Fall 2021

  • 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.

    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
    Start time 10 Sep 11am 10.09.21 - 01.10.21 Friday 10:15 - 13:30 Zoom Room 06 Link
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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 Friday, 10 December from 11am to 1pm, room B 144 in A 5 (entrance B)

    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
    09.09.21 - 09.12.21 Thursday 12:00 - 13:45 Sowi Zoom 4 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.

    Course requirements & assessment

    Active participation, draft of dissertation proposal (graded)

    Schedule
    Workshop
    07.09.21 - 07.12.21 Tuesday 12:00 - 13:30 B 317 in A5
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    09.09.21 - 09.12.21 Thursday 10:15 - 11:45 A 101 in B6 + Zoom 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.

    Schedule
    Workshop
    08.09.21 - 08.12.21 16:08 - 17:08 tbc
    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.

    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
    07.09.21 - 07.12.21 Tuesday 13:45 - 15:15 B 144 (A5) + Zoom Link
    Tutorial
    07.09.21 - 07.12.21 Tuesday 15:30 - 17:00 Sowi Zoom 20
    09.09.21 - 09.12.21 Thursday 12:00 - 13:30 B 243 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 exams (pass/fail), active participation, term paper (graded)

    Schedule
    Lecture
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 08:30 - 10:00 Sowi Zoom 02 Link
    Tutorial
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 10:15 - 11:45 Sowi Zoom 02 Link
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)
    Oliver Rittmann
    Viktoriia Semenova

    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.

    Graded assignments include homeworks, a mid-term exam and data analysis projects.

    Schedule
    Lecture
    08.09.21 - 08.12.21 Wednesday 08:30 - 10:00 B 144 in A5 + Zoom Link
    Tutorial
    Viktoriia Semenova 06.09.21 - 06.12.21 Monday 12:00 - 13:30 C 108 in A5 or Zoom pls check Portal Link
    David Grundmanns 07.09.21 - 07.12.21 Tuesday 17:15 - 18:45 C 108 in A5 or Zoom pls check Portal
    Oliver Rittmann 09.09.21 - 09.12.21 Thursday 10:15 - 11:45 C 108 in A5 or Zoom pls check Portal
    MET: Programming in R and beyond
    4 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
    The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

    http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

    Literature will be given during the course

    Course requirements and assessment

    Regular participation in the course; non-graded test

    Schedule
    Seminar
    biweekly 10.09.21 - 03.12.21 Friday 13:45 - 17:00 Sowi Zoom 09 Link
    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
    06.09.21 - 06.12.21 Monday 08:30 - 10:00 Sowi Zoom 05 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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Lecture
    09.09.21 - 09.12.21 Thursday 13:45 - 15:15 Sowi Zoom 06 Link
    Tutorial
    08.09.21 - 08.12.21 Wednesday 13:45 - 15:15 A 102 in B6 Link
    09.09.21 - 09.12.21 Thursday 15:30 - 17:00 Sowi Zoom 06 Link
    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:

    07 October On the quantification of model uncertainty: A Bayesian perspective

    27 October Introduction to R: Basics

    28 October Introduction to R: Advanced

    29 October Rules of Good Scientific Practise

    08 & 09 November An introduction to data analysis with the generalized additive model

    11 & 12 November Introduction to SoSciSurvey

    17 & 18 November Introduction to Bayesian Modeling

    02 & 03 December Hypothesis Evaluation Using the Bayes Factor

    08 & 09 December Robust Bayesian Cognitive Modeling

    12 & 13 January Browser-based Experimentation with lab.js

    18 January Methodology of Replication Studies

    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 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

    Foundations 1 (Instructor: Thorsten Meiser, Assignment: Exercises in statistical modeling and analysis)

    14 and 15 October 2021
    19 November 2021
    14 January 2022

    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 8 September

    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    Samuel Müller

    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
    06.09.21 - 06.12.21 Monday 10:15 - 11:45 B 244 in A5 + Zoom Link
    Tutorial
    10.09.21 - 10.12.21 Friday 08:30 - 10:00 Sowi Zoom 08
    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Lecturer(s)
    Sophie Scharf

    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
    15.10.21 friday 10:15 - 15:15 Sowi Zoom 25 Link
    16.10.21 Saturday 10:15 - 17:00 Sowi Zoom 25
    29.10.21 Friday 10:15 - 15:15 Sowi Zoom 25
    30.10.21 10:15 - 17:00 Sowi Zoom 25
    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

    Schedule
    Workshop
    26.10.21 Tuesday 10:00 - 13:30 Tbc
    29.10.21 Friday 10:00 - 13:30 Tbc
    04.11.21 Thursday 09:00 - 17:00 Tbc
    05.11.21 Friday 09:00 - 17:00 Tbc
    Seminar
    08.09.21 - 13.10.21 Wednesday 10:15 - 11:45 Tbc
    SOC: Economy & the Welfare State: Structuralist Theory
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    The aim of this course is o introduce students to structuralist theorizing in the social sciences. One way to characterize structuralist thinking is to posit the primacy of relations over attributes: considering kinship, for example, it is not meaningful to speak of being a father without any reference to sons and daughters. In other words, any social role is inherently relational because it always entails a role complement (father-son, or father-daughter). As we will see throughout the course, much of social life and human behavior can be understood with reference to such relational pairs. Pairs often come in the form of binary oppositions: nature-nurture; female-male; young-elder; us-them; and so forth. Drawing on the word of Claude Lévi-Strauss in particular, the course aims (a) to systematically understand the building blocks of structuralist thinking, and (b) to understand its implications for social organizations and cohesion writ large, that is, what holds societies together in the long run. Substantive empirical evidence will come from systems of social exchange, governed by norms of reciprocity; from the contrast between restricted and generalized exchange; the formation and ambiguities of role structures; and from the structural analysis of culture, in particular in the form of myths that provide a foundation for social organization in indigenous societies. Students planning to take this class should e prepared to consider empirical examples that come from rather exotic societies, remote in place and time from our contemprary society. 

    Course requirements & assessment

    Complete all assigned readings ahead of class. Contribute creatively to class discussions. Write three memos (3-4 pages) on three selected sections of the class (graded).

    Schedule
    Seminar
    06.09.21 - 06.12.21 Monday 10:15 - 11:45 Sowi Zoom 01 Link
    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
    09.09.21 - 09.12.21 Thursday 08:30 - 10:00 Sowi Zoom 03 Link
    SOC: Replication & Reproduction of Experimental Social Science Research
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    The purpose of this class is to learn how to conduct cutting-edge experimental social science research by gaining first-hand experience in replicating research published in flagship journals (e.g. American Sociological Review, European Sociological Review, PNAS, American Political Science Review, etc.) as well as extending an existing experimental study with original data. Replications are an important but so far still largely neglected feature of the scientific process whereby we verify and expand our cumulative knowledge of empirical findings. Recently several large-scale replication projects in the social sciences have started to evaluate the validity of our research results, often finding evidence that results cannot be entirely replicated due to various reasons (e.g. coding errors, alternative statistical modelling specification, p-hacking, bugs in statistical software packages, etc.). This course aims to contribute to this open science and replication movement by helping students to develop a transparency routine for their future (research) careers. In addition, this course will improve students’ data literacy by teaching them how to best do replications and provide them with practical knowledge about how to meet the highest standards of reproducibility when conducting and documenting empirical social science research.

    The course consists of two parts: In the first part, students select a published study with access to the data and reproduce the reported results.  In the second part, students work together on an extension of one of the selected studies including pre-registration, new data collection (e.g. survey, online experiment), data analysis and writing of a paper, which can possibly be submitted as joint publication to an academic journal. 

    Course requirements & assessment

    Active participation in class/ peer-feedback/individual consultations, final paper (graded)

     

    Schedule
    Seminar
    07.09.21 - 07.12.21 Tuesday 12:00 - 13:30 Sowi Zoom 20 Link
    SOC: Social Mobility in Europe
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    "Like father, like son!" "Someone's a chip off the old block!" "To follow in his father's foot-steps!" There is a variety of popular proverbs describing the same (sociological) fact: Social origin, i.e. the class or income position of parents, has a large effect on the future life-chances of children. For example, a child from a working class background has considerably lower prospects of achieving a position as a professional compared to a child from parents who work as professionals. And the same is true vice versa. Asking about this lack of social mobility (or, contrary, asking about the amount of social mobility) across generations is one of the classic questions in sociology. The seminar will offer a comprehensive view on con-cepts of intergenerational (and intragenerational) social mobility, on theoretical approaches to explain social mobility, on quantitative methodological approaches and on empirical re-sults regarding the amount of social mobility in Europe. Students are expected to read and discuss the most prominent articles in the field, including rather technical/statistical state-of-the-art applications of theories and new methodologies. The seminar encourages students to think critically of the concepts, theories and empirical applications and invites students to develop their own research questions.

    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
    biweekly 15.09.21 - 27.10.21 Wednesday 08:30 - 11:45 Sowi Zoom 19
    weekly 17.11.21 - 01.12.21 Tuesday 08:30 - 11:45 Sowi Zoom 19
  • 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
    Start time 10 Sep 11am 10.09.21 - 01.10.21 Friday 10:15 - 13:30 Zoom Room 06 Link
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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 Friday, 10 December from 11am to 1pm, room B 144 in A 5 (entrance B)

    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
    09.09.21 - 09.12.21 Thursday 12:00 - 13:45 Sowi Zoom 4 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.

    Course requirements & assessment

    Active participation, draft of dissertation proposal (graded)

    Schedule
    Workshop
    07.09.21 - 07.12.21 Tuesday 12:00 - 13:30 B 317 in A5
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)
    Oliver Rittmann
    Viktoriia Semenova

    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.

    Graded assignments include homeworks, a mid-term exam and data analysis projects.

    Schedule
    Lecture
    08.09.21 - 08.12.21 Wednesday 08:30 - 10:00 B 144 in A5 + Zoom Link
    Tutorial
    Viktoriia Semenova 06.09.21 - 06.12.21 Monday 12:00 - 13:30 C 108 in A5 or Zoom pls check Portal Link
    David Grundmanns 07.09.21 - 07.12.21 Tuesday 17:15 - 18:45 C 108 in A5 or Zoom pls check Portal
    Oliver Rittmann 09.09.21 - 09.12.21 Thursday 10:15 - 11:45 C 108 in A5 or Zoom pls check Portal
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    09.09.21 - 09.12.21 Thursday 10:15 - 11:45 A 101 in B6 + Zoom Link
    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    Samuel Müller

    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
    06.09.21 - 06.12.21 Monday 10:15 - 11:45 B 244 in A5 + Zoom Link
    Tutorial
    10.09.21 - 10.12.21 Friday 08:30 - 10:00 Sowi Zoom 08
    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
    08.09.21 - 08.12.21 Wednesday 12:00 - 13:30 Sowi Zoom 10 Link
    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: 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
    07.09.21 - 07.12.21 Tuesday 13:45 - 15:15 B 144 (A5) + Zoom Link
    Tutorial
    07.09.21 - 07.12.21 Tuesday 15:30 - 17:00 Sowi Zoom 20
    09.09.21 - 09.12.21 Thursday 12:00 - 13:30 B 243 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 exams (pass/fail), active participation, term paper (graded)

    Schedule
    Lecture
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 08:30 - 10:00 Sowi Zoom 02 Link
    Tutorial
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 10:15 - 11:45 Sowi Zoom 02 Link
    MET: Programming in R and beyond
    4 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
    The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

    http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

    Literature will be given during the course

    Course requirements and assessment

    Regular participation in the course; non-graded test

    Schedule
    Seminar
    biweekly 10.09.21 - 03.12.21 Friday 13:45 - 17:00 Sowi Zoom 09 Link
    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
    06.09.21 - 06.12.21 Monday 08:30 - 10:00 Sowi Zoom 05 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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Lecture
    09.09.21 - 09.12.21 Thursday 13:45 - 15:15 Sowi Zoom 06 Link
    Tutorial
    08.09.21 - 08.12.21 Wednesday 13:45 - 15:15 A 102 in B6 Link
    09.09.21 - 09.12.21 Thursday 15:30 - 17:00 Sowi Zoom 06 Link
    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:

    07 October On the quantification of model uncertainty: A Bayesian perspective

    27 October Introduction to R: Basics

    28 October Introduction to R: Advanced

    29 October Rules of Good Scientific Practise

    08 & 09 November An introduction to data analysis with the generalized additive model

    11 & 12 November Introduction to SoSciSurvey

    17 & 18 November Introduction to Bayesian Modeling

    02 & 03 December Hypothesis Evaluation Using the Bayes Factor

    08 & 09 December Robust Bayesian Cognitive Modeling

    12 & 13 January Browser-based Experimentation with lab.js

    18 January Methodology of Replication Studies

    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 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

    Foundations 1 (Instructor: Thorsten Meiser, Assignment: Exercises in statistical modeling and analysis)

    14 and 15 October 2021
    19 November 2021
    14 January 2022

    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 8 September

    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Lecturer(s)
    Sophie Scharf

    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
    15.10.21 friday 10:15 - 15:15 Sowi Zoom 25 Link
    16.10.21 Saturday 10:15 - 17:00 Sowi Zoom 25
    29.10.21 Friday 10:15 - 15:15 Sowi Zoom 25
    30.10.21 10:15 - 17:00 Sowi Zoom 25
    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
    06.09.21 - 06.12.21 Monday 13:45 - 15:15 B 143 in A5 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
    07.09.21 - 07.12.21 Tuesday 12:00 - 13:30 Sowi Zoom 03 Link
    POL: Advanced Topics in International Politics: Agents of Political Violence
    10 ECTS
    Lecturer(s)

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

    This seminar discusses seminal and current work on agents of political violence. We analyze the role and characteristics of the military, including drivers and counterstrategies to coup d’états. A large part of the course will focus on irregular armed agents that are aligned with the stage, engaging with the growing research on militias, death squads, civil defense forces and paramilitary groups. We assess national and transnational drivers of their formation, their termination, how they affect political violence during and outside of civil wars. The seminars will be student-led. Over the course of the seminar you will develop your own research question on one of the topics discussed in the seminar and carry out your own research. Additionally, you are expected to write one book review, read all required materials, engage in the discussions and provide feedback on other student’s work.

    Course requirements & assessment

    One book review (1,000 words), one written feedback on a student’s research proposal, preparing and initiating one seminar discussion (likely to be done as a group assignment)

    Research paper (graded)

    Schedule
    Seminar
    07.09.21 - 07.12.21 Tuesday 13:45 - 15:15 Sowi Zoom 03 Link
    POL: Advanced Topics in International Politics: Crises & Polarization
    10 ECTS
    Lecturer(s)

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

    tbc

     

    Course requirements & assessment

    Active attendance, presentation, term paper (graded)

    Schedule
    Seminar
    07.09.21 - 07.12.21 Tuesday 13:45 - 15:15 Sowi Zoom 11 Link
    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

    Schedule
    Workshop
    26.10.21 Tuesday 10:00 - 13:30 Tbc
    29.10.21 Friday 10:00 - 13:30 Tbc
    04.11.21 Thursday 09:00 - 17:00 Tbc
    05.11.21 Friday 09:00 - 17:00 Tbc
    Seminar
    08.09.21 - 13.10.21 Wednesday 10:15 - 11:45 Tbc
  • 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
    Start time 10 Sep 11am 10.09.21 - 01.10.21 Friday 10:15 - 13:30 Zoom Room 06 Link
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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 Friday, 10 December from 11am to 1pm, room B 144 in A 5 (entrance B)

    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
    09.09.21 - 09.12.21 Thursday 12:00 - 13:45 Sowi Zoom 4 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.

    Course requirements & assessment

    Active participation, draft of dissertation proposal (graded)

    Schedule
    Workshop
    07.09.21 - 07.12.21 Tuesday 12:00 - 13:30 B 317 in A5
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MET
    Credits: 6
    Course Content

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    09.09.21 - 09.12.21 Thursday 10:15 - 11:45 A 101 in B6 + Zoom Link
    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
    07.09.21 - 07.12.21 Tuesday 08:30 - 10:00 Sowi Zoom 10 Link
    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.

    Talk schedule

    Competences acquired

    Improvement in research skills and communication of research results.

    Schedule
    Workshop
    06.09.21 - 06.12.21 Monday 15:30 - 17:00 C 217 in A5, 6 entrance C Link
    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
    06.09.21 - 06.12.21 Monday 10:15 - 11:45 Sowi Zoom 09
    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
    07.09.21 - 07.12.21 Tuesday 13:45 - 15:15 B 144 (A5) + Zoom Link
    Tutorial
    07.09.21 - 07.12.21 Tuesday 15:30 - 17:00 Sowi Zoom 20
    09.09.21 - 09.12.21 Thursday 12:00 - 13:30 B 243 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 exams (pass/fail), active participation, term paper (graded)

    Schedule
    Lecture
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 08:30 - 10:00 Sowi Zoom 02 Link
    Tutorial
    Dr. Theresa Gessler 07.09.21 - 07.12.21 Tuesday 10:15 - 11:45 Sowi Zoom 02 Link
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)
    Oliver Rittmann
    Viktoriia Semenova

    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.

    Graded assignments include homeworks, a mid-term exam and data analysis projects.

    Schedule
    Lecture
    08.09.21 - 08.12.21 Wednesday 08:30 - 10:00 B 144 in A5 + Zoom Link
    Tutorial
    Viktoriia Semenova 06.09.21 - 06.12.21 Monday 12:00 - 13:30 C 108 in A5 or Zoom pls check Portal Link
    David Grundmanns 07.09.21 - 07.12.21 Tuesday 17:15 - 18:45 C 108 in A5 or Zoom pls check Portal
    Oliver Rittmann 09.09.21 - 09.12.21 Thursday 10:15 - 11:45 C 108 in A5 or Zoom pls check Portal
    MET: Programming in R and beyond
    4 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 4
    Course Content

    The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
    The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

    http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

    Literature will be given during the course

    Course requirements and assessment

    Regular participation in the course; non-graded test

    Schedule
    Seminar
    biweekly 10.09.21 - 03.12.21 Friday 13:45 - 17:00 Sowi Zoom 09 Link
    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
    06.09.21 - 06.12.21 Monday 08:30 - 10:00 Sowi Zoom 05 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)
    Tutorial: Homework, oral participation, presentation

    Schedule
    Lecture
    09.09.21 - 09.12.21 Thursday 13:45 - 15:15 Sowi Zoom 06 Link
    Tutorial
    08.09.21 - 08.12.21 Wednesday 13:45 - 15:15 A 102 in B6 Link
    09.09.21 - 09.12.21 Thursday 15:30 - 17:00 Sowi Zoom 06 Link
    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:

    07 October On the quantification of model uncertainty: A Bayesian perspective

    27 October Introduction to R: Basics

    28 October Introduction to R: Advanced

    29 October Rules of Good Scientific Practise

    08 & 09 November An introduction to data analysis with the generalized additive model

    11 & 12 November Introduction to SoSciSurvey

    17 & 18 November Introduction to Bayesian Modeling

    02 & 03 December Hypothesis Evaluation Using the Bayes Factor

    08 & 09 December Robust Bayesian Cognitive Modeling

    12 & 13 January Browser-based Experimentation with lab.js

    18 January Methodology of Replication Studies

    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 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

    Foundations 1 (Instructor: Thorsten Meiser, Assignment: Exercises in statistical modeling and analysis)

    14 and 15 October 2021
    19 November 2021
    14 January 2022

    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 8 September

    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    Samuel Müller

    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
    06.09.21 - 06.12.21 Monday 10:15 - 11:45 B 244 in A5 + Zoom Link
    Tutorial
    10.09.21 - 10.12.21 Friday 08:30 - 10:00 Sowi Zoom 08
    MET/PSY: Creating Experiments with lab.js
    4 ECTS
    Lecturer(s)
    Sophie Scharf

    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
    15.10.21 friday 10:15 - 15:15 Sowi Zoom 25 Link
    16.10.21 Saturday 10:15 - 17:00 Sowi Zoom 25
    29.10.21 Friday 10:15 - 15:15 Sowi Zoom 25
    30.10.21 10:15 - 17:00 Sowi Zoom 25
    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. We will present cutting edge research conducted in Cognitive Psychology at the University of Mannheim. After an introductory overview of Cognitive Psychology and its advanced methods by A. Bröder, various researchers will present their current work. The following reseachers are planned as lecturers (changes possible): Dr. Nina Arnold, Dr. Martin Brandt, Prof. A. Bröder, Prof. E. Erdfelder, Dr. Michael Gräf, Dr. Meike Kroneisen, Dr. Lena Naderevic, Prof. Rüdiger Pohl und Dr. Monika Undorf.

    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
    09.09.21 - 09.12.21 Thursday 15:30 - 17:00 SN 169 + Zoom Link
    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
    09.09.21 - 09.12.21 Thursday 17:15 - 18:45 Sowi Zoom 08 Link
    PSY: Work & Organizational Psychology: Work & Health
    4 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: PSY
    Credits: 4
    Course Content

    Organizations are increasingly concerned about the health of their 
    employees.This is reflected in integrating health-related concepts and health-promoting measures into organizational strategies.

    The seminar deals with the question of how can organizations act in order to maintain the health of their employees in the long term. The individual health-related actions of employees should also be discussed.

    The seminar consists of two parts. In the first part, we work together to investigate state-of-the-art research on the topic using current literature.

    During this time, the students have the opportunity to decide on a topic that they will work on in depth as part of their term paper and in the second part of the seminar in project groups.

    In the context of project groups, in the second part of the seminar, the students will put the learned and self-developed content into practice in the form of a practical "product" (e.g., poster, video, brochure etc.). At the end of the seminar, the project group results will be presented.

    In addition to a content-related discussion, the seminar also places particular emphasis on the work and organizational psychological methods with which health is examined at work.

    Course requirements & assessment
    Course participation and homework assignments, term paper (graded).

     

    Schedule
    Seminar
    08.09.21 - 08.12.21 Wednesday 08:30 - 10:00 Sowi Zoom 08 Link
    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

    Schedule
    Workshop
    26.10.21 Tuesday 10:00 - 13:30 Tbc
    29.10.21 Friday 10:00 - 13:30 Tbc
    04.11.21 Thursday 09:00 - 17:00 Tbc
    05.11.21 Friday 09:00 - 17:00 Tbc
    Seminar
    08.09.21 - 13.10.21 Wednesday 10:15 - 11:45 Tbc