Doctoral theses supervised by Henning Hillmann, Florian Keusch, Irena Kogan, Frauke Kreuter, and Katja Möhring respectively, will be discussed.
Tutorial | |||||||
Kogan, biweekly | 05.09.22 – 28.11.22 | Monday | 10:15 – 11:45 | ||||
Keusch | 05.09.22 – 05.12.22 | Monday | 13:00 – 14:30 | ||||
Kalter | 06.09.22 – 06.12.22 | Tuesday | 10:15 – 11:45 | ||||
Hillmann | 06.09.22 – 06.12.22 | Tuesday | 15:30 – 17:00 | ||||
Ebbinghaus, biweekly | 07.09.22 – 30.11.22 | Wednesday | 08:30 – 10:00 | ||||
Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will 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.
Lecture | |||||||
09.09.22 | Friday | 10:15 – 13:30 | online | Link | |||
23.09.22 – 07.10.22 | Friday | 10:15 – 13:30 | online | ||||
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 Monday, 12 December 2022 from 10–12 in room C 012 in A5, 6.
Basic readings:
Additional readings:
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | 211 in B6, 30–32 | Link | |||
The goal of this course is to jump-start students with their dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions, the development of a theoretical framework, the specification of the methodology and planned empirical analysis. You should be prepared to address the following questions: What makes that an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?
This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently, students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.
Course requirements & assessment
Mandatory readings, active participation in class, homework assignments, presentation of research proposal and performing as a discussant of proposals of peers in a workshop format, research proposal term paper (circa 10 pages, graded)
Workshop | |||||||
06.09.22 – 06.12.22 | Tuesday | 12:00 – 13:30 | B244 in A5, 6 entrance B | Link | |||
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.
Workshop | |||||||
28.09.22 – 07.12.22 | Wednesday | 16:00 – 17:00 | 209 in B6, 30–32 | ||||
Please refer to the MZES webpages for dates and times.
Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.
To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.
CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.
Workshop | |||||||
22.09.22 – 20.10.22 | Thursday | 09:00 – 12:00 | tbc | ||||
Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.
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
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.
Tutorial | |||||||
Dr. Sandra Morgenstern | 06.09.22 – 06.12.22 | Tuesday | 15:30 – 17:00 | B 243 in A5 | Link | ||
Malte Grönemann | 08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | B 244 in A5, 6 | Link | ||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 13:45 – 15:15 | B 244 in A5 | Link | |||
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)
The course will be taught by Prof. Joseph Sakshaug
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 10:15 – 11:45 | online | Link | |||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | Online | Link | |||
tbc
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | A 101 in B6, 23–25 | ||||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | A 101 in B6, 23–25 | ||||
The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.
The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.
In the accompanying course “Tutorial Multivariate Analyses” students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.
Course requirements & assessment
Take-home exam (graded)
Tutorial | |||||||
Oliver Rittmann | 07.09.22 – 07.12.22 | Wednesday | 12:00 – 13:30 | A 103 in B6, 23–25 | Link | ||
Domantas Undzenas | 08.09.22 – 08.12.22 | Thursday | 10:15 – 11:45 | C 217 in A5, 6 | Link | ||
Lion Behrens | 09.09.22 – 02.12.22 | Friday | 10:15 – 11:45 | A 102 in B6, 23–25 | Link | ||
Lecture | |||||||
07.09.22 – 07.12.22 | Wednesday | 08:30 – 10:00 | B 244 in A5,6 | Link | |||
The seminar gives an overview of
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)
Seminar | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | C216 in A5, 6 entrance C | Link |
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
Tutorial | |||||||
07.09.22 – 07.12.22 | Wednesday | 13:45 – 15:15 | A102 in B6, 23–25 | Link | |||
Daria Szafran | 08.09.22 – 08.12.22 | Thursday | 15:30 – 17:00 | A 103 in B6, 23–25 | Link | ||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 13:45 – 15:15 | A 103 in B6, 23–25 | Link | |||
Following SMiP courses open to CDSS doctoral students. Please register for the course program via the registration link before 15 August 2022. Please always refer to the complete SMiP program for continuous updates and if you should have any questions relating to any of the courses. Generally 0,8 ECTS are given per workshop day but there are exceptions.
Research Organization and Useful Software
Instructor: | Martin Schnuerch |
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Date: | 12 October 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Rules of Good Scientific Practice
Instructor: | Arndt Bröder |
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Date: | 02 November (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Many-Analysts Projects in Cognitive Psychology: A Practical Workshop
Instructor: | Alexandra Sarafoglou |
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Dates: | 27 September 2022 (10:00 a.m. – 6:00 p.m.) and 11 January 2023 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room TBA) |
The Science of Science Communication
Instructor: | Asheley Landrum |
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Dates: | 04 October (12:00 a.m. – 6:00 pm. ) and 05 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Signal Detection Theory – Application and Criticism
Instructor: | Anne Voormann & Constantin Meyer-Grant |
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Dates: | 20 October (10:00 a.m. – 6:00 p.m. ) and 21 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Freiburg (Engelbergerstr. 41; Room: Seminarraum SR 4003) |
Instructor: | David Izydorczyk |
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Date: | 03 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: TBA) |
Instructor: | David Izydorczyk |
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Date: | 04 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Substantive SMiP Research Topics in Mannheim
Instructors: | Arndt Bröder, Edgar Erdfelder, Martin Schnuerch & Nikoletta Symeonidou |
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Date: | 07 December 2022 (10 a.m. – 6 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Introduction to Bayesian Modeling
Instructor: | Martin Schnuerch |
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Dates: | 08 December (10:00 a.m. – 6:00 p.m. ) and 09 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room 1st day: D 002; B6, 27–29, Bauteil D; Room 2nd day: 211; B6, 30–32) |
(Online) Tools for Experimental Psychologists
Instructors: | Maren Mayer, Barbara Kreis, Tobias Rebholz, Marcel Schreiner & Annika Stump |
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Dates: | 15 December (10:00 a.m. – 6:00 pm. ) and 16 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Dynamic Latent Class Models for the Detection of Inattention
Instructor: | Holger Brandt |
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Dates: | 23 January (10:00 a.m. – 6:00 pm. ) and 24 January 2023 (9:00 a.m. – 5:00 p.m.) |
Location: | Tübingen or Mannheim (Room: TBA) |
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
Block 1: | Dates: 13 October (10:00 a.m.- 6:00 p.m.) and 14 October 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
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Block 2: | Dates: 10 November (10:00 a.m.- 6:00 p.m.) and 11 November 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
Block 3: | Dates: 12 January (10:00 a.m.- 6:00 p.m.) and 13 January 2023 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: O 142 [Schloss, Ostflügel]) |
Please register for the course program via the registration link before 15 August 2022. Please refer to the complete SMiP program for continuous updates.
CDSS doctoral students in psychology only who attend all three modules of this course can:
- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.
- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.
Please contact the CDSS Center Manager to discuss the above mentioned possibilities.
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
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.
Tutorial | |||||||
09.09.22 – 09.12.22 | Friday | 08:30 – 10:00 | C 217 in A5, 6 | Link | |||
Lecture | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | B 244 in A5, 6 | Link | |||
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.
Seminar | |||||||
07.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | Link | |||
08.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 | ||||
21.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | ||||
22.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 |
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
Lecture | |||||||
07.09.22 | Wednesday | 10:15 – 11:45 | O 048 | Link | |||
21.09.22 – 19.10.22 | Wednesday | 10:15 – 11:45 | 1st two dates O 048 then in O 145 | ||||
26.10.22 – 16.11.22 | Wednesday | 10:15 – 13:30 | O 048 | ||||
Tbc
Course requirements & assignments
Active participation in class / data collection / peer-feedback: 30 hours (1 ECTS), Response Papers /assignments: 60 hours (2 ECTS), Learning Portfolio: 30 hours (1 ECTS), Contribution to the survey design and final publication: 60 hours (2 ECTS). This is a graded course
Seminar | |||||||
07.09.22 – 07.12.22 | Thursday | 13:45 – 15:15 | B 143 in A5, 6 | Link |
The comparative method is according to Durkheim ‘the’ method of sociology. This seminar provides an introduction to comparative strategies and methods, particular those used in cross-national comparison of modern welfare states and market economies. In the seminar, the different quantitative and qualitative methods and strategies to compare internationally and to analyse processes over time will be discussed. It begins with an overview of the traditional approaches to historical and comparative sociology (Durkheim, Weber) and the differences in current research practice between variable-and case-oriented sociological analysis. Comparative welfare state analysis and the varieties of capitalism perspective use macro-comparative typologies to explain cross-national differences, using both qualitative and quantitative methods to explore cross-national differences. Qualitative comparative methods (QCA, fuzzy set), comparative or time-related quantitative methods (cluster analysis, pooled time series) will be discussed. In addition, the method of within-case analysis, in particular process tracing, that seeks to establish tests for evaluating evidence about causal mechanism over time will be discussed. The application of these methods and approaches will be illustrated by examples from comparative studies of welfare states and market economies. Finally, recent debates on the pro/
Literature
Course requirements & assessment
Active participation and oral presentation during the seminar. A written term paper (graded, max. 5.000 words) within a month after the last session
Seminar | |||||||
06.09.22 – 06.12.22 | Tuesday | 10:15 – 11:45 | B 317 in A5, 6 | Link |
This advanced seminar will explore classic and recent social science research that seeks to explain variation in organizational behavior and development. We will consider a variety of research questions that tap into both formal and informal ways of organizing: what kinds of institutions are necessary to make economic organization work? Where do such institutions come from? Why do we observe very different outcomes across contexts even though they share the same market-supporting institutions? Why do some organizations survive even though they face the most unfavorable environments? How do conditions at the time of an organization's birth shape its development? To address these and further questions, we will rely both on recent theoretical advances and on empirical studies in a various settings.
Course requirements & assessment
Seminar | |||||||
08.09.22 – 08.12.22 | Thursday | 10:15 – 11:45 | D007 in B6, 27–29 | Link |
Poverty and social exclusion are extreme forms of inequality in modern societies. In Europe, these phenomena show up in different forms and imply different consequences for the people at risk. The seminar will provide an introduction into various concepts, dimensions and measures of poverty and social conclusion. We will discuss theories on the causes of poverty and social exclusion, learn about different policies throughout Europe to lower poverty, and we will study consequences of poverty in various domains.
Course requirements & assessment
Active participation, regular small assignments (developing research questions based on the readings), term paper (graded, max. 5000 words) deadline 22 January 2023
Seminar | |||||||
biweekly | 07.09.22 – 21.09.22 | Wednesday | 08:30 – 11:45 | 222 in B6, 4–5 | Link | ||
12.10.22 | Wednesday | 08:30 – 11:45 | 222 in B6, 4–5 | ||||
biweekly | 26.10.22 – 07.12.22 | 08:30 – 11:45 | 222 in B6, 4–5 |
tbc
The course will be taught by Dr. Amalia Alvarez Benjumea
Course requirements & assessment
Active participation, presentation, project proposal (graded)
Seminar | |||||||
07.09.22 – 07.12.22 | Wednesday | 12:00 – 13:30 | Link |
Doctoral theses supervised by professors in the department of Political Science will be discussed.
Please check with individual chairs for dates and times.
Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will 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.
Lecture | |||||||
09.09.22 | Friday | 10:15 – 13:30 | online | Link | |||
23.09.22 – 07.10.22 | Friday | 10:15 – 13:30 | online | ||||
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 Monday, 12 December 2022 from 10–12 in room C 012 in A5, 6.
Basic readings:
Additional readings:
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | 211 in B6, 30–32 | Link | |||
The goal of this course is to jump-start students with their dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions, the development of a theoretical framework, the specification of the methodology and planned empirical analysis. You should be prepared to address the following questions: What makes that an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?
This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently, students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.
Course requirements & assessment
Mandatory readings, active participation in class, homework assignments, presentation of research proposal and performing as a discussant of proposals of peers in a workshop format, research proposal term paper (circa 10 pages, graded)
Workshop | |||||||
06.09.22 – 06.12.22 | Tuesday | 12:00 – 13:30 | B244 in A5, 6 entrance B | Link | |||
The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.
The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.
In the accompanying course “Tutorial Multivariate Analyses” students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.
Course requirements & assessment
Take-home exam (graded)
Tutorial | |||||||
Oliver Rittmann | 07.09.22 – 07.12.22 | Wednesday | 12:00 – 13:30 | A 103 in B6, 23–25 | Link | ||
Domantas Undzenas | 08.09.22 – 08.12.22 | Thursday | 10:15 – 11:45 | C 217 in A5, 6 | Link | ||
Lion Behrens | 09.09.22 – 02.12.22 | 10:15 – 11:45 | A 102 in B6, 23–25 | Link | |||
Lecture | |||||||
07.09.22 – 07.12.22 | Wednesday | 08:30 – 10:00 | B 244 in A5,6 | Link | |||
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
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.
Tutorial | |||||||
09.09.22 – 09.12.22 | Friday | 08:30 – 10:00 | C 217 in A5, 6 | Link | |||
Lecture | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | B 244 in A5, 6 | Link | |||
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.
Workshop | |||||||
05.09.22 – 05.12.22 | Monday | 15:30 – 17:00 | 211 in B6, 30–32 | Link | |||
Please refer to the MZES webpages for dates and times.
Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.
To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.
CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.
Workshop | |||||||
22.09.22 – 20.10.22 | Thursday | 09:00 – 12:00 | tbc | ||||
Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.
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
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.
Tutorial | |||||||
Dr. Sandra Morgenstern | 06.09.22 – 06.12.22 | Tuesday | 15:30 – 17:00 | B 243 in A5 | Link | ||
Malte Grönemann | 08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | B 244 in A5, 6 | Link | ||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 13:45 – 15:15 | B 244 in A5 | Link | |||
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)
The course will be taught by Prof. Joseph Sakshaug
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 10:15 – 11:45 | online | Link | |||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | Online | Link | |||
tbc
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | A 101 in B6, 23–25 | ||||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | A 101 in B6, 23–25 | ||||
The seminar gives an overview of
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)
Seminar | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | C216 in A5, 6 entrance C | Link |
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
Tutorial | |||||||
07.09.22 – 07.12.22 | Wednesday | 13:45 – 15:15 | A102 in B6, 23–25 | Link | |||
Daria Szafran | 08.09.22 – 08.12.22 | Thursday | 15:30 – 17:00 | A 103 in B6, 23–25 | Link | ||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 13:45 – 15:15 | A 103 in B6, 23–25 | Link | |||
Following SMiP courses open to CDSS doctoral students. Please register for the course program via the registration link before 15 August 2022. Please always refer to the complete SMiP program for continuous updates and if you should have any questions relating to any of the courses. Generally 0,8 ECTS are given per workshop day but there are exceptions.
Research Organization and Useful Software
Instructor: | Martin Schnuerch |
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Date: | 12 October 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Rules of Good Scientific Practice
Instructor: | Arndt Bröder |
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Date: | 02 November (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Many-Analysts Projects in Cognitive Psychology: A Practical Workshop
Instructor: | Alexandra Sarafoglou |
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Dates: | 27 September 2022 (10:00 a.m. – 6:00 p.m.) and 11 January 2023 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room TBA) |
The Science of Science Communication
Instructor: | Asheley Landrum |
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Dates: | 04 October (12:00 a.m. – 6:00 pm. ) and 05 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Signal Detection Theory – Application and Criticism
Instructor: | Anne Voormann & Constantin Meyer-Grant |
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Dates: | 20 October (10:00 a.m. – 6:00 p.m. ) and 21 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Freiburg (Engelbergerstr. 41; Room: Seminarraum SR 4003) |
Instructor: | David Izydorczyk |
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Date: | 03 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: TBA) |
Instructor: | David Izydorczyk |
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Date: | 04 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Substantive SMiP Research Topics in Mannheim
Instructors: | Arndt Bröder, Edgar Erdfelder, Martin Schnuerch & Nikoletta Symeonidou |
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Date: | 07 December 2022 (10 a.m. – 6 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Introduction to Bayesian Modeling
Instructor: | Martin Schnuerch |
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Dates: | 08 December (10:00 a.m. – 6:00 p.m. ) and 09 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room 1st day: D 002; B6, 27–29, Bauteil D; Room 2nd day: 211; B6, 30–32) |
(Online) Tools for Experimental Psychologists
Instructors: | Maren Mayer, Barbara Kreis, Tobias Rebholz, Marcel Schreiner & Annika Stump |
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Dates: | 15 December (10:00 a.m. – 6:00 pm. ) and 16 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Dynamic Latent Class Models for the Detection of Inattention
Instructor: | Holger Brandt |
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Dates: | 23 January (10:00 a.m. – 6:00 pm. ) and 24 January 2023 (9:00 a.m. – 5:00 p.m.) |
Location: | Tübingen or Mannheim (Room: TBA) |
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
Block 1: | Dates: 13 October (10:00 a.m.- 6:00 p.m.) and 14 October 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
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Block 2: | Dates: 10 November (10:00 a.m.- 6:00 p.m.) and 11 November 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
Block 3: | Dates: 12 January (10:00 a.m.- 6:00 p.m.) and 13 January 2023 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: O 142 [Schloss, Ostflügel]) |
Please register for the course program via the registration link before 15 August 2022. Please refer to the complete SMiP program for continuous updates.
CDSS doctoral students in psychology only who attend all three modules of this course can:
- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.
- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.
Please contact the CDSS Center Manager to discuss the above mentioned possibilities.
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.
Seminar | |||||||
07.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | Link | |||
08.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 | ||||
21.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | ||||
22.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 |
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.
Course requirements & assessment
Mandatory readings, case study, agenda-setting presentation based on the set readings, short proposal of research paper, presentation and critique of a peer's proposal and active participation in class. Term paper (ca. 5000 words, graded)
Literature
Seminar | |||||||
05.09.22 – 05.12.22 | Monday | 13:45 – 15:15 | B 317 in A5, 6 | Link |
This seminar starts from the premise that the current epistemic political crisis is not rooted in a denial of scientific facts, but in the ubiquitous tendency to evaluate scientific findings selectively and according to one's own political ideas. Researchers, too, are ordinary people and therefore politically motivated information processors. They seek information that supports their political ideas and avoid information that contradicts their ideas. They also seem to consider study results more plausible when they are consistent with their political ideas and to doubt study results that contradict their political beliefs. If this tendency, known as myside bias, meets the possibility of flexible presentation of results and a politically unbalanced disciplinary environment, politically biased findings are a very real danger and epistemic distrust an obvious, if not indicated, consequence. In this seminar we will discuss what political biases in science exist, where they come from, what consequences they have and what we can do about them.
Course requirements & assessment
Active class participation, term paper (graded)
Seminar | |||||||
06.09.22 – 06.12.22 | Tuesday | 13:45 – 15:15 | C 217 in A5, 6 |
This seminar focuses on human rights violations as a theoretical concept and as an empirical phenomenon. It tackles questions such as: What are human rights violations? How can we study human rights violations empirically? When are human rights violations most likely to occur? Which groups of individuals face an elevated risk of human rights violations? And which types of interventions are effective to promote human rights? The seminar will be structured according to different political contexts analysing distinct dynamics of repression during 'peace' and armed conflicts. The seminar sheds light on different perpetrators of human rights abuse and on patterns of civilian victimization and sexual violence during armed conflicts. The seminar also covers selective types of external interventions to conflict-torn societies such as UN Peacekeeping missions or international criminal prosecutions. To bridge the gap between theory and practice, current real-world examples of human rights violations will be discussed in relation to the theoretical concepts introduced in class. By the end of the seminar, students are expected to write their own empirical research paper on a topic of their own choice related to the field of human rights.
Course requirements & assessment
tudents are expected to participate regularly in the seminar and to cover the required readings. Additionally, participants are expected to holdone presentation in a group of two students on one of the optional readings on the Syllabus. Finally, students must submit a research proposal, outlining their ideas for the final research paper. The research proposal will be discussed in detail in the context of the “mini-research conference” by the end of the seminar. Empirical research paper (graded)
Seminar | |||||||
08.09.22 – 08.12.22 | Thursday | 10:15 – 11:45 | C112 in A5, 6 | Link |
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
Lecture | |||||||
07.09.22 | Wednesday | 10:15 – 11:45 | O 048 | Link | |||
21.09.22 – 19.10.22 | Wednesday | 10:15 – 11:45 | 1st two dates O 048 then in O 145 | ||||
26.10.22 – 16.11.22 | Wednesday | 10:15 – 13:30 | O 048 | ||||
Description: The course “Current Research Perspectives” introduces first year CDSS doctoral students to the theoretically informed research approaches and substantive research fields that build the stronghold of social science research in Mannheim. A series of talks provide first year CDSS doctoral students with an overview of current scholarly debates and ongoing research in the fields of political science, psychology, and sociology. CDSS faculty members will present an outline of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will 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.
Lecture | |||||||
09.09.22 | Friday | 10:15 – 13:30 | online | Link | |||
23.09.22 – 07.10.22 | Friday | 10:15 – 13:30 | online | ||||
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 Monday, 12 December 2022 from 10–12 in room C 012 in A5, 6.
Basic readings:
Additional readings:
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | 211 in B6, 30–32 | Link | |||
The goal of this course is to jump-start students with their dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions, the development of a theoretical framework, the specification of the methodology and planned empirical analysis. You should be prepared to address the following questions: What makes that an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?
This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently, students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.
Course requirements & assessment
Mandatory readings, active participation in class, homework assignments, presentation of research proposal and performing as a discussant of proposals of peers in a workshop format, research proposal term paper (circa 10 pages, graded)
Workshop | |||||||
06.09.22 – 06.12.22 | Tuesday | 12:00 – 13:30 | B244 in A5, 6 entrance B | Link | |||
Please check with individual chairs in the Psychology Department for dates and times of research colloquia as well as registration.
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.
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.
Improvement in research skills and communication of research results.
Workshop | |||||||
06.09.22 – 06.12.22 | Tuesday | 09:00 – 10:00 | 016–017 in L 13, 15–17 | ||||
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.
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.
Improvement in research skills and communication of research results.
Workshop | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | 519 in L13, 15–17 | ||||
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.
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
Workshop | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | B317 in A5, 6 | ||||
Statistical models are widely used in the social sciences for measurement, prediction, and hypothesis testing. While popular statistical software packages cover a growing number of pre-implemented model types, the diversification of substantive research domains and the increasing complexity of data structures drive persistently high demand for custom modeling solutions. Implementing such custom solutions requires that researchers build their own models and use them to obtain reliable estimates of quantities of substantive interest. Bayesian methods offer a powerful and versatile infrastructure for these tasks. Yet, seemingly high entry costs still deter many social scientists from fully embracing Bayesian methods.
To push past these initial hurdles and to equip participants with the required skills for custom statistical modeling, this two-day workshop offers an advanced introduction to statistical modeling using R and Stan. Following a targeted review of the underlying mechanics of generalized linear models and core concepts of Bayesian inference, the course introduces participants to Stan, a platform for statistical modeling and Bayesian statistical inference. Participants will get an overview of the programming language, the R interface RStan, and the workflow for Bayesian model building, inference, and convergence diagnosis. Applied exercises provide participants with the chance to write and run various model types and to process the resulting estimates into publication-ready graphs.
CDSS members can take this class for credit by participating in an additional 180 min session, to be held later in the semester, and submitting either a short technical report (4 ECTS) or a research paper (6 ECTS) by January 31, 2023.
Workshop | |||||||
22.09.22 – 20.10.22 | Thursday | 09:00 – 12:00 | tbc | ||||
Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.
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
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.
Tutorial | |||||||
Dr. Sandra Morgenstern | 06.09.22 – 06.12.22 | Tuesday | 15:30 – 17:00 | B 243 in A5 | Link | ||
Malte Grönemann | 08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | B 244 in A5, 6 | Link | ||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 13:45 – 15:15 | B 244 in A5 | Link | |||
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)
The course will be taught by Prof. Joseph Sakshaug
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 10:15 – 11:45 | online | Link | |||
Lecture | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | Online | Link | |||
tbc
Tutorial | |||||||
06.09.22 – 06.12.22 | Tuesday | 08:30 – 10:00 | A 101 in B6, 23–25 | ||||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 12:00 – 13:30 | A 101 in B6, 23–25 | ||||
The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.
The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.
In the accompanying course “Tutorial Multivariate Analyses” students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.
Course requirements & assessment
Take-home exam (graded)
Tutorial | |||||||
Oliver Rittmann | 07.09.22 – 07.12.22 | Wednesday | 12:00 – 13:30 | A 103 in B6, 23–25 | Link | ||
Domantas Undzenas | 08.09.22 – 08.12.22 | Thursday | 10:15 – 11:45 | C 217 in A5, 6 | Link | ||
Lion Behrens | 09.09.22 – 02.12.22 | Friday | 10:15 – 11:45 | A 102 in B6, 23–25 | Link | ||
Lecture | |||||||
07.09.22 – 07.12.22 | Wednesday | 08:30 – 10:00 | B 244 in A5,6 | Link | |||
The seminar gives an overview of
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)
Seminar | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | C216 in A5, 6 entrance C | Link |
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
Tutorial | |||||||
07.09.22 – 07.12.22 | Wednesday | 13:45 – 15:15 | A102 in B6, 23–25 | Link | |||
Daria Szafran | 08.09.22 – 08.12.22 | Thursday | 15:30 – 17:00 | A 103 in B6, 23–25 | Link | ||
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 13:45 – 15:15 | A 103 in B6, 23–25 | Link | |||
Following SMiP courses open to CDSS doctoral students. Please register for the course program via the registration link before 15 August 2022. Please always refer to the complete SMiP program for continuous updates and if you should have any questions relating to any of the courses. Generally 0,8 ECTS are given per workshop day but there are exceptions.
Research Organization and Useful Software
Instructor: | Martin Schnuerch |
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Date: | 12 October 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Rules of Good Scientific Practice
Instructor: | Arndt Bröder |
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Date: | 02 November (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Many-Analysts Projects in Cognitive Psychology: A Practical Workshop
Instructor: | Alexandra Sarafoglou |
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Dates: | 27 September 2022 (10:00 a.m. – 6:00 p.m.) and 11 January 2023 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room TBA) |
The Science of Science Communication
Instructor: | Asheley Landrum |
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Dates: | 04 October (12:00 a.m. – 6:00 pm. ) and 05 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Signal Detection Theory – Application and Criticism
Instructor: | Anne Voormann & Constantin Meyer-Grant |
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Dates: | 20 October (10:00 a.m. – 6:00 p.m. ) and 21 October 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Freiburg (Engelbergerstr. 41; Room: Seminarraum SR 4003) |
Instructor: | David Izydorczyk |
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Date: | 03 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: TBA) |
Instructor: | David Izydorczyk |
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Date: | 04 November 2022 (10:00 a.m. – 6:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Substantive SMiP Research Topics in Mannheim
Instructors: | Arndt Bröder, Edgar Erdfelder, Martin Schnuerch & Nikoletta Symeonidou |
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Date: | 07 December 2022 (10 a.m. – 6 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Introduction to Bayesian Modeling
Instructor: | Martin Schnuerch |
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Dates: | 08 December (10:00 a.m. – 6:00 p.m. ) and 09 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room 1st day: D 002; B6, 27–29, Bauteil D; Room 2nd day: 211; B6, 30–32) |
(Online) Tools for Experimental Psychologists
Instructors: | Maren Mayer, Barbara Kreis, Tobias Rebholz, Marcel Schreiner & Annika Stump |
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Dates: | 15 December (10:00 a.m. – 6:00 pm. ) and 16 December 2022 (9:00 a.m. – 5:00 p.m.) |
Location: | Mannheim (Room: 211; B6, 30–32) |
Dynamic Latent Class Models for the Detection of Inattention
Instructor: | Holger Brandt |
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Dates: | 23 January (10:00 a.m. – 6:00 pm. ) and 24 January 2023 (9:00 a.m. – 5:00 p.m.) |
Location: | Tübingen or Mannheim (Room: TBA) |
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
Block 1: | Dates: 13 October (10:00 a.m.- 6:00 p.m.) and 14 October 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
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Block 2: | Dates: 10 November (10:00 a.m.- 6:00 p.m.) and 11 November 2022 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: D 002; B6, 27–29; Bauteil B) |
Block 3: | Dates: 12 January (10:00 a.m.- 6:00 p.m.) and 13 January 2023 (9:00 a.m. – 5.00 p.m.) Location: Mannheim (Room: O 142 [Schloss, Ostflügel]) |
Please register for the course program via the registration link before 15 August 2022. Please refer to the complete SMiP program for continuous updates.
CDSS doctoral students in psychology only who attend all three modules of this course can:
- let it count against the mandatory course Mathematics for Social Scientists (2 ECTS) and have 4 ECTS credited towards elective requirements in the MET module.
- let it count against the core course 'Theory Building and Causal Inference' (6ECTS), which is taught in spring.
Please contact the CDSS Center Manager to discuss the above mentioned possibilities.
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
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.
Tutorial | |||||||
09.09.22 – 09.12.22 | Friday | 08:30 – 10:00 | C 217 in A5, 6 | Link | |||
Lecture | |||||||
05.09.22 – 05.12.22 | Monday | 10:15 – 11:45 | B 244 in A5, 6 | Link | |||
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.
Seminar | |||||||
07.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | Link | |||
08.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 | ||||
21.10.22 | Friday | 10:15 – 15:15 | 108 CIP-Pool in B6, 30–32 | ||||
22.10.22 | Saturday | 10:15 – 17:00 | 108 CIP-Pool in B6, 30–32 |
This Lecture provides an advanced treatment of research methods in cognitive psychology as well as an overview of research topics of Cognitive Psychology in Mannheim.
Exemplary Topics
Literature
Course requirements & assessment:
Active participation, final written exam (90 mins, graded)
Knowledge of the main research strategies and theoretical developments in the study of memory; ability to discuss empirical studes critically
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 15:30 – 17:00 | B 244 in A5, 6 | Link | |||
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).
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.
Room B244 in A5, 6 has been reserved so that you can attend it from there using your laptop if you don't/can't want to work from home.
Lecture | |||||||
08.09.22 – 08.12.22 | Thursday | 17:15 – 18:45 | online | Link | |||
tbc
Workshop | |||||||
11.11.22 | Friday | 10:00 – 17:00 | 211 in B6, 30–32 | ||||
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
Lecture | |||||||
07.09.22 | Wednesday | 10:15 – 11:45 | O 048 | Link | |||
21.09.22 – 19.10.22 | Wednesday | 10:15 – 11:45 | 1st two dates O 048 then in O 145 | ||||
26.10.22 – 16.11.22 | Wednesday | 10:15 – 13:30 | O 048 | ||||