done directly via Annette Förster, SMiP Administration Assistant (foerster@smip.uni-mannheim.de).

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)
    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

    Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

    Registration by e-mail to Annette Förster (foerster@smip.uni-mannheim.de)

    Deadline

    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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
    Tutorial
    09.09.22 - 09.12.22 Friday 08:30 - 10:00 C 217 in A5, 6
    Lecture
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 B 244 in A5, 6
    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Course Type: elective course
    Course Number: MET/PSY
    Credits: 4
    Course Content

    lab.js is a simple, graphical tool to help you build studies for the web and the laboratory – in addition, it is free and open-source. Many standard tasks can be implemented in lab.js  using its graphical user interface. In addition, more complex tasks can be realized through the underlying programming language JavaScript. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of lab.js. No prior knowledge of the software or JavaScript is required. As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

    Course requirements & assessment

    As an assignment (graded), participants will create their own experiment based on the requirements discussed in the workshop.

    Schedule
    Seminar
    07.10.22 Friday 10:15 - 15:15 tbc
    08.10.22 Saturday 10:15 - 17:00 tbc
    21.10.22 Friday 10:15 - 15:15 tbc
    22.10.22 Saturday 10:15 - 17:00 tbc
    RES (Bridge Course): Women in Leadership
    5 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Course Content

    This course takes an interdisciplinary approach to examine the gender gap in leadership positions. We will analyze the psychological and economic reasons for the low fraction of women in leadership. While leadership positions are defined broadly and range from politics to public and private institutions, a special emphasis will be on the academic environment. The course will highlight women’s educational and labor market choices, their fertility decisions, and their preferences. We will also examine structural hurdles for women to reach the top, for example stereotypes, discrimination, and social norms. Finally, the effectiveness of gender equality measures – such as quota systems – will be discussed. In addition to the theoretical and empirical fundamentals, the course also comprises two hands-on practical sessions taught by experienced instructors in which students’ rhetoric and negotiation skills are trained.

    The course consists of four core building blocks:

    1. Women in Leadership: The Economic Perspective.
    2. Women in Leadership: The Psychological Perspective.
    3. “Raise Your Voice” – Rhetoric Training
    4. “Raise Your Pay” – Negotiation Training

    Full course syllabus & dates

    Schedule
    Lecture
    07.09.22 Wednesday 10:15 - 11:45 O 048
    21.09.22 - 19.10.22 Wednesday 10:15 - 11:45 1st two dates O 048 then in O 145
    26.10.22 - 16.11.22 Wednesday 10:15 - 13:30 O 048
    SOC: Comparative Methods and Process-Tracing Analysis
    6 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    The comparative method is according to Durkheim ‘the’ method of sociology. This seminar provides an introduction to comparative strategies and methods, particular those used in cross-national comparison of modern welfare states and market economies. In the seminar, the different quantitative and qualitative methods and strategies to compare internationally and to analyse processes over time will be discussed. It begins with an overview of the traditional approaches to historical and comparative sociology (Durkheim, Weber) and the differences in current research practice between variable-and case-oriented sociological analysis. Comparative welfare state analysis and the varieties of capitalism perspective use macro-comparative typologies to explain cross-national differences, using both qualitative and quantitative methods to explore cross-national differences. Qualitative comparative methods (QCA, fuzzy set), comparative or time-related quantitative methods (cluster analysis, pooled time series) will be discussed. In addition, the method of within-case analysis, in particular process tracing, that seeks to establish tests for evaluating evidence about causal mechanism over time will be discussed. The application of these methods and approaches will be illustrated by examples from comparative studies of welfare states and market economies. Finally, recent debates on the pro/cons of using comparative methods will be discussed.

    Literature

    • Goertz, G., & Mahoney, J. (2012). A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton University Press.
    • Beach, D., & Pedersen, R. B. (2013). Process-Tracing Methods. University of Michigan Press.

    Course requirements & assessment

    Active participation and oral presentation during the seminar. A written term paper (graded, max. 5.000 words) within a month after the last session

    Schedule
    Seminar
    06.09.22 - 06.12.22 Tuesday 10:15 - 11:45 B 317 in A5, 6
    SOC: Muslim Immigration and Integration
    6 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    The main aim of this seminar is to discuss current topics in relation to Muslim immigration and integration in Western Europe. Besides theoretical and normative issues the seminar focusses strongly on empirical questions. 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. Mass immigration has resulted in widespread xenophobia and fierce debates on the difficulties of integrating new arrivals. Muslim migration
    seems to pose a particular challenge to Western Europe. In this seminar we will consider how Muslims integrate and how Western societies react 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
    07.09.22 - 07.12.22 Thursday 13:45 - 15:15 B 143 in A5, 6
    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
    • Active participation
    • Term paper (graded)
    Schedule
    Seminar
    08.09.22 - 08.12.22 Thursday 10:15 - 11:45 tbc
    SOC: Poverty and Social Exclusion
    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 conclusion. We will discuss theories on the causes of poverty and social exclusion, learn about different policies throughout Europe to lower poverty, and we will study consequences of poverty in various domains.

    Course requirements & assessment

    Active participation, regular small assignments (developing research questions based on the readings), term paper (graded, max. 5000 words) deadline 22 January 2023

    Schedule
    Seminar
    biweekly 07.09.22 - 21.09.22 Wednesday 08:30 - 11:45 tbc
    12.10.22 Wednesday 08:30 - 11:45 tbc
    biweekly 26.10.22 - 07.12.22 08:30 - 11:45 tbc
  • Political Science

    Dissertation Tutorial: Political Science
    0 ECTS
    Lecturer(s)
    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

    The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science 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 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
    09.09.22 Friday 10:15 - 13:30 online
    23.09.22 - 07.10.22 Friday 10:15 - 13:30 online
    BAS: Mathematics for Social Scientists
    2 ECTS
    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
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 211 in B6, 30-32
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)
    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

    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
    06.09.22 - 06.12.22 Tuesday 12:00 - 13:30 B244 in A5, 6 entrance B
    MET: 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.

    In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

    Course requirements & assessment

    Take-home exam (graded)

    Schedule
    Tutorial
    Oliver Rittmann 07.09.22 - 07.12.22 Wednesday 12:00 - 13:30 A 103 in B6, 23-25
    Tbc 08.09.22 - 08.12.22 Thursday 10:15 - 11:45 C 217 in A5, 6
    Lion Behrens 09.09.22 - 09.12.22 10:15 - 11:45 A 102 in B6, 23-25
    Lecture
    07.09.22 - 07.12.22 Wednesday 08:30 - 10:00 B 244 in A5, 6
    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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
    Tutorial
    09.09.22 - 09.12.22 Friday 08:30 - 10:00 C 217 in A5, 6
    Lecture
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 B 244 in A5, 6
    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 and SFB 884) 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
    05.09.22 - 05.12.22 Monday 08:30 - 10:00 211 in B6, 30-32
    RES: MZES B Colloquium "European Political Systems and their Integration"
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    Please refer to the MZES webpages for dates and times.

    MET: Advanced Statistical Modeling with R and Stan (for CDSS doctoral students only, registration via MZES - closed)
    4 or 6 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4 or 6
    Course Content

    Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.

    To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.

    CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.

    Schedule
    Workshop
    22.09.22 - 20.10.22 Thursday 09:00 - 12:00 tbc
    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
    Tutorial
    Dr. Sandra Morgenstern 06.09.22 - 06.12.22 Tuesday 15:30 - 17:00 B 243 in A5 + Zoom
    Malte Grönemann 08.09.22 - 08.12.22 Thursday 12:00 - 13:30 tbc
    Lecture
    06.09.22 - 06.12.22 Tuesday 13:45 - 15:15 B 244 in A5 + Zoom
    MET: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6 + 2
    Course Content

    Summary

    This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

    Course assessment & requirements

    3 mock exam (pass/fail), active participation, term paper (graded)

    The course will be taught by Prof. Joseph Sakshaug

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 10:15 - 11:45 B 244 in A5, 6
    Lecture
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 B 244 in A5, 6
    MET: Machine Learning (IE675b)
    9 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 9
    Course Content

    tbc

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 A 101 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 A 101 in B6, 23-25
    MET: Regression & classification: Basic & advanced topics with illustrations in R
    4 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The seminar gives an overview of

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

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

    Literature

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

    Course requirements & assessment

    Participation and written exam (graded)

    Schedule
    Seminar
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 C216 in A5, 6 entrance C
    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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Tutorial
    07.09.22 - 07.12.22 Wednesday 13:45 - 15:15 A102 in B6, 23-25
    08.09.22 - 08.12.22 Thursday 15:30 - 17:00 A 103 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 13:45 - 15:15 A 103 in B6, 23-25
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    Various ECTS
    Course Type: elective course
    Course Number: MET
    Credits: Various
    Course Content

    Further SMiP courses open to CDSS doctoral students are:

    tbc

     

    Registration for SMiP courses must be done directly via Annette Förster, SMiP Administration Assistant (foerster@smip.uni-mannheim.de).

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)
    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

    Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

    Registration by e-mail to Annette Förster (foerster@smip.uni-mannheim.de)

    Deadline

    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Course Type: elective course
    Course Number: MET/PSY
    Credits: 4
    Course Content

    lab.js is a simple, graphical tool to help you build studies for the web and the laboratory – in addition, it is free and open-source. Many standard tasks can be implemented in lab.js  using its graphical user interface. In addition, more complex tasks can be realized through the underlying programming language JavaScript. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of lab.js. No prior knowledge of the software or JavaScript is required. As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

    Course requirements & assessment

    As an assignment (graded), participants will create their own experiment based on the requirements discussed in the workshop.

    Schedule
    Seminar
    07.10.22 Friday 10:15 - 15:15 tbc
    08.10.22 Saturday 10:15 - 17:00 tbc
    21.10.22 Friday 10:15 - 15:15 tbc
    22.10.22 Saturday 10:15 - 17:00 tbc
    POL: Advanced Topics in Comparative Politics: Elections in Comparative Perspective
    10 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: POL
    Credits: 10
    Course Content

    Elections are the central focus of political activity in democracies. The characteristics of politics, parties and electoral systems are fundamental to the outcome of elections, which differ across and within countries. To better understand elections we need to study them comparatively, therefore this course focuses on comparative research on elections. The course focuses on the context in which elections are fought and how this affects electoral outcomes. A number of contextual effects of electoral behaviour will be covered, such as institutional configurations, election campaigns, the strategies of political parties and the importance of events in understanding the dynamics of electoral outcomes. We will consider competing theoretical and empirical explanations of the electoral process in democratic as well as partially democratic and even non-democratic countries.

    Literature

    • Collier, Paul. 2010. Wars, Guns, and Votes: Democracy in Dangerous Places. Harper Collins.
    • Farrell, David. 2011. Electoral Systems: A Comparative Introduction 2nd. ed. Palgrave Macmillan.
    • Thomassen, Jacques. 2014. Elections and Democracy: Representation and Accountability. Oxford University Press.
    Schedule
    Seminar
    05.09.22 - 05.12.22 Monday 13:45 - 15:15 B 317 in A5, 6
    POL: Advanced Topics in Comparative Politics: Political Bias in Science
    10 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: POL
    Credits: 10
    Course Content

    This seminar starts from the premise that the current epistemic political crisis is not rooted in a denial of scientific facts, but in the ubiquitous tendency to evaluate scientific findings selectively and according to one's own political ideas. Researchers, too, are ordinary people and therefore politically motivated information processors. They seek information that supports their political ideas and avoid information that contradicts their ideas. They also seem to consider study results more plausible when they are consistent with their political ideas and to doubt study results that contradict their political beliefs. If this tendency, known as myside bias, meets the possibility of flexible presentation of results and a politically unbalanced disciplinary environment, politically biased findings are a very real danger and epistemic distrust an obvious, if not indicated, consequence. In this seminar we will discuss what political biases in science exist, where they come from, what consequences they have and what we can do about them.

    Course requirements & assessment

    Active class participation, term paper (graded)

    Schedule
    Seminar
    06.09.22 - 06.12.22 Tuesday 13:45 - 15:15 C 217 in A5, 6
    RES (Bridge Course): Women in Leadership
    5 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Course Content

    This course takes an interdisciplinary approach to examine the gender gap in leadership positions. We will analyze the psychological and economic reasons for the low fraction of women in leadership. While leadership positions are defined broadly and range from politics to public and private institutions, a special emphasis will be on the academic environment. The course will highlight women’s educational and labor market choices, their fertility decisions, and their preferences. We will also examine structural hurdles for women to reach the top, for example stereotypes, discrimination, and social norms. Finally, the effectiveness of gender equality measures – such as quota systems – will be discussed. In addition to the theoretical and empirical fundamentals, the course also comprises two hands-on practical sessions taught by experienced instructors in which students’ rhetoric and negotiation skills are trained.

    The course consists of four core building blocks:

    1. Women in Leadership: The Economic Perspective.
    2. Women in Leadership: The Psychological Perspective.
    3. “Raise Your Voice” – Rhetoric Training
    4. “Raise Your Pay” – Negotiation Training

    Full course syllabus & dates

    Schedule
    Lecture
    07.09.22 Wednesday 10:15 - 11:45 O 048
    21.09.22 - 19.10.22 Wednesday 10:15 - 11:45 1st two dates O 048 then in O 145
    26.10.22 - 16.11.22 Wednesday 10:15 - 13:30 O 048
  • Psychology

    BAS: Current Research Perspectives
    2 ECTS
    Lecturer(s)
    Course Type: core course
    Course Number: BAS
    Credits: 2
    Course Content

    The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science 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 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
    09.09.22 Friday 10:15 - 13:30 online
    23.09.22 - 07.10.22 Friday 10:15 - 13:30 online
    BAS: Mathematics for Social Scientists
    2 ECTS
    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
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 211 in B6, 30-32
    MET: Crafting Social Science Research
    6 ECTS
    Lecturer(s)
    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

    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
    06.09.22 - 06.12.22 Tuesday 12:00 - 13:30 B244 in A5, 6 entrance B
    RES: AC2/BC3 Colloquia I
    2 ECTS
    Lecturer(s)
    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.

    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
    06.09.22 - 06.12.22 Tuesday 09:00 - 10:00 016-017 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
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 519 in L13, 15-17
    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
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 B317 in A5, 6
    MET: Advanced Statistical Modeling with R and Stan (for CDSS doctoral students only, registration via MZES - closed)
    4 or 6 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4 or 6
    Course Content

    Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.

    To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.

    CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.

    Schedule
    Workshop
    22.09.22 - 20.10.22 Thursday 09:00 - 12:00 tbc
    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
    Tutorial
    Dr. Sandra Morgenstern 06.09.22 - 06.12.22 Tuesday 15:30 - 17:00 B 243 in A5 + Zoom
    Malte Grönemann 08.09.22 - 08.12.22 Thursday 12:00 - 13:30 tbc
    Lecture
    06.09.22 - 06.12.22 Tuesday 13:45 - 15:15 B 244 in A5 + Zoom
    MET: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6 + 2
    Course Content

    Summary

    This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

    Course assessment & requirements

    3 mock exam (pass/fail), active participation, term paper (graded)

    The course will be taught by Prof. Joseph Sakshaug

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 10:15 - 11:45 B 244 in A5, 6
    Lecture
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 B 244 in A5, 6
    MET: Machine Learning (IE675b)
    9 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 9
    Course Content

    tbc

    Schedule
    Tutorial
    06.09.22 - 06.12.22 Tuesday 08:30 - 10:00 A 101 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 12:00 - 13:30 A 101 in B6, 23-25
    MET: 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.

    In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

    Course requirements & assessment

    Take-home exam (graded)

    Schedule
    Tutorial
    Oliver Rittmann 07.09.22 - 07.12.22 Wednesday 12:00 - 13:30 A 103 in B6, 23-25
    Tbc 08.09.22 - 08.12.22 Thursday 10:15 - 11:45 C 217 in A5, 6
    Lion Behrens 09.09.22 - 09.12.22 Friday 10:15 - 11:45 A 102 in B6, 23-25
    Lecture
    07.09.22 - 07.12.22 Wednesday 08:30 - 10:00 B 244 in A5, 6
    MET: Regression & classification: Basic & advanced topics with illustrations in R
    4 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The seminar gives an overview of

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

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

    Literature

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

    Course requirements & assessment

    Participation and written exam (graded)

    Schedule
    Seminar
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 C216 in A5, 6 entrance C
    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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Tutorial
    07.09.22 - 07.12.22 Wednesday 13:45 - 15:15 A102 in B6, 23-25
    08.09.22 - 08.12.22 Thursday 15:30 - 17:00 A 103 in B6, 23-25
    Lecture
    08.09.22 - 08.12.22 Thursday 13:45 - 15:15 A 103 in B6, 23-25
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    Various ECTS
    Course Type: elective course
    Course Number: MET
    Credits: Various
    Course Content

    Further SMiP courses open to CDSS doctoral students are:

    tbc

     

    Registration for SMiP courses must be done directly via Annette Förster, SMiP Administration Assistant (foerster@smip.uni-mannheim.de).

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS doctoral students only)
    6 ECTS
    Lecturer(s)
    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

    Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

    Registration by e-mail to Annette Förster (foerster@smip.uni-mannheim.de)

    Deadline

    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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
    Tutorial
    09.09.22 - 09.12.22 Friday 08:30 - 10:00 C 217 in A5, 6
    Lecture
    05.09.22 - 05.12.22 Monday 10:15 - 11:45 B 244 in A5, 6
    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Course Type: elective course
    Course Number: MET/PSY
    Credits: 4
    Course Content

    lab.js is a simple, graphical tool to help you build studies for the web and the laboratory – in addition, it is free and open-source. Many standard tasks can be implemented in lab.js  using its graphical user interface. In addition, more complex tasks can be realized through the underlying programming language JavaScript. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of lab.js. No prior knowledge of the software or JavaScript is required. As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

    Course requirements & assessment

    As an assignment (graded), participants will create their own experiment based on the requirements discussed in the workshop.

    Schedule
    Seminar
    07.10.22 Friday 10:15 - 15:15 tbc
    08.10.22 Saturday 10:15 - 17:00 tbc
    21.10.22 Friday 10:15 - 15:15 tbc
    22.10.22 Saturday 10:15 - 17:00 tbc
    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
    08.09.22 - 08.12.22 Thursday 15:30 - 17:00 B 244 in A5, 6
    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
    08.09.22 - 08.12.22 Thursday 17:15 - 18:45 B 244 in A5, 6
    RES (Bridge Course): Women in Leadership
    5 ECTS
    Lecturer(s)
    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Course Content

    This course takes an interdisciplinary approach to examine the gender gap in leadership positions. We will analyze the psychological and economic reasons for the low fraction of women in leadership. While leadership positions are defined broadly and range from politics to public and private institutions, a special emphasis will be on the academic environment. The course will highlight women’s educational and labor market choices, their fertility decisions, and their preferences. We will also examine structural hurdles for women to reach the top, for example stereotypes, discrimination, and social norms. Finally, the effectiveness of gender equality measures – such as quota systems – will be discussed. In addition to the theoretical and empirical fundamentals, the course also comprises two hands-on practical sessions taught by experienced instructors in which students’ rhetoric and negotiation skills are trained.

    The course consists of four core building blocks:

    1. Women in Leadership: The Economic Perspective.
    2. Women in Leadership: The Psychological Perspective.
    3. “Raise Your Voice” – Rhetoric Training
    4. “Raise Your Pay” – Negotiation Training

    Full course syllabus & dates

    Schedule
    Lecture
    07.09.22 Wednesday 10:15 - 11:45 O 048
    21.09.22 - 19.10.22 Wednesday 10:15 - 11:45 1st two dates O 048 then in O 145
    26.10.22 - 16.11.22 Wednesday 10:15 - 13:30 O 048

Register

Social Sciences Fall 2022