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

Spring 2024

  • Sociology

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

    Doctoral theses supervised by professors in the department of Sociology will be discussed. Please check with individual chairs for dates and times.

    DIS: Dissertation Proposal Workshop
    2+8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: DIS
    Credits: 2+8
    Prerequisites

    Crafting Social Science Research, Literature Review

    Course Content

    The goal of this course is to provide support and crucial feedback on writing students' 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)?

    Nota bene: Further meeting dates will be determined during the first session. Only the first meeting will be held online, the remainder of the semester you'll meet in person.

    Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.

    Schedule
    Workshop
    further dates tbd 13.02.24 Tuesday 10:15 – 11:45 Zoom Link
    MET: Theory Building and Causal Inference
    6 ECTS
    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.

    The course will be taught by Prof. Marc Ratkovic, PhD

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    13.02.24 – 28.05.24 Tuesday 08:30 – 10:00 211 in B6, 30–32
    RES: CDSS Workshop: Sociology
    2 ECTS
    Lecturer(s)
    Lars Leszczensky

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

    Participation is mandatory for first to third year CDSS Sociology students. Participation is recommended for later CDSS doctoral candidates, but to no credit.

    The goal of this course is to provide support and crucial feedback for CDSS doctoral candidates in sociology on their ongoing dissertation project. In this workshop CDSS students are expected to play two roles. They should provide feedback to their peers as well as present their own work in order to receive feedback.

    Dates tbd

    Schedule
    Workshop
    13.02.24 – 28.05.24 Tuesday 17:15 – 18:45 209 in B6, 30–32
    RES: Colloquia
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    CDSS doctoral students in political science and sociology can choose freely which weekly colloquium to attend. Colloquia must be attended regularly in year two and three of doctoral studies.

    Please choose from

    MZES A Colloquium “European Societies and their Integration”

    MZES Colloquium B “European Political Systems and their Integration”

    Please refer to the MZES webpages for all further details. The talk announcements will be communicated via the CDSS mailing list as well.

    Alternatively you can attend the Mannheim Research Colloquium on Survey Methods (MaRCS) or the MZES Social Science Data Lab, which will be announced through the Faculty of Social Sciences mailing list.

    RES: English Academic Writing
    3 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 3
    Prerequisites

    CSSR, Literature Review

    Course Content

    The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.

    Course requirements & assessment

    Term paper

    Schedule
    Workshop
    15.02.24 – 23.05.24 Thursday 12:00 – 13:30 B 317 in A5, 6 entrance B Link
    CDSS Job Talk Series: Next GESS
    0 ECTS
    Course Type: elective course
    Course Number: CDSS Job Talk Series
    Course Content

    Johannes Lattmann is the recipient of the 2023 CDSS Young Scholar Award, which he received for his project 'Next GESS – Career Talks for PhD and Master students with Industry, NGOs, and Policy Institutions'.

    For this upcoming semester he organized the “Next GESS” Job Talk Series for doctoral students. Every Thursday, from 16.30 – 17.30 he invites a speaker to talk Online via Zoom about their job and career. The precise schedule can be found in the Syllabus which you can find in the appendix.

    In the context of this series, he has invited speakers from a variety of companies and institutions including IBM, CEPS, OECD, UNESCO and many more. Through their experiences, this series aims to provide insights into exciting career trajectories. Eventually, this series should inform you about career opportunities and provide valuable insights about application processes and the day-to-day work life in different institutions. 

    Each session contains a 15–20 minutes long presentation by the speaker, introducing the respective job or institution. This is followed by a Q&A session in which you can ask questions. 

    Talk schedule

    MET: 13th GESIS Summer School in Survey Methodology & GESIS Seminars
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

    Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

    The GESIS summer school takes place in Cologne from 24 July to 16 August. Detailed information about the summer school program is available on the GESIS website.

    *According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

    MET: Advanced Quantitative Methods
    6+2 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6+2
    Prerequisites

    Knowledge of Multivariate Analysis

    Course Content

    The goal of this course is to provide an introduction into maximum-likelihood estimation.

    Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is mandatory for the assignments which complement the lecture (6 ECTS).

    Literature

    • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
    • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
    • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

    Course requirements & assessment

    Homework assignements, research paper (all graded)

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

    Schedule
    Lecture
    14.02.24 – 29.05.24 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    15.02.24 – 23.05.24 Thursday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Computational Social Science Methods and Digital Behavioral Data
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Please bring your own laptops for use in the course. At least basic knowledge of R is required.

    Course Content

    Computational Social Science is a young research field at the intersection of various social science disciplines, data science and computer science. The goal is to gain new insights into society through large amounts of data and the direct observation of human behavior. CSS relies on two cornerstones: digital behavioral data, which can be collected from online platforms or sensors like smartphones, and computer science methods such as automated text analysis to create appropriate measures for social science research questions. In the course, students will get to know foundational studies, theories and methods used in the field of CSS. We will discuss infrastructural, ethical and legal challenges and how to navigate these to devise appropriate research designs in CSS.
    The course will be application oriented. Students will familiarize themselves with the main applications of CSS methods and implement them in R. The range of applications will cover data management and preprocessing, the application of machine learning, data and results visualization, statistical data analysis and the validation of results. The hands-on application examples will cover questions from various research fields and different data types like social media data or web browsing histories. Equipped with this theoretical and methodological toolkit, students will develop their own CSS research projects.

    The course will be taught by Prof. Sebastian Stier.

    Course requirements & assessment

    Regular small assignments (programming homework, developing research questions and your own project); compulsory attendance; participating in discussions. Written term paper based on an analysis in R graded, (max. 5000 words), deadline: July 31, 2024

    Schedule
    Seminar
    14.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B Link
    28.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    13.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    27.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    03.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    17.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    22.05.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    MET: Generative AI in the Social Sciences
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Course Content

    Selected topics relating to Generative AI in the social sciences are introduced in this seminar. Assigned readings and in-class activities will impart a deeper insight into the current status of research in this field, which is used to determine open questions and perspectives for further research.

    Course taught by Prof. Joe Sakshaug.

    Course requirements & assessment

    For the examination, students write a term paper (5,000 words max.) where they either
    1) carry out an empirical study in a focus area of social science research using Generative AI methods, OR
    2) conduct a critical literature review of Generative AI used in the social sciences.

    Competences acquired

    Upon completion of the module, students are able to:
    • present their basic knowledge in Generative AI applied to social science research fields
    • name the latest Generative AI developments in social science research
    • describe their in-depth knowledge of empirical approaches to Generative AI in the social science research fields covered
    • critically evaluate the empirical literature and applications of Generative AI in the social science research fields covered

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 08:30 – 10:00 Online
    MET: Introduction to Comparative Survey Research
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of comparative survey research. These surveys enable researchers to conduct cross-national/cross-cultural analysis, sometimes even across time. Yet, these important surveys come with their own challenge. The course covers the major steps of implementing and conducting a comparative survey and design decisions at each step. A special focus of the course will be on discussing sources of error that may be introduced by survey design decisions. For illustration purposes and exercise, the course will draw on examples and case studies from well-known cross-national surveys such as the European Social Survey (ESS), the European Values Study (EVS), the Generations and Gender Survey (GGS), the Programme for the International Assessment of Adult Competencies (PIAAC), and the World Values Survey (WVS).

    Course requirements & assessment

    Active participation, homework assignments/oral presentations, term paper (graded)

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Prerequisites

    Some basic knowledge of statistical inference and R is required

    Course Content

    Lecture

    The course provides a broad overview of methods used in longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-section data, using measurements of the same individuals taken repeatedly through time can lead to better causal inferences in some cases, and can also give the possibility to learn more about the dynamics of individual behavior. The first objective of this course is to discuss the advantages of panel data, and the characteristics of the structure of panel data. Then, the course will give an overview of the main models (pooled OLS, fixed effects, random effects, first-differences) and provide the tools to choose betwen these models. The course will also discuss panel generalized linear models. Finally, an overview of event history analysis will be presented.

    Tutorial

    Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

    Tutorial -

    • Homework: students must complete and submit the eight homework and pass at least six.
    •  Assignments: To be completed during the session and submitted by the end of the session.
    Schedule
    Lecture
    12.02.24 – 27.05.24 Monday 10:15 – 11:45 B 143 in A5, 6 entrance B Link
    Tutorial
    13.02.24 – 28.05.24 Tuesday 15:30 – 17:00 B 143 in A5, 6 entrance B 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
    not on 25 March 12.02.24 – 27.05.24 Monday 10:15 – 11:45 A 103 in B6, 23–25 Link
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue

    Please register for the SMiP course program via their online registration tool by 15 February.

    MET: Workshop IRT Modeling – Theory and Applications in R
    4 ECTS
    Lecturer(s)

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

    This workshop provides an introduction to Item Response Theory (IRT) with basic and advanced models for dichotomous and polytomous items. The topics include the Rasch model and extensions with two, three and four item parameters for dichotomous items. Concerning polytomous items, we discuss the partial credit and rating scale model, generalized partial-credit model and graded response model for items with ordinal response format, and the nominal response model for items with categorical response format. In addition, multidimensional IRT models for response styles and IRTree models for multiple response processes are presented.
    The IRT models are outlined with their formal model equations, theoretical assumptions and implications, estimation techniques, and statistical testing procedures. Applications to simulated and real data sets illustrate the use of IRT models for the analysis of individual differences in basic and applied research.
    The workshop includes practical exercises of IRT modeling and analysis with current R packages. Basic knowledge and experience in R, including data management and use of R packages, are required for participation in this workshop.
    The language of instruction is English. The course program includes online meetings, videos and analysis projects as homework.

    Literature

    • Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
    • Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
    • De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
    • Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
    • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
    • Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
    • Course Credit/Exam: Presentation of IRT analysis
    Schedule
    Seminar
    16.02.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32 Link
    15.03.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    26.04.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    24.05.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET 931
    Credits: 5
    Prerequisites

    The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.

    Recommended: Knowledge of basic statistics and prior experience with R or Stata is helpful, but not necessary.

    Course Content

    This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

    In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

    Learning outcomes:

    The specific applications cover a broad set of skills with a focus on design of questionnaires and survey experiments, data analysis and quantitative methods, classification, inference, writing of own reports, and opportunities for own research.

    • Analytical Skills/Problem-Solving:
    Schedule
    Lecture
    Lecture 13.02.24 – 28.05.24 Tuesday 10:15 – 11:45 room O 048
    RES (bridge course): Mental health during dissertations: “Research is Me-Search” (GESS doctoral students only)
    5 ECTS
    Course Type: elective course
    Course Number: RES (bridge course)
    Credits: 5
    Course Content

    Almost every doctoral dissertation is marked by difficult periods and times of frustration, which can also affect one´s mental health.

    Not only aspects directly related to one's dissertation but also structural and/or personal aspects can make it challenging to maintain one's mental health.

    The aim of this course is to get to know and discuss typical risk factors and challenging constellations doctoral students are likely to face during their dissertations. The course will consist of literature-informed/guided group discussions of several predefined topics addressing common difficulties during dissertation projects. During the first session(s), the group will decide the particular topics of interest for each of the sessions based on a brief literature discussion and their personal interests. Then, based on selected literature provided by the lecturer, the students will discuss these topics both from an academic standpoint and from their individual perspective/experience during their dissertation project. Each session will thus serve as information input and offer room for discussion and exchange. The aim of this format is to foster student's knowledge about mental health during dissertation projects and open up possibilities to reflect one´s own situation and standpoints in a group of peers.

    The course will be taught by Dr. Matthias Volz

    Course  requirements & assessment

    Students need to be willing to read articles, and discuss and articulate their own views on typical challenging situations during dissertation projects in guided group discussions.

    Schedule
    Seminar
    bi-weekly 20.02.24 – 19.03.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    bi-weekly 09.04.24 – 21.05.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    RES (Bridge Course): New Perspectives on Economics and Politics
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Prerequisites

    Prerequisites: For each session, students need to have read the respective book in advance. (Detailed schedule will be provided in an introductory session.)

    Form of Assessment: Essay 50 %, Class Participation 50 %


    Friedman, M. (1963). Capitalism and Freedom. University of Chicago Press.
    Deaton, A. (2023). Economics in America: An Immigrant Economist Explores the Land of Inequality. Princeton University Press.
    Sandel, M. J. (2020). The Tyranny of Merit: What’s Become of the Common Good?. Penguin UK.
    Bowles, S. (2016). The moral economy. Yale University Press.
    Formal: Students need to be enrolled a PhD program at the GESS at the University of Mannheim, or the Master of Political Science.
    Required: Willingness to read, discuss, challenge, engage and think for yourself is critical for this course.

    Course Content

    We live in interesting times both, economically and politically. Many observers point to crises and uncertain developments in the economic and political world. Making sense of the nature of these challenges and pointing toward economic and political solutions for the future requires new perspectives. This is a course about the big and bold questions in economics and politics. How can or should economics and politics be organized to best serve society? What does it mean to put humans as they really are at the center of economic and political thinking? What role do morals and values, or dignity and respect, play for the way economics and politics work? What are the implications of digitalization for capitalism and freedom?

    We will try to come to grips with these questions by reading and discussing four key books on various new perspectives at the intersection between economics and politics. The aim of this course is to go as deep as we can and to get as much out of an in-class discussion of the material as possible. Willingness to acquire and read the books is a must. If you are unsure about whether or not you would want to take on the commitment of reading four books in one semester then this course is probably not the right one for you.

    Students need to be willing to read books, form their own opinions on them, and elaborate on and defend their own views in group discussions and a final essay.

    We will meet for an introductory session on Monday 4.3. at 13.45–15.15 and then meet again for a total of seven Thursday and Wednesday sessions after the Easter break: 11.4., 25.4., 2.5., 8.5., 16.5., 23.5., and 29.5. at 8.30–11.45.

    Competences acquired

    Learning outcomes: The aim of this course is to engage in intellectual dialogue, to develop a personal point of view on some of the central economic and political questions we face today, and to allow ourselves to think creatively, freely, and out of the box. After completing this course, students will have read important texts on new perspectives in economics and politics, they will have trained their ability to distill an own point of view from the writings of leading scientists, they will train their writing and discussion skills, and they will train to creatively apply what they have read in writing about the future of economics and politics in our society.

    Schedule
    Lecture
    Introduction to the Course 04.03.24 – 04.03.24 Monday 13:45 – 15:15 L9, 1–2, Room 409
    Lecture 11.04.24 – 11.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 25.04.24 – 25.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 02.05.24 – 02.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 08.05.24 – 08.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 16.05.24 – 16.05.24 Thursday 08:30 – 11:45 B6, 23–25, Room A301
    Lecture 23.05.24 – 23.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 29.05.24 – 29.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409
    SOC: Political Networks
    6 ECTS
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    What explains the rise of the Medici in 15th century Florence? Why did thousands of women join the guerilla war in 1980s El Salvador? What can online book co-purchases tell us about ideological differences between Republicans and Democrats in contemporary America? These are some of the questions we will grapple with as we explore how social scientists have applied network analysis to the study of politics.

    The course is designed as a general introduction to social network analysis, but it focuses heavily on examples from political sociology (and adjacent fields) as one area in which network theories and methodologies have had a great influence. We will treat network analysis both as a theoretical approach that regards relations as the basic building blocks of social life, and as a methodological toolkit for visualizing and analyzing the structure of relations. Many of these methods involve the quantitative measurement of network structures (e.g., the degree to which networks are clustered) and different positions within the network (e.g., central vs. peripheral actors). The course is organized around a set of key concepts and theoretical insights in network analysis – such as weak ties, brokerage, and diffusion – which we will apply to a variety of substantive issues ranging from recruitment into social movements to the emergence of new political identities to the nature of political action.

    The best way to learn about social networks is to work with them, which is why the class has a large practical component. After developing the theoretical foundations in class discussions, students will learn how to analyze networks in a series of practical assignments. The final project will give students an opportunity to follow their own curiosity and apply the analytical tools introduced in class to an empirical context of their choosing.

    This course will be taught by Benjamin Rohr

    Course requirements & assessment

    Regular & active participation, formulation of questions/comments to literature, term paper (graded)

    Schedule
    Seminar
    12.02.24 – 27.05.24 Monday 13:45 – 15:15 B 143 in A5, 6 entrance B
    SOC: Poverty, Inequality and Social Policy
    6 ECTS
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    An increasing base of literature shows ‘disappointing poverty trends’ both in the global North and the global South. Inequality in many societies is on the rise: gains of globalization have been concentrated with a small elite while incomes and wealth of the rest have stagnated or declined. At the same time, school systems and welfare provisions have expanded, aiming at equalizing opportunities. How to explain this paradox of increasing opportunities and disappointing poverty and inequality trends? This seminar examines aspects of poverty and inequality from a sociological perspective, with insights from economics and public policy. It covers contexts of all levels of development, including high- and low- income countries. In the first part, we discuss the definitions and measurements of poverty and inequality, how they vary across countries and change over time, and critically assess how such variation may create identification issues and affect policy responses. In the second part, we review empirical research findings in sociology and economics regarding poverty and inequality in different contexts and globally. We take a closer look at different cross-sectional and longitudinal research designs that consider poverty and inequality as static and dynamic phenomena, and look at different population subgroups: children, workforce, and the elderly. In the third part, we investigate how policy interventions affect poverty and inequality. We examine the effectiveness of cash transfers, employment, care services and taxes as policy responses, and discuss issues related to policy design such as conditionality, targeting, social investment provisions, and their redistributive implications.

    The course will be taught by Prof. Ilze Plavgo

    Course requirements & assessment

    1. Students are expected to read the assigned readings for each week before coming to class, and actively engage in discussions during the seminar sessions.
    2. One oral presentation of one of the assigned readings of approx. 8 minutes with 4–5 PPT slides. PPT slides to be uploaded on the ILIAS one day before class.
    3. Two response papers of 300–400 words each: short reflections about theoretical arguments or questions provoked by the assigned readings of two sessions of choice, uploaded 1 day before class.

    Written term paper (graded) on a topic related to the seminar, in English, composed of two parts with a total of 5,000 words:
    1) Term paper outline (500 words) a week before the last session (Friday 17 May noon)
    2) Term paper (4,500 words, excl. literature, appendix), a month after the last session (1st July noon)

    Schedule
    Seminar
    12.02.24 – 27.05.24 Monday 13:45 – 15:15 B 318 in A5, 6 entrance B 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 footsteps!” 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 (class position, income) of their 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 whose parents work as professionals. And the same is true vice versa. Asking about social mobility or immobility across generations is one of the classic questions in sociology. The seminar will offer a comprehensive view on concepts of intergenerational (and intragenerational) social mobility, on theoretical approaches to explain social mobility, on quantitative methodological approaches and on empirical results regarding the amount of social mobility in Europe. Students are expected to read and discuss the most prominent articles in the field, including 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.

    Written term paper (graded, max. 5000 words), deadline: July 31, 2024

    Schedule
    Seminar
    21.02.24 Wednesday 08:30 – 11:45 C116 in A5, 6 entrance C Link
    28.02.24 Wednesday 08:30 – 11:45 C116 in A5, 6 entrance C
    20.03.24 Wednesday 08:30 – 11:45 C116 in A5, 6 entrance C
    bi-weekly 10.04.24 – 08.05.24 Wednesday 08:30 – 11:45 C116 in A5, 6 entrance C
    15.05.24 Wednesday 08:30 – 11:45 C116 in A5, 6 entrance C
  • Political Science

    Dissertation Tutorial: Political Science
    0 ECTS
    Course Type: core course
    Course Content

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

    Please check with individual chairs for dates and times.

    DIS: Dissertation Proposal Workshop
    2+8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: DIS
    Credits: 2+8
    Prerequisites

    Crafting Social Science Research, Literature Review

    Course Content

    The goal of this course is to provide support and crucial feedback on writing students' 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)?

    Nota bene: Further meeting dates will be determined during the first session. Only the first meeting will be held online, the remainder of the semester you'll meet in person.

    Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.

    Schedule
    Workshop
    further dates tbd 13.02.24 Tuesday 10:15 – 11:45 Zoom Link
    MET: Advanced Quantitative Methods
    6+2 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MET
    Credits: 6+2
    Prerequisites

    Knowledge of Multivariate Analysis

    Course Content

    The goal of this course is to provide an introduction into maximum-likelihood estimation.

    Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is mandatory for the assignments which complement the lecture (6 ECTS).

    Literature

    • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
    • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
    • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

    Course requirements & assessment

    Homework assignements, research paper (all graded)

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

    Schedule
    Lecture
    14.02.24 – 29.05.24 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    15.02.24 – 23.05.24 Thursday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Theory Building and Causal Inference
    6 ECTS
    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.

    The course will be taught by Prof. Marc Ratkovic, PhD

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    13.02.24 – 28.05.24 Tuesday 08:30 – 10:00 211 in B6, 30–32
    RES: CDSS Workshop: Political Science
    2 ECTS
    Lecturer(s)

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

    Participation is mandatory for first to third year CDSS students of Political Science. Participation is recommended for later CDSS PhD candidates, but to no credit.

    Other young researchers in the social sciences affiliated with the University of Mannheim (incl. MZES) are also invited to attend the talks.

    The goal of this course is to provide support and crucial feedback for CDSS doctoral students on their ongoing dissertation project. In this workshop they are expected to play two roles – provide feedback to their peers as well as present their own work in order to receive feedback.

    In order to receive useful feedback, participants are asked to circulate their paper and two related published pieces of research one week before the talk.

    Schedule
    Workshop
    12.02.24 – 27.05.24 Monday 15:30 – 17:00 A 102 in B6, 23–25
    RES: Colloquia
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    CDSS doctoral students in political science and sociology can choose freely which weekly colloquium to attend. Colloquia must be attended regularly in year two and three of doctoral studies.

    Please choose from

    MZES A Colloquium “European Societies and their Integration”

    MZES Colloquium B “European Political Systems and their Integration”

    Please refer to the MZES webpages for all further details. The talk announcements will be communicated via the CDSS mailing list as well.

    Alternatively you can attend the Mannheim Research Colloquium on Survey Methods (MaRCS) or the MZES Social Science Data Lab, which will be announced through the Faculty of Social Sciences mailing list.

    RES: English Academic Writing
    3 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 3
    Prerequisites

    CSSR, Literature Review

    Course Content

    The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.

    Course requirements & assessment

    Term paper

    Schedule
    Workshop
    15.02.24 – 23.05.24 Thursday 12:00 – 13:30 B 317 in A5, 6 entrance B Link
    CDSS Job Talk Series: Next GESS
    0 ECTS
    Course Type: elective course
    Course Number: CDSS Job Talk Series
    Course Content

    Johannes Lattmann is the recipient of the 2023 CDSS Young Scholar Award, which he received for his project 'Next GESS – Career Talks for PhD and Master students with Industry, NGOs, and Policy Institutions'.

    For this upcoming semester he organized the “Next GESS” Job Talk Series for doctoral students. Every Thursday, from 16.30 – 17.30 he invites a speaker to talk Online via Zoom about their job and career. The precise schedule can be found in the Syllabus which you can find in the appendix.

    In the context of this series, he has invited speakers from a variety of companies and institutions including IBM, CEPS, OECD, UNESCO and many more. Through their experiences, this series aims to provide insights into exciting career trajectories. Eventually, this series should inform you about career opportunities and provide valuable insights about application processes and the day-to-day work life in different institutions. 

    Each session contains a 15–20 minutes long presentation by the speaker, introducing the respective job or institution. This is followed by a Q&A session in which you can ask questions. 

    Talk schedule

    MET: 13th GESIS Summer School in Survey Methodology & GESIS Seminars
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

    Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

    The GESIS summer school takes place in Cologne from 24 July to 16 August. Detailed information about the summer school program is available on the GESIS website.

    *According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

    MET: Computational Social Science Methods and Digital Behavioral Data
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Please bring your own laptops for use in the course. At least basic knowledge of R is required.

    Course Content

    Computational Social Science is a young research field at the intersection of various social science disciplines, data science and computer science. The goal is to gain new insights into society through large amounts of data and the direct observation of human behavior. CSS relies on two cornerstones: digital behavioral data, which can be collected from online platforms or sensors like smartphones, and computer science methods such as automated text analysis to create appropriate measures for social science research questions. In the course, students will get to know foundational studies, theories and methods used in the field of CSS. We will discuss infrastructural, ethical and legal challenges and how to navigate these to devise appropriate research designs in CSS.
    The course will be application oriented. Students will familiarize themselves with the main applications of CSS methods and implement them in R. The range of applications will cover data management and preprocessing, the application of machine learning, data and results visualization, statistical data analysis and the validation of results. The hands-on application examples will cover questions from various research fields and different data types like social media data or web browsing histories. Equipped with this theoretical and methodological toolkit, students will develop their own CSS research projects.

    The course will be taught by Prof. Sebastian Stier.

    Course requirements & assessment

    Regular small assignments (programming homework, developing research questions and your own project); compulsory attendance; participating in discussions. Written term paper based on an analysis in R graded, (max. 5000 words), deadline: July 31, 2024

    Schedule
    Seminar
    14.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B Link
    28.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    13.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    27.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    03.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    17.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    22.05.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    MET: Generative AI in the Social Sciences
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Course Content

    Selected topics relating to Generative AI in the social sciences are introduced in this seminar. Assigned readings and in-class activities will impart a deeper insight into the current status of research in this field, which is used to determine open questions and perspectives for further research.

    Course taught by Prof. Joe Sakshaug.

    Course requirements & assessment

    For the examination, students write a term paper (5,000 words max.) where they either
    1) carry out an empirical study in a focus area of social science research using Generative AI methods, OR
    2) conduct a critical literature review of Generative AI used in the social sciences.

    Competences acquired

    Upon completion of the module, students are able to:
    • present their basic knowledge in Generative AI applied to social science research fields
    • name the latest Generative AI developments in social science research
    • describe their in-depth knowledge of empirical approaches to Generative AI in the social science research fields covered
    • critically evaluate the empirical literature and applications of Generative AI in the social science research fields covered

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 08:30 – 10:00 Online
    MET: Introduction to Comparative Survey Research
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of comparative survey research. These surveys enable researchers to conduct cross-national/cross-cultural analysis, sometimes even across time. Yet, these important surveys come with their own challenge. The course covers the major steps of implementing and conducting a comparative survey and design decisions at each step. A special focus of the course will be on discussing sources of error that may be introduced by survey design decisions. For illustration purposes and exercise, the course will draw on examples and case studies from well-known cross-national surveys such as the European Social Survey (ESS), the European Values Study (EVS), the Generations and Gender Survey (GGS), the Programme for the International Assessment of Adult Competencies (PIAAC), and the World Values Survey (WVS).

    Course requirements & assessment

    Active participation, homework assignments/oral presentations, term paper (graded)

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Prerequisites

    Some basic knowledge of statistical inference and R is required

    Course Content

    Lecture

    The course provides a broad overview of methods used in longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-section data, using measurements of the same individuals taken repeatedly through time can lead to better causal inferences in some cases, and can also give the possibility to learn more about the dynamics of individual behavior. The first objective of this course is to discuss the advantages of panel data, and the characteristics of the structure of panel data. Then, the course will give an overview of the main models (pooled OLS, fixed effects, random effects, first-differences) and provide the tools to choose betwen these models. The course will also discuss panel generalized linear models. Finally, an overview of event history analysis will be presented.

    Tutorial

    Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

    Tutorial -

    • Homework: students must complete and submit the eight homework and pass at least six.
    •  Assignments: To be completed during the session and submitted by the end of the session.
    Schedule
    Lecture
    12.02.24 – 27.05.24 Monday 10:15 – 11:45 B 143 in A5, 6 entrance B Link
    Tutorial
    13.02.24 – 28.05.24 Tuesday 15:30 – 17:00 B 143 in A5, 6 entrance B 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
    not on 25 March 12.02.24 – 27.05.24 Monday 10:15 – 11:45 A 103 in B6, 23–25 Link
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue

    Please register for the SMiP course program via their online registration tool by 15 February.

    MET: Workshop IRT Modeling – Theory and Applications in R
    4 ECTS
    Lecturer(s)

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

    This workshop provides an introduction to Item Response Theory (IRT) with basic and advanced models for dichotomous and polytomous items. The topics include the Rasch model and extensions with two, three and four item parameters for dichotomous items. Concerning polytomous items, we discuss the partial credit and rating scale model, generalized partial-credit model and graded response model for items with ordinal response format, and the nominal response model for items with categorical response format. In addition, multidimensional IRT models for response styles and IRTree models for multiple response processes are presented.
    The IRT models are outlined with their formal model equations, theoretical assumptions and implications, estimation techniques, and statistical testing procedures. Applications to simulated and real data sets illustrate the use of IRT models for the analysis of individual differences in basic and applied research.
    The workshop includes practical exercises of IRT modeling and analysis with current R packages. Basic knowledge and experience in R, including data management and use of R packages, are required for participation in this workshop.
    The language of instruction is English. The course program includes online meetings, videos and analysis projects as homework.

    Literature

    • Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
    • Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
    • De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
    • Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
    • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
    • Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
    • Course Credit/Exam: Presentation of IRT analysis
    Schedule
    Seminar
    16.02.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32 Link
    15.03.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    26.04.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    24.05.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET 931
    Credits: 5
    Prerequisites

    The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.

    Recommended: Knowledge of basic statistics and prior experience with R or Stata is helpful, but not necessary.

    Course Content

    This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

    In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

    Learning outcomes:

    The specific applications cover a broad set of skills with a focus on design of questionnaires and survey experiments, data analysis and quantitative methods, classification, inference, writing of own reports, and opportunities for own research.

    • Analytical Skills/Problem-Solving:
    Schedule
    Lecture
    Lecture 13.02.24 – 28.05.24 Tuesday 10:15 – 11:45 room O 048
    POL: Advanced Topics in Comparative Politics – Discovering Causality: Natural Experiments in Political Science
    8 ECTS
    Course Type: elective course
    Course Number: POL
    Credits: 8
    Course Content

    Natural experiments have become increasingly popular in political science research. By leveraging the “as-if” random assignment to units to real-word interventions or treatments, such methods allow scholars to draw causal inferences from observational data. Yet finding good natural experiments is as much of an art as a science. To that end, this course aims to help students recognize and discover opportunities for natural experimental research by surveying a broad range of empirical applications. Students should leave the course with both (i) a repertoire of potential natural experimental designs and (ii) the skills to evaluate the promises and pitfalls of such research.

    The course will be taught by Nan Zhang, PhD

    Course requirements & assessment

    Term paper (graded)

    Schedule
    Seminar
    14.02.24 – 29.05.24 Wednesday 10:15 – 11:45 tbc Link
    POL: Advanced Topics in Comparative Politics: Travelling through time and space: Conceptual and measurement issues in comparative political behavior
    10 ECTS
    Lecturer(s)

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

    Comparison is essential to research in comparative political behavior. At the same time, it raises multiple conceptual and measurement issues. Questions include what is ‘similar’, what is ‘different’, and what is incommensurable, and how can we figure out whether measures are equivalent or not. In this seminar, we will address conceptual and methodological issues in measurement and comparison in comparative political research.

    Course requirements & assessment

    Students will review empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets., term paper (graded)

    Schedule
    Seminar
    13.02.24 – 28.05.24 Tuesday 12:00 – 13:30 B 318 in A5, 6 entrance B Link
    POL: Advanced Topics in International Politics: Affective Polarization of Democracy between Populism and Technocracism
    8 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: POL
    Credits: 8
    Course Content

    In this course students are required to prepare the scholarly on different models of governance and to do empirical analyses for studying affective polarization of democracy and its challenges by technocracism and populism. Ideally, students know about the state of the art and can command statistical models using R. We will also distinguish between associational and causal research designs, assumptions on the data generation process, theoretical and statistical identification.

    Literature

    • Caramani, Daniele. 2017. Will vs. Reason: The Populist and Technocratic Forms of Political Representation and Their Critique to Party Government. American Political Science Review 111: 54 – 67.
    • Hahm, Hyeonho, David Hilpert and Thomas König. 2023. Divided We Unite: The Nature of Partyism and the Role of Coalition Partnership in Europe. American Political Science Review, First View.
    • Mudde, Cas. and Rovira Kaltwasser. 2013. Exclusionary vs. Inclusionary Populism: Comparing Contemporary Europe and Latin America. Government and Opposition 48(2): 147–174.

    Course requirements and assessment

    Preparation of sessions, presentation of 2 studies, active and regular participation is recommended, term paper (12–15 pages, graded)

    Schedule
    Seminar
    15.02.24 – 23.05.24 Thursday 12:00 – 13:30 B318 in A5, 6 entrance B Link
    POL: Comparative Political Behavior
    6 ECTS
    Lecturer(s)

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

    The main goal of this lecture is to present an advanced introduction to theoretical approaches, key concepts, and substantive issues in comparative political behavior. Building on a multi-level perspective, it will provide an overview of key concepts and theories in the analysis of micro-level processes of political behavior that are embedded in and feed into macro-level processes. Capitalizing on this analytical perspective, the lecture will also address major changes in the relationship between societal and political processes and institutions.

    Course requirements & assessment

    Regular participation is recommended, mandatory reading, term paper (graded)

    Schedule
    Lecture
    12.02.24 – 27.05.24 Monday 10:15 – 11:45 B 244 in A5, 6 entrance B Link
    POL: International Politics
    6 ECTS
    Lecturer(s)

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

    The security of individuals and states depends profoundly on international politics. Beyond the realm of security, structures and actors of “global governance” have been proliferating for many years. They influence crucial public policies in diverse ways. This lecture surveys academic debates on key topics of international politics, including: the sources of war, peace, and terrorism, the emergence and operation of international organizations and transnational civil society, and the making of key international policy outcomes including respect for human rights and climate policies.

    Course requirements & assessment

    Written exam (graded)

    Schedule
    Lecture
    12.02.24 – 27.05.24 Monday 13:45 – 15:15 B 244 in A5, 6 entrance B Link
    POL: Political Institutions and the Political Process
    6 ECTS
    Course Type: elective course
    Course Number: POL
    Credits: 6
    Course Content

    This lecture gives an overview of selected theoretical concepts and the main research findings in the field of Comparative Government, specifically focusing on the role of political institutions and their impact for political decision-making at all stages in the political process. The course introduces a number of core themes in the comparative study of political institutions, such as electoral institutions and their effects on turnout, voting behaviour and party strategies. In addition, the lecture focuses on the impact of different institutional designs on patterns of party competition, government formation and coalition governance. In a third step, we discuss the effects of political institutions and of personal characteristics of legislators on various aspects of decision-making within parliaments and governments.

    The course will be taught by Or Tuttnauer, PhD

    Course requirements & assessment

    Lecture of recommended texts, written exam (graded)

    Schedule
    Lecture
    13.02.24 – 28.05.24 Tuesday 10:15 – 11:45 C217 in A5, 6 entrance C Link
    POL: Selected Topics in International Politics: Causal Inference in International Political Economy
    10 ECTS
    Lecturer(s)

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

    In this seminar, we study the political economy of international organizations (IOs) with quantitative methods for causal inference. Our focus will be on the major IOs such as the United Nations (UN), the World Bank, the International Monetary Fund (IMF), and the European Union (EU). We will examine and discuss research on how these organizations operate, how they make decisions, and how they affect political and economic outcomes in their member states. Methodologically, the course will place an emphasis on literature that applies quantitative methods of causal inference and students will learn how these methods are applied in research practice. We will also discuss the political implications of the empirical findings for the future of global economic governance.In this seminar, we study the political economy of international organizations (IOs) with quantitative methods for causal inference. Our focus will be on the major IOs such as the United Nations (UN), the World Bank, the International Monetary Fund (IMF), and the European Union (EU). We will examine and discuss research on how these organizations operate, how they make decisions, and how they affect political and economic outcomes in their member states. Methodologically, the course will place an emphasis on literature that applies quantitative methods of causal inference and students will learn how these methods are applied in research practice. We will also discuss the political implications of the empirical findings for the future of global economic governance.In this seminar, we study the political economy of international organizations (IOs) with quantitative methods for causal inference. Our focus will be on the major IOs such as the United Nations (UN), the World Bank, the International Monetary Fund (IMF), and the European Union (EU). We will examine and discuss research on how these organizations operate, how they make decisions, and how they affect political and economic outcomes in their member states. Methodologically, the course will place an emphasis on literature that applies quantitative methods of causal inference and students will learn how these methods are applied in research practice. We will also discuss the political implications of the empirical findings for the future of global economic governance.

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Seminar
    13.02.24 – 28.05.24 Tuesday 13:45 – 15:15 B 317 in A5, 6 entrance B Link
    RES (bridge course): Mental health during dissertations: “Research is Me-Search” (GESS doctoral students only)
    5 ECTS
    Course Type: elective course
    Course Number: RES (bridge course)
    Credits: 5
    Course Content

    Almost every doctoral dissertation is marked by difficult periods and times of frustration, which can also affect one´s mental health.

    Not only aspects directly related to one's dissertation but also structural and/or personal aspects can make it challenging to maintain one's mental health.

    The aim of this course is to get to know and discuss typical risk factors and challenging constellations doctoral students are likely to face during their dissertations. The course will consist of literature-informed/guided group discussions of several predefined topics addressing common difficulties during dissertation projects. During the first session(s), the group will decide the particular topics of interest for each of the sessions based on a brief literature discussion and their personal interests. Then, based on selected literature provided by the lecturer, the students will discuss these topics both from an academic standpoint and from their individual perspective/experience during their dissertation project. Each session will thus serve as information input and offer room for discussion and exchange. The aim of this format is to foster student's knowledge about mental health during dissertation projects and open up possibilities to reflect one´s own situation and standpoints in a group of peers.

    The course will be taught by Dr. Matthias Volz

    Course  requirements & assessment

    Students need to be willing to read articles, and discuss and articulate their own views on typical challenging situations during dissertation projects in guided group discussions.

    Schedule
    Seminar
    bi-weekly 20.02.24 – 19.03.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    bi-weekly 09.04.24 – 21.05.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    RES (Bridge Course): New Perspectives on Economics and Politics
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Prerequisites

    Prerequisites: For each session, students need to have read the respective book in advance. (Detailed schedule will be provided in an introductory session.)

    Form of Assessment: Essay 50 %, Class Participation 50 %


    Friedman, M. (1963). Capitalism and Freedom. University of Chicago Press.
    Deaton, A. (2023). Economics in America: An Immigrant Economist Explores the Land of Inequality. Princeton University Press.
    Sandel, M. J. (2020). The Tyranny of Merit: What’s Become of the Common Good?. Penguin UK.
    Bowles, S. (2016). The moral economy. Yale University Press.
    Formal: Students need to be enrolled a PhD program at the GESS at the University of Mannheim, or the Master of Political Science.
    Required: Willingness to read, discuss, challenge, engage and think for yourself is critical for this course.

    Course Content

    We live in interesting times both, economically and politically. Many observers point to crises and uncertain developments in the economic and political world. Making sense of the nature of these challenges and pointing toward economic and political solutions for the future requires new perspectives. This is a course about the big and bold questions in economics and politics. How can or should economics and politics be organized to best serve society? What does it mean to put humans as they really are at the center of economic and political thinking? What role do morals and values, or dignity and respect, play for the way economics and politics work? What are the implications of digitalization for capitalism and freedom?

    We will try to come to grips with these questions by reading and discussing four key books on various new perspectives at the intersection between economics and politics. The aim of this course is to go as deep as we can and to get as much out of an in-class discussion of the material as possible. Willingness to acquire and read the books is a must. If you are unsure about whether or not you would want to take on the commitment of reading four books in one semester then this course is probably not the right one for you.

    Students need to be willing to read books, form their own opinions on them, and elaborate on and defend their own views in group discussions and a final essay.

    We will meet for an introductory session on Monday 4.3. at 13.45–15.15 and then meet again for a total of seven Thursday and Wednesday sessions after the Easter break: 11.4., 25.4., 2.5., 8.5., 16.5., 23.5., and 29.5. at 8.30–11.45.

    Competences acquired

    Learning outcomes: The aim of this course is to engage in intellectual dialogue, to develop a personal point of view on some of the central economic and political questions we face today, and to allow ourselves to think creatively, freely, and out of the box. After completing this course, students will have read important texts on new perspectives in economics and politics, they will have trained their ability to distill an own point of view from the writings of leading scientists, they will train their writing and discussion skills, and they will train to creatively apply what they have read in writing about the future of economics and politics in our society.

    Schedule
    Lecture
    Introduction to the Course 04.03.24 – 04.03.24 Monday 13:45 – 15:15 L9, 1–2, Room 409
    Lecture 11.04.24 – 11.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 25.04.24 – 25.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 02.05.24 – 02.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 08.05.24 – 08.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 16.05.24 – 16.05.24 Thursday 08:30 – 11:45 B6, 23–25, Room A301
    Lecture 23.05.24 – 23.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 29.05.24 – 29.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409
  • Psychology

    DIS: Dissertation Proposal Workshop
    2+8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: DIS
    Credits: 2+8
    Prerequisites

    Crafting Social Science Research, Literature Review

    Course Content

    The goal of this course is to provide support and crucial feedback on writing students' 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)?

    Nota bene: Further meeting dates will be determined during the first session. Only the first meeting will be held online, the remainder of the semester you'll meet in person.

    Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.

    Schedule
    Workshop
    further dates tbd 13.02.24 Tuesday 10:15 – 11:45 Zoom Link
    MET: Theory Building and Causal Inference
    6 ECTS
    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.

    The course will be taught by Prof. Marc Ratkovic, PhD

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    13.02.24 – 28.05.24 Tuesday 08:30 – 10:00 211 in B6, 30–32
    RES: AC3/BC4: Colloquia II
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Prerequisites

    TCBI, CSSR, Dissertation Proposal

    Course Content

    Please check with individual chairs in the Psychology department for dates and times of research colloquia.

    RES: CDSS Workshop: Research in Psychology
    2 ECTS
    Lecturer(s)

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

    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.

    Research in Psychology: Research projects 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.

    Literature: References will be given during the course.

    The workshop will be hosted by Prof. Arndt Bröder & Dr. Meike Kroneisen

    Talk schedule

    Competences acquired

    Improvement in research skills and communication of research results.

    Schedule
    Workshop
    12.02.24 – 27.05.24 Monday 15:30 – 17:00 C 217 in A5, 6 entrance C
    RES: English Academic Writing
    3 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 3
    Prerequisites

    CSSR, Literature Review

    Course Content

    The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.

    Course requirements & assessment

    Term paper

    Schedule
    Workshop
    15.02.24 – 23.05.24 Thursday 12:00 – 13:30 B 317 in A5, 6 entrance B Link
    CDSS Job Talk Series: Next GESS
    0 ECTS
    Course Type: elective course
    Course Number: CDSS Job Talk Series
    Course Content

    Johannes Lattmann is the recipient of the 2023 CDSS Young Scholar Award, which he received for his project 'Next GESS – Career Talks for PhD and Master students with Industry, NGOs, and Policy Institutions'.

    For this upcoming semester he organized the “Next GESS” Job Talk Series for doctoral students. Every Thursday, from 16.30 – 17.30 he invites a speaker to talk Online via Zoom about their job and career. The precise schedule can be found in the Syllabus which you can find in the appendix.

    In the context of this series, he has invited speakers from a variety of companies and institutions including IBM, CEPS, OECD, UNESCO and many more. Through their experiences, this series aims to provide insights into exciting career trajectories. Eventually, this series should inform you about career opportunities and provide valuable insights about application processes and the day-to-day work life in different institutions. 

    Each session contains a 15–20 minutes long presentation by the speaker, introducing the respective job or institution. This is followed by a Q&A session in which you can ask questions. 

    Talk schedule

    MET: 13th GESIS Summer School in Survey Methodology & GESIS Seminars
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS doctoral students have privileged access to the GESIS Summer School in Survey Methodology as well as GESIS workshops are exempt from course fees*.

    Contact the Center Manager before registering for any of the courses and only thereafter register directly through the GESIS web page making sure to mention that you are a CDSS doctoral student.

    The GESIS summer school takes place in Cologne from 24 July to 16 August. Detailed information about the summer school program is available on the GESIS website.

    *According to the provisions stated in §3 (5) of the GESIS CDSS cooperative treaty.

    MET: Advanced Quantitative Methods
    6+2 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6+2
    Prerequisites

    Knowledge of Multivariate Analysis

    Course Content

    The goal of this course is to provide an introduction into maximum-likelihood estimation.

    Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is mandatory for the assignments which complement the lecture (6 ECTS).

    Literature

    • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
    • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
    • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

    Course requirements & assessment

    Homework assignements, research paper (all graded)

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science.

    Schedule
    Lecture
    14.02.24 – 29.05.24 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    15.02.24 – 23.05.24 Thursday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Computational Social Science Methods and Digital Behavioral Data
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Please bring your own laptops for use in the course. At least basic knowledge of R is required.

    Course Content

    Computational Social Science is a young research field at the intersection of various social science disciplines, data science and computer science. The goal is to gain new insights into society through large amounts of data and the direct observation of human behavior. CSS relies on two cornerstones: digital behavioral data, which can be collected from online platforms or sensors like smartphones, and computer science methods such as automated text analysis to create appropriate measures for social science research questions. In the course, students will get to know foundational studies, theories and methods used in the field of CSS. We will discuss infrastructural, ethical and legal challenges and how to navigate these to devise appropriate research designs in CSS.
    The course will be application oriented. Students will familiarize themselves with the main applications of CSS methods and implement them in R. The range of applications will cover data management and preprocessing, the application of machine learning, data and results visualization, statistical data analysis and the validation of results. The hands-on application examples will cover questions from various research fields and different data types like social media data or web browsing histories. Equipped with this theoretical and methodological toolkit, students will develop their own CSS research projects.

    The course will be taught by Prof. Sebastian Stier.

    Course requirements & assessment

    Regular small assignments (programming homework, developing research questions and your own project); compulsory attendance; participating in discussions. Written term paper based on an analysis in R graded, (max. 5000 words), deadline: July 31, 2024

    Schedule
    Seminar
    14.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B Link
    28.02.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    13.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    27.03.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    03.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    17.04.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    22.05.24 Wednesday 08:30 – 11:45 B243 in A5, 6 entrance B
    MET: Generative AI in the Social Sciences
    6 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6
    Course Content

    Selected topics relating to Generative AI in the social sciences are introduced in this seminar. Assigned readings and in-class activities will impart a deeper insight into the current status of research in this field, which is used to determine open questions and perspectives for further research.

    Course taught by Prof. Joe Sakshaug.

    Course requirements & assessment

    For the examination, students write a term paper (5,000 words max.) where they either
    1) carry out an empirical study in a focus area of social science research using Generative AI methods, OR
    2) conduct a critical literature review of Generative AI used in the social sciences.

    Competences acquired

    Upon completion of the module, students are able to:
    • present their basic knowledge in Generative AI applied to social science research fields
    • name the latest Generative AI developments in social science research
    • describe their in-depth knowledge of empirical approaches to Generative AI in the social science research fields covered
    • critically evaluate the empirical literature and applications of Generative AI in the social science research fields covered

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 08:30 – 10:00 Online
    MET: Introduction to Comparative Survey Research
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of comparative survey research. These surveys enable researchers to conduct cross-national/cross-cultural analysis, sometimes even across time. Yet, these important surveys come with their own challenge. The course covers the major steps of implementing and conducting a comparative survey and design decisions at each step. A special focus of the course will be on discussing sources of error that may be introduced by survey design decisions. For illustration purposes and exercise, the course will draw on examples and case studies from well-known cross-national surveys such as the European Social Survey (ESS), the European Values Study (EVS), the Generations and Gender Survey (GGS), the Programme for the International Assessment of Adult Competencies (PIAAC), and the World Values Survey (WVS).

    Course requirements & assessment

    Active participation, homework assignments/oral presentations, term paper (graded)

    Schedule
    Seminar
    16.02.24 – 31.05.24 Friday 10:15 – 11:45 A 102 in B6, 23–25 Link
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Prerequisites

    Some basic knowledge of statistical inference and R is required

    Course Content

    Lecture

    The course provides a broad overview of methods used in longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-section data, using measurements of the same individuals taken repeatedly through time can lead to better causal inferences in some cases, and can also give the possibility to learn more about the dynamics of individual behavior. The first objective of this course is to discuss the advantages of panel data, and the characteristics of the structure of panel data. Then, the course will give an overview of the main models (pooled OLS, fixed effects, random effects, first-differences) and provide the tools to choose betwen these models. The course will also discuss panel generalized linear models. Finally, an overview of event history analysis will be presented.

    Tutorial

    Using R, we apply methods of longitudinal data analysis (presented in the lecture “Longitudinal Data Analysis”) to real survey data.

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Lecture – Three quizzes (two must be a pass), regular attendance, written examination (graded, closed-book)

    Tutorial -

    • Homework: students must complete and submit the eight homework and pass at least six.
    •  Assignments: To be completed during the session and submitted by the end of the session.
    Schedule
    Lecture
    12.02.24 – 27.05.24 Monday 10:15 – 11:45 B 143 in A5, 6 entrance B Link
    Tutorial
    13.02.24 – 28.05.24 Tuesday 15:30 – 17:00 B 143 in A5, 6 entrance B 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
    not on 25 March 12.02.24 – 27.05.24 Monday 10:15 – 11:45 A 103 in B6, 23–25 Link
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue

    Please register for the SMiP course program via their online registration tool by 15 February.

    MET: Workshop IRT Modeling – Theory and Applications in R
    4 ECTS
    Lecturer(s)

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

    This workshop provides an introduction to Item Response Theory (IRT) with basic and advanced models for dichotomous and polytomous items. The topics include the Rasch model and extensions with two, three and four item parameters for dichotomous items. Concerning polytomous items, we discuss the partial credit and rating scale model, generalized partial-credit model and graded response model for items with ordinal response format, and the nominal response model for items with categorical response format. In addition, multidimensional IRT models for response styles and IRTree models for multiple response processes are presented.
    The IRT models are outlined with their formal model equations, theoretical assumptions and implications, estimation techniques, and statistical testing procedures. Applications to simulated and real data sets illustrate the use of IRT models for the analysis of individual differences in basic and applied research.
    The workshop includes practical exercises of IRT modeling and analysis with current R packages. Basic knowledge and experience in R, including data management and use of R packages, are required for participation in this workshop.
    The language of instruction is English. The course program includes online meetings, videos and analysis projects as homework.

    Literature

    • Böckenholt, U., & Meiser, T. (2017). Response style analysis with threshold and multi-process IRT models: A review and tutorial. British Journal of Mathematical and Statistical Psychology, 70, 159–181.
    • Debelak, R., Strobl, C., & Zeigenfuse, M. (2022). An introduction to the Rasch model with Examples in R. Boca Raton, FL: CRC Press.
    • De Boeck, P., & Wilson, M. (2004). Explanatory item response models. New York: Springer.
    • Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.
    • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum.
    • Rizopoulos, D. (2006). ltm: An R package for latent variable modeling and item response analysis. Journal of Statistical Software, 17(5), 1–25
    • Course Credit/Exam: Presentation of IRT analysis
    Schedule
    Seminar
    16.02.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32 Link
    15.03.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    26.04.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    24.05.24 Friday 10:15 – 11:45 108 CIP Pool in B6, 30–32
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET 931
    Credits: 5
    Prerequisites

    The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.

    Recommended: Knowledge of basic statistics and prior experience with R or Stata is helpful, but not necessary.

    Course Content

    This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

    In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

    Learning outcomes:

    The specific applications cover a broad set of skills with a focus on design of questionnaires and survey experiments, data analysis and quantitative methods, classification, inference, writing of own reports, and opportunities for own research.

    • Analytical Skills/Problem-Solving:
    Schedule
    Lecture
    Lecture 13.02.24 – 28.05.24 Tuesday 10:15 – 11:45 room O 048
    PSY: Comparative Perspectives on Social Learning
    4 ECTS
    Course Type: elective course
    Course Number: PSY
    Credits: 4
    Course Content

    While culture is often seen as a hallmark of humanity, an evolutionary perspective challenges this view by proposing continuity in behavioral traits across species. According to this view, culture and its constituting elements exhibit variations in degree rather than kind.
    Overall, the goal of this course is to understand how and why individuals (humans and nonhumans) learn in a social context, an essential component of culture. By adopting a (cross-species) comparative perspective, the course covers the mechanisms and functions of social learning, as well as its driving factors, including psychological aspects (cognitive and non-cognitive), and environmental determinants (physical and social), providing a deeper understanding of the evolutionary links that underlie cultural behaviors.

    The course will be taught by Camilla Cenni, PhD

    Course requirements & assessment

    Active participation, homework, in-class presentation, term paper (graded)

    Schedule
    Seminar
    13.02.24 – 28.05.24 Tuesday 10:15 – 11:45 B 143 in A5, 6 entrance B Link
    PSY: Research in Clinical Psychology
    4 ECTS
    Lecturer(s)

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

    We invite CDSS doctoral 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 discussion.

    Schedule
    Seminar
    13.02.24 – 28.05.24 Tuesday 12:00 – 13:30 016–017 in L13, 15–17
    RES (bridge course): Mental health during dissertations: “Research is Me-Search” (GESS doctoral students only)
    5 ECTS
    Course Type: elective course
    Course Number: RES (bridge course)
    Credits: 5
    Course Content

    Almost every doctoral dissertation is marked by difficult periods and times of frustration, which can also affect one´s mental health.

    Not only aspects directly related to one's dissertation but also structural and/or personal aspects can make it challenging to maintain one's mental health.

    The aim of this course is to get to know and discuss typical risk factors and challenging constellations doctoral students are likely to face during their dissertations. The course will consist of literature-informed/guided group discussions of several predefined topics addressing common difficulties during dissertation projects. During the first session(s), the group will decide the particular topics of interest for each of the sessions based on a brief literature discussion and their personal interests. Then, based on selected literature provided by the lecturer, the students will discuss these topics both from an academic standpoint and from their individual perspective/experience during their dissertation project. Each session will thus serve as information input and offer room for discussion and exchange. The aim of this format is to foster student's knowledge about mental health during dissertation projects and open up possibilities to reflect one´s own situation and standpoints in a group of peers.

    The course will be taught by Dr. Matthias Volz

    Course  requirements & assessment

    Students need to be willing to read articles, and discuss and articulate their own views on typical challenging situations during dissertation projects in guided group discussions.

    Schedule
    Seminar
    bi-weekly 20.02.24 – 19.03.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    bi-weekly 09.04.24 – 21.05.24 Tuesday 10:15 – 11:45 209 in B6, 30–32
    RES (Bridge Course): New Perspectives on Economics and Politics
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES (Bridge Course)
    Credits: 5
    Prerequisites

    Prerequisites: For each session, students need to have read the respective book in advance. (Detailed schedule will be provided in an introductory session.)

    Form of Assessment: Essay 50 %, Class Participation 50 %


    Friedman, M. (1963). Capitalism and Freedom. University of Chicago Press.
    Deaton, A. (2023). Economics in America: An Immigrant Economist Explores the Land of Inequality. Princeton University Press.
    Sandel, M. J. (2020). The Tyranny of Merit: What’s Become of the Common Good?. Penguin UK.
    Bowles, S. (2016). The moral economy. Yale University Press.
    Formal: Students need to be enrolled a PhD program at the GESS at the University of Mannheim, or the Master of Political Science.
    Required: Willingness to read, discuss, challenge, engage and think for yourself is critical for this course.

    Course Content

    We live in interesting times both, economically and politically. Many observers point to crises and uncertain developments in the economic and political world. Making sense of the nature of these challenges and pointing toward economic and political solutions for the future requires new perspectives. This is a course about the big and bold questions in economics and politics. How can or should economics and politics be organized to best serve society? What does it mean to put humans as they really are at the center of economic and political thinking? What role do morals and values, or dignity and respect, play for the way economics and politics work? What are the implications of digitalization for capitalism and freedom?

    We will try to come to grips with these questions by reading and discussing four key books on various new perspectives at the intersection between economics and politics. The aim of this course is to go as deep as we can and to get as much out of an in-class discussion of the material as possible. Willingness to acquire and read the books is a must. If you are unsure about whether or not you would want to take on the commitment of reading four books in one semester then this course is probably not the right one for you.

    Students need to be willing to read books, form their own opinions on them, and elaborate on and defend their own views in group discussions and a final essay.

    We will meet for an introductory session on Monday 4.3. at 13.45–15.15 and then meet again for a total of seven Thursday and Wednesday sessions after the Easter break: 11.4., 25.4., 2.5., 8.5., 16.5., 23.5., and 29.5. at 8.30–11.45.

    Competences acquired

    Learning outcomes: The aim of this course is to engage in intellectual dialogue, to develop a personal point of view on some of the central economic and political questions we face today, and to allow ourselves to think creatively, freely, and out of the box. After completing this course, students will have read important texts on new perspectives in economics and politics, they will have trained their ability to distill an own point of view from the writings of leading scientists, they will train their writing and discussion skills, and they will train to creatively apply what they have read in writing about the future of economics and politics in our society.

    Schedule
    Lecture
    Introduction to the Course 04.03.24 – 04.03.24 Monday 13:45 – 15:15 L9, 1–2, Room 409
    Lecture 11.04.24 – 11.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 25.04.24 – 25.04.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 02.05.24 – 02.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 08.05.24 – 08.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 16.05.24 – 16.05.24 Thursday 08:30 – 11:45 B6, 23–25, Room A301
    Lecture 23.05.24 – 23.05.24 Thursday 08:30 – 11:45 L9, 1–2, Room 409
    Lecture 29.05.24 – 29.05.24 Wednesday 08:30 – 11:45 L9, 1–2, Room 409