Spring 2020

  • Sociology

    Dissertation Tutorial: Sociology
    0 ECTS
    Lecturer(s)

    Course Type: core course
    Course Content

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

    Schedule
    Tutorial
    Keusch 10.02.20 – 25.05.20 Monday 15:30 – 17:00 tbd
    Möhring 11.02.20 – 26.05.20 Tuesday 10:15 – 11:45 tbd
    Kogan 11.02.20 – 26.05.20 Tuesday 13:45 – 15:15 tbd
    Hillmann 12.02.20 – 27.05.20 Wednesday 17:15 – 18:45 tbd
    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.

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

    Schedule
    Workshop
    1st meeting, further dates tbd 11.02.20 Tuesday 394066:15 – 11:45 211 in B6, 30–32
    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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    RES: CDSS Workshop: Sociology
    2 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 2 or 3 depending on applicable study regulations
    Course Content

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

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

    Schedule
    Workshop
    29.04.20 Wednesday 16:00 – 18:00 211 in B6, 30–32
    08.05.20 Friday 10:00 – 11:00 212, in B6 30–32
    20.05.20 Wednesday 16:00 – 18:00 211 in B6, 30–32
    22.05.20 Friday 10:00 – 11:00 212 in B6, 30–32
    27.05.20 Wednesday 16:00 – 18:00 211 in B6, 30–32
    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.

    Schedule
    Workshop
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C 112 in A 5, 6 entrance C
    RES: MZES A Colloquium “European Societies and their Integration”
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    Please refer to the MZES webpages for dates and times.

    MET: 1-Click Reproducibility: Online Teaching Materials for Data Science with Python
    0 ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    The goal of this interactive lecture is to provide an overview of online self-teaching materials for Data Science with Python. Data Science is the interdisciplinary science of the extraction of useful knowledge from heterogeneous and potentially large datasets and Jupyter Notebooks are the goto environment for this kind of analysis. Participants will obtain a hands-on introduction to the Jupyter ecosystem as well as online services for working with Jupyter Notebooks such as mybinder.org and GESIS Notebooks.

    Please note that participants should bring their own laptop for this course. All utilized software is browser-based and online available.
    Lecturer: Dr. Arnim Bleier is a postdoctoral researcher in the Department of Computational Social Science at GESIS. His research interests are in the field of Natural Language Processing and Computational Social Science. In collaboration with social scientists, he develops Bayesian models for the content, structure and dynamics of social phenomena.

    Registration deadline: 31 March

    Date:    07.04.2020, 13.45 -- 15:15
    (link to virtual room will be sent to participants)

    MET: 9th GESIS Summer School in Survey Methodology
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology and are exempt from course fees*. Please register directly through the GESIS web page and make sure to mention that you are a CDSS doctoral student.

    The courses take place in Cologne and take place from 6 to 28 August 2020. 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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    MET: Big Data in Immigration Research – registration must be made via the lecturer and the CDSS
    6 ECTS
    Lecturer(s)

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

    Students should have some interest and experience in one of the following areas: (1) working with large, unstructured data sets, (2) immigration research, and (3) project management. Students do not need to have extensive experience in all three areas. However, students are expected to have taken at least one statistics course and have basic familiarity with a software program that can be used for statistical analysis (e.g., R, Python, SAS, Stata). Although we will not teach students how to use statistical software, students who want instruction in this area can access DataCamp learning modules that will be made available for the course.

    Course Content

    The growing complexity of human mobility and the integration of immigrants into host societies has created an increased need for reliable and timely data to inform policy development and humanitarian assistance. Data from traditional sources (e.g., national population censuses, sample surveys, and administrative sources) on migration and immigration are limited in quantity and quality, and new alternatives have recently emerged. Some of these new types of “Big Data” are particularly promising for the study of migration-related phenomena. These include mobile phone call logs, Internet activity (e.g., Google searches, tracking of online media content use), geo-referenced social media activity, and other passively collected (mobile) data. In this course, students from the two partnering universities will form international groups to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions.

    Lectures

    Class lectures will be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. 
    Group project
    Students from the two partnering universities will form international teams to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions. Students will virtually attend the same class/lecture and then collaborate via online tools (Canvas and Zoom) on their projects.

    Literature

    Assigned readings for each lecture are indicated on the syllabus. They are available online or through the course website.

    Active participation, homework and presentations, final written term paper.

    Competences acquired

    In this course students will learn to:

    • obtain and analyze data from non-traditional sources;
    • formulate and answer research questions related to migration and immigration using such data;
    • work in teams to scope a problem, distribute work, and combine their results for a joint presentation; and
    • work as part of an international collaboration with teams formed across countries.
    Schedule
    Seminar
    No seminar on 17 Feb & 25 May 10.02.20 – 25.05.20 Monday 17:15 – 18:45 C 212 in A5, 6 entrance C
    MET: Experimental Methods in Sociology and the Social Sciences
    6 ECTS
    Lecturer(s)

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

    A growing interest in causal identification and mechanisms testing in the social sciences has provided a surge in empirical research using the experimental method. As a result, the experimental methodology has become a common tool not just of psychologists and behavioral economists but also of sociologists and political scientists enabling them to test (bounded) rational choice theories and to isolate and study the causes, dynamics and effects of social phenomena. For example, important sociological concepts such as trust and trustworthiness, cooperation in social dilemma and social norms have been examined via using survey, field, lab-in-the-field and laboratory experiments.
    The main objective of this seminar is to introduce students to the range of experimental methods, classical work as well as recent trends and best practices of experimental social science research. In addition, the seminar aims to teach students how to design and analyze an experiment aimed at answering a self-developed research question. Each student is expected to actively participate in classes by presenting and discussing papers selected by the instructor and to develop an experimental design of her/his own or in collaboration with one other student of the seminar.

    Course requirements & assessment

    Weekly response papers (Week 3 – 12) (30%)
    Write a review paper & guide a class discussion for one session (30%)
    In-class presentation of research design (10%)
    Research Design as final paper (30%)
    Active participation

    Schedule
    Seminar
    12.02.20 – 27.05.20 Wednesday 12:00 – 13:30 309 in B6, 30–32 Link
    MET: Fundamentals of Computing and Data Display
    6 ECTS
    Lecturer(s)

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

    Some basic experience with programming in R or Python is helpful, but not strictly necessary. Students without any R knowledge are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

    rstudio.com


    rstudio.cloud/learn/primers


    www.statmethods.net


    swirlstats.com

    Course Content

    Empirical social scientists are often confronted with a variety of data sources and formats that extend beyond structured and handleable survey data. With the emergence of Big Data, especially data from web sources play an increasingly important role in scientific research. However, the potential of new data sources comes with the need for comprehensive computational skills in order to deal with loads of potentially unstructured information. Against this background, the first part of this course provides an introduction to web scraping and APIs for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. Tools for reproducible research will be introduced to facilitate transparent and collaborative programming. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.

    Course requirements & assessment

    Participation
    Presentation
    Written term paper

    Schedule
    Seminar
    not on 19 March 13.02.20 – 07.05.20 Wednesday 15:30 – 18:00 C 212 in A5, 6 entrance C
    MET: Longitudinal Data Analysis (Lecture + Lab Course)
    6+3 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture 'Longitudinal Data Analysis'

    The course provides a broad overview over methods of longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-sectional data, panel data can allow to improve causal inference. The first objective of this course is to understand why and under which conditions this is the case. In the next step, we will discuss a variety of different modeling approaches to panel data (fixed effects, random effects, first difference) and learn how to decide between these models. The lecture also provides an overview over event history models. It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models are applied to real data sets.

    Tutorial “Data Sources in Social Sciences” taught by Andreas Weiland

    Using Stata, we apply methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) to real survey data. Attendance of the complementary lecture “Longitudinal Data Analysis” is highly recommended as firm knowledge of the lecture content is presumed. Some knowledge of Stata is helpful, but not required.
     

    6 ECTS will be awarded for successful completion of a 90 minute exam and an additional 3 ECTS can be awarded for participation in the lab course where active participation and short oral presentations are expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C -108 in A 5, 6 entrance C
    Lecture
    02.03.20 – 25.05.20 Monday 15:30 – 17:00 B 243 in A 5, 6 entrance B Link
    MET: Machine Learning
    6 ECTS
    Lecturer(s)

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

    Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course.

    Course Content

    This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses
    their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune, evaluate and interpret prediction models using R.

    Learning outcomes: At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods using R.

    Form of assessment: Presentation and / or term paper

    Schedule
    Lecture
    18.02.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    03.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    17.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    31.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    21.04.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    05.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    19.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    MET: Modelling Social Processes
    6 ECTS
    Lecturer(s)

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

    This course provides an introduction to formal models in the social sciences. It discusses a series of basic prototypes which have proved to be important tools for theory construction in various fields. Relating to the general model of sociological explanation (‘Coleman boat’), the focus is on processes of the steps of (non-trivial) aggregation and on dynamics over time. Topics covered are, for example, exchange, strategic action, collective action and the evolution of cooperation, mobilisation, diffusion, or segregation. While most of the models and examples chosen might already be fairly well known, this course puts specific emphasis on explaining the math behind them in more detail than usual and on practically ‘playing around’ with the models. Thus, it will provide some expertise and training in general formal skills, such as game theory, difference equations, differential equations, and agent-based simulation. The aim is to enable participants in principle to modify, extend or combine existing models according to their own research questions.

    Course requirements & assessment

    Participation, homework, presentations

    Written term paper (max. 5000 words)

    Schedule
    Seminar
    11.02.20 – 26.05.20 10:15 – 11:45 C 112 in A5, 6 entrance C
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP web page with full details

    SMiP courses open to CDSS doctoral students:

    Foundations 2: Modeling Intraindividual Variabilty and Change Instructors: Tanja Lischetzke & Sabine Sonnentag, Dates: 30.03. (10:00 – 17:00) and 31.03.2020 (09:00 – 16:00),  Location: Landau

    Foundations 2: Stochastic Models of Time-Dependent Cognitive Mechanisms Instructors: Andreas Voss & Rolf Ulrich, Dates: 28.05. (10:00 – 18:00) and 29.05.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Foundations 2: Multinomial-Processing-Tree (MPT) Modeling: Basic Methods and Recent Advances Instructors: Edgar Erdfelder &  Daniel Heck (University of Marburg), Dates: 25.06. (10:00 – 18:00) and 26.06.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Postponed to winter term 2020Academic Writing and Publishing Instructor: Benjamin Hilbig, Dates: 25.09. (10:00 – 18:00) and 26.09.2020 (09:00 – 17:00), Location: Mannheim

    Growth models and variants of growth models Instructor: Paul Bliese, Dates: 07.04. (10:00 – 18:00) and 08.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Python basics Instructors: Stefan Radev and Ulf Mertens, Dates: 27.04. (10:00 – 18:00) and 28.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Advanced / Modern R Instructor: David Izydorczyk, Dates: 12.05. (10:00 – 18:00) and 13.05.2020 (09:00 – 12:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    CXLD – IRT Modeling – Theory and Applications in R Instructor: Thorsten Meiser, Date: 13.05.2020 (13:00 – 17:00), Location: Mannheim (Building B6, 30–32, room 211) (NOTE: Online contents as prerequisites (workload about 4–5 hours) for attending the meeting in Mannheim!)

    Postponed to spring semester 2021 – Multilevel Structural Equation Modeling Instructor: Kristopher Preacher,

    Speed-accuracy mechanisms in continuous and discrete-state decision models Instructor: Jeffrey Starns, Dates: 26.05. (10:00 – 18:00) and 27.05.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants). The instructor will share material for self-study and preparation with the registered participants.

    CXLD – Bayesian hierarchical and factor analysis models Instructor: Jeffrey Rouder, Date: 17.06.2020 (10:00 – 18:00), Location: Mannheim

    Transparent Open Science Instructor: Jeffrey Rouder, Date: 24.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Introduction to Bayesian Modeling Instructor: Jeffrey Rouder, Dates: 29.06. (16:30 – 21:30) and 30.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Robust Modeling in Cognitive Science Instructor: Jeffrey Rouder, Dates: 06.07. (16:30 – 21:30) and 07.07.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Please register by sending an email to Annette Förster.

    Registration deadline: 29 February 2020

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 8
    Prerequisites

    Topics covered in introductory Game Theory class

    Course Content

    This course is a continuation of the intro into Game Theory and surveys key applications of game theory with a particular emphasis on the link of theories, methods and empirics. Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. Topics covered include electoral competition, delegation, political agency, governmental veto players, authoritarian politics, manipulation, war and crisis bargaining. While the focus is on understanding applied work, previous training in game theory is required. Students will build upon their previous game theory training to become informed consumers of scholarship utilizing the methodology and begin to learn how to apply game-theoretic logic to their own work. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

    Course requirements & assessment

    Class discussion, paper presentation, term paper

    Schedule
    Lecture
    12.02.20 – 27.05.20 Wednesday 10:15 – 11:45 B 317 in A5, 6 entrance B Link
    RES: Interdisciplinary Work in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES
    Credits: 5
    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS.

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by me, each participant will shortly (max 10 min, 3 slides per person) present the core idea of an interdisciplinary paper that involves her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. GESS Research Day

    The GESS research day – kindly co-organized by your student representatives – consists of presentations by PhD students from all three centers and discussion panels with senior experts. Participation at the GESS Research Day is mandatory for all course participants. You will give  presentation on a current working paper or research project of yours and you will  discuss a paper/presentation from one of your fellow students from another field (15–20 min presentation, 5 min discussion, 5 min Q&A). Additionally, you will participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

    3. Science Speed Dating

    The science speed dating event – also organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a students from another field.

    4. Project Presentations & Writeups

    The proposals will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in one week after the presentation.

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in one discussion panel at the GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a the write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline by sending title and abstract of the research project/topic you would like to present during the GESS research day to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first serve basis.


    Course Dates:

    March 11th, 2020, 10am-1pm, Kick-Off Meeting, Room 409 in L9, 1–2 (4. OG)        

    April 23th, 2020, Deadline to send paper to discussant (and in cc: to gess uni-mannheim.de)

    April 30th, 2020, Research Day (whole day symposium) – cancelled

    May 4th, 2020, time TBA – Science Speed Dating Event

    May 28th, 2020, Presentation of research proposal (half- to full day symposium)


    Course Room: B6, 30–32, room 211

    SOC: Careers and Mobility Within and Between Organizations
    6 ECTS
    Lecturer(s)

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

    This advanced seminar will explore recent social science research that seeks to explain variation in career opportunities within organizations and career mobility between organizations. We will consider a variety of research questions: what kinds of changes do we observe in career paths over time? How much of the change can be attributed to the variation in experience between different cohorts of workers? How much of the change in career patterns is due to organizational change within firms? How does matching between labor demand and supply work in different occupational settings? What are the underlying mechanisms that channel mobility within and between organizations? To what extent are skills transferable from one job to the next? How do new occupations and professions emerge and establish themselves? To address these and further questions, we will rely both on recent theoretical advances and on empirical studies in various settings.

    Schedule
    Seminar
    11.02.20 – 24.03.20 Tuesday 12:00 – 15:15 tbc
  • Political Science

    Dissertation Tutorial: Political Science
    0 ECTS
    Lecturer(s)

    Course Type: core course
    Course Content

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

    Please check with individual chairs for dates and times.

    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.

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

    Schedule
    Workshop
    1st meeting, further dates tbd 11.02.20 Tuesday 394066:15 – 11:45 211 in B6, 30–32
    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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    RES: CDSS Workshop: Political Science
    2 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 2 or 3 depending on applicable study regulations
    Course Content

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

    Other young researchers in the social sciences affiliated with the University of Mannheim (incl. MZES and SFB 884) are also invited to attend the talks.

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

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

    Schedule
    Workshop
    12.02.20 – 27.05.20 Wednesday 12:00 – 13:30 310 in B6, 30–32
    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.

    Schedule
    Workshop
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C 112 in A 5, 6 entrance C
    RES: MZES B Colloquium “European Political Systems and their Integration”
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    Please refer to the MZES webpages for dates and times.

    RES: SFB 884 Seminar Series
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Prerequisites

    CSSR, TBCI, Dissertation Proposal

    Course Content

    Attending the Seminar Series on the Political Economy of Reforms is a possible alternative to attending the MZES B colloquium. Please refer to the SFB 884 website for dates and times.

    MET: 1-Click Reproducibility: Online Teaching Materials for Data Science with Python
    0 ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    The goal of this interactive lecture is to provide an overview of online self-teaching materials for Data Science with Python. Data Science is the interdisciplinary science of the extraction of useful knowledge from heterogeneous and potentially large datasets and Jupyter Notebooks are the goto environment for this kind of analysis. Participants will obtain a hands-on introduction to the Jupyter ecosystem as well as online services for working with Jupyter Notebooks such as mybinder.org and GESIS Notebooks.

    Please note that participants should bring their own laptop for this course. All utilized software is browser-based and online available.
    Lecturer: Dr. Arnim Bleier is a postdoctoral researcher in the Department of Computational Social Science at GESIS. His research interests are in the field of Natural Language Processing and Computational Social Science. In collaboration with social scientists, he develops Bayesian models for the content, structure and dynamics of social phenomena.

    Registration deadline: 31 March

    Date:    07.04.2020, 13.45 -- 15:15
    (link to virtual room will be sent to participants)

    MET: 9th GESIS Summer School in Survey Methodology
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology and are exempt from course fees*. Please register directly through the GESIS web page and make sure to mention that you are a CDSS doctoral student.

    The courses take place in Cologne and take place from 6 to 28 August 2020. 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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    MET: Big Data in Immigration Research – registration must be made via the lecturer and the CDSS
    6 ECTS
    Lecturer(s)

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

    Students should have some interest and experience in one of the following areas: (1) working with large, unstructured data sets, (2) immigration research, and (3) project management. Students do not need to have extensive experience in all three areas. However, students are expected to have taken at least one statistics course and have basic familiarity with a software program that can be used for statistical analysis (e.g., R, Python, SAS, Stata). Although we will not teach students how to use statistical software, students who want instruction in this area can access DataCamp learning modules that will be made available for the course.

    Course Content

    The growing complexity of human mobility and the integration of immigrants into host societies has created an increased need for reliable and timely data to inform policy development and humanitarian assistance. Data from traditional sources (e.g., national population censuses, sample surveys, and administrative sources) on migration and immigration are limited in quantity and quality, and new alternatives have recently emerged. Some of these new types of “Big Data” are particularly promising for the study of migration-related phenomena. These include mobile phone call logs, Internet activity (e.g., Google searches, tracking of online media content use), geo-referenced social media activity, and other passively collected (mobile) data. In this course, students from the two partnering universities will form international groups to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions.

    Lectures

    Class lectures will be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. 
    Group project
    Students from the two partnering universities will form international teams to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions. Students will virtually attend the same class/lecture and then collaborate via online tools (Canvas and Zoom) on their projects.

    Literature

    Assigned readings for each lecture are indicated on the syllabus. They are available online or through the course website.

    Active participation, homework and presentations, final written term paper.

    Competences acquired

    In this course students will learn to:

    • obtain and analyze data from non-traditional sources;
    • formulate and answer research questions related to migration and immigration using such data;
    • work in teams to scope a problem, distribute work, and combine their results for a joint presentation; and
    • work as part of an international collaboration with teams formed across countries.
    Schedule
    Seminar
    No seminar on 17 Feb & 25 May 10.02.20 – 25.05.20 Monday 17:15 – 18:45 C 212 in A5, 6 entrance C
    MET: Experimental Methods in Sociology and the Social Sciences
    6 ECTS
    Lecturer(s)

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

    A growing interest in causal identification and mechanisms testing in the social sciences has provided a surge in empirical research using the experimental method. As a result, the experimental methodology has become a common tool not just of psychologists and behavioral economists but also of sociologists and political scientists enabling them to test (bounded) rational choice theories and to isolate and study the causes, dynamics and effects of social phenomena. For example, important sociological concepts such as trust and trustworthiness, cooperation in social dilemma and social norms have been examined via using survey, field, lab-in-the-field and laboratory experiments.
    The main objective of this seminar is to introduce students to the range of experimental methods, classical work as well as recent trends and best practices of experimental social science research. In addition, the seminar aims to teach students how to design and analyze an experiment aimed at answering a self-developed research question. Each student is expected to actively participate in classes by presenting and discussing papers selected by the instructor and to develop an experimental design of her/his own or in collaboration with one other student of the seminar.

    Course requirements & assessment

    Weekly response papers (Week 3 – 12) (30%)
    Write a review paper & guide a class discussion for one session (30%)
    In-class presentation of research design (10%)
    Research Design as final paper (30%)
    Active participation

    Schedule
    Seminar
    12.02.20 – 27.05.20 Wednesday 12:00 – 13:30 309 in B6, 30–32 Link
    MET: Fundamentals of Computing and Data Display
    6 ECTS
    Lecturer(s)

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

    Some basic experience with programming in R or Python is helpful, but not strictly necessary. Students without any R knowledge are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

    rstudio.com


    rstudio.cloud/learn/primers


    www.statmethods.net


    swirlstats.com

    Course Content

    Empirical social scientists are often confronted with a variety of data sources and formats that extend beyond structured and handleable survey data. With the emergence of Big Data, especially data from web sources play an increasingly important role in scientific research. However, the potential of new data sources comes with the need for comprehensive computational skills in order to deal with loads of potentially unstructured information. Against this background, the first part of this course provides an introduction to web scraping and APIs for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. Tools for reproducible research will be introduced to facilitate transparent and collaborative programming. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.

    Course requirements & assessment

    Participation
    Presentation
    Written term paper

    Schedule
    Seminar
    not on 19 March 13.02.20 – 07.05.20 Wednesday 15:30 – 18:00 C 212 in A5, 6 entrance C
    MET: Longitudinal Data Analysis (Lecture + Lab Course)
    6+3 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture 'Longitudinal Data Analysis'

    The course provides a broad overview over methods of longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-sectional data, panel data can allow to improve causal inference. The first objective of this course is to understand why and under which conditions this is the case. In the next step, we will discuss a variety of different modeling approaches to panel data (fixed effects, random effects, first difference) and learn how to decide between these models. The lecture also provides an overview over event history models. It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models are applied to real data sets.

    Tutorial “Data Sources in Social Sciences” taught by Andreas Weiland

    Using Stata, we apply methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) to real survey data. Attendance of the complementary lecture “Longitudinal Data Analysis” is highly recommended as firm knowledge of the lecture content is presumed. Some knowledge of Stata is helpful, but not required.
     

    6 ECTS will be awarded for successful completion of a 90 minute exam and an additional 3 ECTS can be awarded for participation in the lab course where active participation and short oral presentations are expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C -108 in A 5, 6 entrance C
    Lecture
    02.03.20 – 25.05.20 Monday 15:30 – 17:00 B 243 in A 5, 6 entrance B Link
    MET: Machine Learning
    6 ECTS
    Lecturer(s)

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

    Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course.

    Course Content

    This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses
    their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune, evaluate and interpret prediction models using R.

    Learning outcomes: At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods using R.

    Form of assessment: Presentation and / or term paper

    Schedule
    Lecture
    18.02.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    03.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    17.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    31.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    21.04.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    05.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    19.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    MET: Modelling Social Processes
    6 ECTS
    Lecturer(s)

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

    This course provides an introduction to formal models in the social sciences. It discusses a series of basic prototypes which have proved to be important tools for theory construction in various fields. Relating to the general model of sociological explanation (‘Coleman boat’), the focus is on processes of the steps of (non-trivial) aggregation and on dynamics over time. Topics covered are, for example, exchange, strategic action, collective action and the evolution of cooperation, mobilisation, diffusion, or segregation. While most of the models and examples chosen might already be fairly well known, this course puts specific emphasis on explaining the math behind them in more detail than usual and on practically ‘playing around’ with the models. Thus, it will provide some expertise and training in general formal skills, such as game theory, difference equations, differential equations, and agent-based simulation. The aim is to enable participants in principle to modify, extend or combine existing models according to their own research questions.

    Course requirements & assessment

    Participation, homework, presentations

    Written term paper (max. 5000 words)

    Schedule
    Seminar
    11.02.20 – 26.05.20 10:15 – 11:45 C 112 in A5, 6 entrance C
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP web page with full details

    SMiP courses open to CDSS doctoral students:

    Foundations 2: Modeling Intraindividual Variabilty and Change Instructors: Tanja Lischetzke & Sabine Sonnentag, Dates: 30.03. (10:00 – 17:00) and 31.03.2020 (09:00 – 16:00),  Location: Landau

    Foundations 2: Stochastic Models of Time-Dependent Cognitive Mechanisms Instructors: Andreas Voss & Rolf Ulrich, Dates: 28.05. (10:00 – 18:00) and 29.05.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Foundations 2: Multinomial-Processing-Tree (MPT) Modeling: Basic Methods and Recent Advances Instructors: Edgar Erdfelder &  Daniel Heck (University of Marburg), Dates: 25.06. (10:00 – 18:00) and 26.06.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Postponed to winter term 2020Academic Writing and Publishing Instructor: Benjamin Hilbig, Dates: 25.09. (10:00 – 18:00) and 26.09.2020 (09:00 – 17:00), Location: Mannheim

    Growth models and variants of growth models Instructor: Paul Bliese, Dates: 07.04. (10:00 – 18:00) and 08.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Python basics Instructors: Stefan Radev and Ulf Mertens, Dates: 27.04. (10:00 – 18:00) and 28.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Advanced / Modern R Instructor: David Izydorczyk, Dates: 12.05. (10:00 – 18:00) and 13.05.2020 (09:00 – 12:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    CXLD – IRT Modeling – Theory and Applications in R Instructor: Thorsten Meiser, Date: 13.05.2020 (13:00 – 17:00), Location: Mannheim (Building B6, 30–32, room 211) (NOTE: Online contents as prerequisites (workload about 4–5 hours) for attending the meeting in Mannheim!)

    Postponed to spring semester 2021 – Multilevel Structural Equation Modeling Instructor: Kristopher Preacher,

    Speed-accuracy mechanisms in continuous and discrete-state decision models Instructor: Jeffrey Starns, Dates: 26.05. (10:00 – 18:00) and 27.05.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants). The instructor will share material for self-study and preparation with the registered participants.

    CXLD – Bayesian hierarchical and factor analysis models Instructor: Jeffrey Rouder, Date: 17.06.2020 (10:00 – 18:00), Location: Mannheim

    Transparent Open Science Instructor: Jeffrey Rouder, Date: 24.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Introduction to Bayesian Modeling Instructor: Jeffrey Rouder, Dates: 29.06. (16:30 – 21:30) and 30.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Robust Modeling in Cognitive Science Instructor: Jeffrey Rouder, Dates: 06.07. (16:30 – 21:30) and 07.07.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Please register by sending an email to Annette Förster.

    Registration deadline: 29 February 2020

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 8
    Prerequisites

    Topics covered in introductory Game Theory class

    Course Content

    This course is a continuation of the intro into Game Theory and surveys key applications of game theory with a particular emphasis on the link of theories, methods and empirics. Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. Topics covered include electoral competition, delegation, political agency, governmental veto players, authoritarian politics, manipulation, war and crisis bargaining. While the focus is on understanding applied work, previous training in game theory is required. Students will build upon their previous game theory training to become informed consumers of scholarship utilizing the methodology and begin to learn how to apply game-theoretic logic to their own work. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

    Course requirements & assessment

    Class discussion, paper presentation, term paper

    Schedule
    Lecture
    12.02.20 – 27.05.20 Wednesday 10:15 – 11:45 B 317 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 class attendance is recommended, mandatory reading

    Term paper

    Schedule
    Lecture
    10.02.20 – 25.05.20 Monday 10:15 – 11:45 B 244 in A5, 6 entrance B Link
    POL: International Political Economy
    6 ECTS
    Lecturer(s)

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

    In this course, we will discuss contemporary scholarly research on International Political Economy. The course examines how domestic and international politics drive trade, investment, financial, and immigration policies and outcomes.  It emphasizes the theoretical core, and some current debates, in the field but also aims to expose students to some nuts and bolts of each policy area and the chief methods by which scholars acquire knowledge of the subject. We pick up some knowledge of historical and contemporary examples wherever possible, but presenting historical material systematically is not the focus of the course. The course is intended to stimulate students to form original ideas for promising research projects in the area of international relations and political economy.

    Assessment: Term paper

    Schedule
    Lecture
    12.02.20 – 27.05.20 Wednesday 12:00 – 13:30 A 103 in B6, 23–25 entrance A Link
    POL: Selected Topics in Comparative Politics: Withering Democracy? Citizen Attitudes toward the Political System
    8 ECTS
    Lecturer(s)

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

    Around the world liberal democracy has been facing challenges for several years. For example, some political leaders who call into question key principles of liberal democracy. As public support is considered an important factor of the persistence of democracy, this seminar will address citizen attitudes toward the political system. This seminar will address key conceptual, theoretical, and methodological issues in the analysis of citizen attitudes toward the political system. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.

    Oral presentation of a literature review, presentation of research project, and active participation during the sessions.Term paper.

    Schedule
    Seminar
    11.02.20 – 26.05.20 Tuesday 12:00 – 13:30 B 317 in A5, 6 entrance B Link
    POL: Selected Topics in International Politics: Repression, Security & Peace
    8 ECTS
    Lecturer(s)

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

    This seminar discusses seminal and current work on state repression, security and peace. It introduces on why and how states violate human rights. It focuses on how governments organize and implement repression and how they aim to justify or obfuscate state violence, particularly in the context of democratic institutions and international human rights norms. The discussion also discusses peace as a more heterogenous concept than the absence of war. Over the course of the seminar you will develop your own research question on one of the topics discussed in the seminar and carrying out your own research. Additionally, you are expected to read all required materials, provide feedback on other student’s work and lead one class discussion.

    • Participation: Discussion and analysis of academic literature, development and discussion of research questions, group discussions (Vorleistung)
    • Presentation: Critical literature review (Vorleistung)
    • Discussion of another student’s research proposal (Vorleistung)
    • Written research proposal (Vorleistung)
    • Research paper
    Schedule
    Seminar
    11.02.20 – 26.05.20 Tuesday 13:45 – 15:15 A 302 in B6, 23–25
    RES: Interdisciplinary Work in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES
    Credits: 5
    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS.

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by me, each participant will shortly (max 10 min, 3 slides per person) present the core idea of an interdisciplinary paper that involves her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. GESS Research Day

    The GESS research day – kindly co-organized by your student representatives – consists of presentations by PhD students from all three centers and discussion panels with senior experts. Participation at the GESS Research Day is mandatory for all course participants. You will give  presentation on a current working paper or research project of yours and you will  discuss a paper/presentation from one of your fellow students from another field (15–20 min presentation, 5 min discussion, 5 min Q&A). Additionally, you will participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

    3. Science Speed Dating

    The science speed dating event – also organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a students from another field.

    4. Project Presentations & Writeups

    The proposals will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in one week after the presentation.

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in one discussion panel at the GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a the write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline by sending title and abstract of the research project/topic you would like to present during the GESS research day to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first serve basis.


    Course Dates:

    March 11th, 2020, 10am-1pm, Kick-Off Meeting, Room 409 in L9, 1–2 (4. OG)        

    April 23th, 2020, Deadline to send paper to discussant (and in cc: to gess uni-mannheim.de)

    April 30th, 2020, Research Day (whole day symposium) – cancelled

    May 4th, 2020, time TBA – Science Speed Dating Event

    May 28th, 2020, Presentation of research proposal (half- to full day symposium)


    Course Room: B6, 30–32, room 211

  • 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.

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

    Schedule
    Workshop
    1st meeting, further dates tbd 11.02.20 Tuesday 394066:15 – 11:45 211 in B6, 30–32
    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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    RES: AC3/BC4: Colloquia II
    2 ECTS
    Lecturer(s)

    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 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: RES
    Credits: 2 or 3 depending on applicable study regulations
    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 Cognitive Psychology: Research projects in cognitive psychology and neuropsychology are planned, conducted, analyzed, and discussed.

     Application via 'Studierendenportal' is necessary to have access to the course material provided in ILIAS.

    Open office hours:
    Prof. Dr. Erdfelder: Thursday, 10.15h – 11.45h.


    Literature: References will be given during the course.

    Talk schedule

    Competences acquired

    Improvement in research skills and communication of research results.

    Schedule
    Workshop
    10.02.20 – 25.05.20 Monday 15:30 – 17:00 C 217 in A5, 6 entrance C Link
    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.

    Schedule
    Workshop
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C 112 in A 5, 6 entrance C
    RES: English Academic Writing for Psychologists 2nd part (CDSS only)
    2 ECTS
    Lecturer(s)

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

    This course provides guidance, tools, and skills for/in English academic writing. The focus will be on writing and publishing articles in psychological journals. Important general techniques for writing will be discussed (and practiced in the form of exercises and writing assignments). Publication strategies, issues of journal selection, how to deal with reviews, and the usual “paperwork” (cover letter, revision letter etc) will also be covered.

    Dates

    17 April 10am to 6pm

    18 April 9am to 5pm

    RES: English Academic Writing for Psychologists 2nd part (CDSS only)
    2 ECTS
    Lecturer(s)

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

    This course provides guidance, tools, and skills for/in English academic writing. The focus will be on writing and publishing articles in psychological journals. Important general techniques for writing will be discussed (and practiced in the form of exercises and writing assignments). Publication strategies, issues of journal selection, how to deal with reviews, and the usual “paperwork” (cover letter, revision letter etc) will also be covered.

    Dates

    17 April 10am to 6pm

    18 April 9am to 5pm

    MET: 1-Click Reproducibility: Online Teaching Materials for Data Science with Python
    0 ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    The goal of this interactive lecture is to provide an overview of online self-teaching materials for Data Science with Python. Data Science is the interdisciplinary science of the extraction of useful knowledge from heterogeneous and potentially large datasets and Jupyter Notebooks are the goto environment for this kind of analysis. Participants will obtain a hands-on introduction to the Jupyter ecosystem as well as online services for working with Jupyter Notebooks such as mybinder.org and GESIS Notebooks.

    Please note that participants should bring their own laptop for this course. All utilized software is browser-based and online available.
    Lecturer: Dr. Arnim Bleier is a postdoctoral researcher in the Department of Computational Social Science at GESIS. His research interests are in the field of Natural Language Processing and Computational Social Science. In collaboration with social scientists, he develops Bayesian models for the content, structure and dynamics of social phenomena.

    Registration deadline: 31 March

    Date:    07.04.2020, 13.45 -- 15:15
    (link to virtual room will be sent to participants)

    MET: 9th GESIS Summer School in Survey Methodology
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

    CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology and are exempt from course fees*. Please register directly through the GESIS web page and make sure to mention that you are a CDSS doctoral student.

    The courses take place in Cologne and take place from 6 to 28 August 2020. 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

    This course on 'Advanced Quantitative Methods' introduces doctoral students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.

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

     

    Tutorial

    This tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing data sets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 B 317 in A5, 6 entrance B
    Lecture
    12.02.20 – 27.05.20 Wednesday 08:30 – 10:00 B 243 in A 5, 6 entrance B
    MET: Big Data in Immigration Research – registration must be made via the lecturer and the CDSS
    6 ECTS
    Lecturer(s)

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

    Students should have some interest and experience in one of the following areas: (1) working with large, unstructured data sets, (2) immigration research, and (3) project management. Students do not need to have extensive experience in all three areas. However, students are expected to have taken at least one statistics course and have basic familiarity with a software program that can be used for statistical analysis (e.g., R, Python, SAS, Stata). Although we will not teach students how to use statistical software, students who want instruction in this area can access DataCamp learning modules that will be made available for the course.

    Course Content

    The growing complexity of human mobility and the integration of immigrants into host societies has created an increased need for reliable and timely data to inform policy development and humanitarian assistance. Data from traditional sources (e.g., national population censuses, sample surveys, and administrative sources) on migration and immigration are limited in quantity and quality, and new alternatives have recently emerged. Some of these new types of “Big Data” are particularly promising for the study of migration-related phenomena. These include mobile phone call logs, Internet activity (e.g., Google searches, tracking of online media content use), geo-referenced social media activity, and other passively collected (mobile) data. In this course, students from the two partnering universities will form international groups to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions.

    Lectures

    Class lectures will be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. 
    Group project
    Students from the two partnering universities will form international teams to collaboratively work on the collection and analysis of Big Data to answer immigration-related research questions. Students will virtually attend the same class/lecture and then collaborate via online tools (Canvas and Zoom) on their projects.

    Literature

    Assigned readings for each lecture are indicated on the syllabus. They are available online or through the course website.

    Active participation, homework and presentations, final written term paper.

    Competences acquired

    In this course students will learn to:

    • obtain and analyze data from non-traditional sources;
    • formulate and answer research questions related to migration and immigration using such data;
    • work in teams to scope a problem, distribute work, and combine their results for a joint presentation; and
    • work as part of an international collaboration with teams formed across countries.
    Schedule
    Seminar
    No seminar on 17 Feb & 25 May 10.02.20 – 25.05.20 Monday 17:15 – 18:45 C 212 in A5, 6 entrance C
    MET: Experimental Methods in Sociology and the Social Sciences
    6 ECTS
    Lecturer(s)

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

    A growing interest in causal identification and mechanisms testing in the social sciences has provided a surge in empirical research using the experimental method. As a result, the experimental methodology has become a common tool not just of psychologists and behavioral economists but also of sociologists and political scientists enabling them to test (bounded) rational choice theories and to isolate and study the causes, dynamics and effects of social phenomena. For example, important sociological concepts such as trust and trustworthiness, cooperation in social dilemma and social norms have been examined via using survey, field, lab-in-the-field and laboratory experiments.
    The main objective of this seminar is to introduce students to the range of experimental methods, classical work as well as recent trends and best practices of experimental social science research. In addition, the seminar aims to teach students how to design and analyze an experiment aimed at answering a self-developed research question. Each student is expected to actively participate in classes by presenting and discussing papers selected by the instructor and to develop an experimental design of her/his own or in collaboration with one other student of the seminar.

    Course requirements & assessment

    Weekly response papers (Week 3 – 12) (30%)
    Write a review paper & guide a class discussion for one session (30%)
    In-class presentation of research design (10%)
    Research Design as final paper (30%)
    Active participation

    Schedule
    Seminar
    12.02.20 – 27.05.20 Wednesday 12:00 – 13:30 309 in B6, 30–32 Link
    MET: Fundamentals of Computing and Data Display
    6 ECTS
    Lecturer(s)

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

    Some basic experience with programming in R or Python is helpful, but not strictly necessary. Students without any R knowledge are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

    rstudio.com


    rstudio.cloud/learn/primers


    www.statmethods.net


    swirlstats.com

    Course Content

    Empirical social scientists are often confronted with a variety of data sources and formats that extend beyond structured and handleable survey data. With the emergence of Big Data, especially data from web sources play an increasingly important role in scientific research. However, the potential of new data sources comes with the need for comprehensive computational skills in order to deal with loads of potentially unstructured information. Against this background, the first part of this course provides an introduction to web scraping and APIs for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. Tools for reproducible research will be introduced to facilitate transparent and collaborative programming. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.

    Course requirements & assessment

    Participation
    Presentation
    Written term paper

    Schedule
    Seminar
    not on 19 March 13.02.20 – 07.05.20 Wednesday 15:30 – 18:00 C 212 in A5, 6 entrance C
    MET: Longitudinal Data Analysis (Lecture + Lab Course)
    6+3 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture 'Longitudinal Data Analysis'

    The course provides a broad overview over methods of longitudinal data analysis, with a focus on the analysis of panel data. Compared to cross-sectional data, panel data can allow to improve causal inference. The first objective of this course is to understand why and under which conditions this is the case. In the next step, we will discuss a variety of different modeling approaches to panel data (fixed effects, random effects, first difference) and learn how to decide between these models. The lecture also provides an overview over event history models. It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models are applied to real data sets.

    Tutorial “Data Sources in Social Sciences” taught by Andreas Weiland

    Using Stata, we apply methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) to real survey data. Attendance of the complementary lecture “Longitudinal Data Analysis” is highly recommended as firm knowledge of the lecture content is presumed. Some knowledge of Stata is helpful, but not required.
     

    6 ECTS will be awarded for successful completion of a 90 minute exam and an additional 3 ECTS can be awarded for participation in the lab course where active participation and short oral presentations are expected.

    Schedule
    Tutorial
    13.02.20 – 28.05.20 Thursday 12:00 – 13:30 C -108 in A 5, 6 entrance C
    Lecture
    02.03.20 – 25.05.20 Monday 15:30 – 17:00 B 243 in A 5, 6 entrance B Link
    MET: Machine Learning
    6 ECTS
    Lecturer(s)

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

    Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course.

    Course Content

    This course provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses
    their potential application in the social sciences. These methods focus on predicting an outcome Y based on some learned function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists’ toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune, evaluate and interpret prediction models using R.

    Learning outcomes: At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods using R.

    Form of assessment: Presentation and / or term paper

    Schedule
    Lecture
    18.02.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    03.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    17.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    31.03.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    21.04.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    05.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    19.05.20 Tuesday 15:30 – 18:45 B 6, 30–32, room 211
    MET: Modelling Social Processes
    6 ECTS
    Lecturer(s)

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

    This course provides an introduction to formal models in the social sciences. It discusses a series of basic prototypes which have proved to be important tools for theory construction in various fields. Relating to the general model of sociological explanation (‘Coleman boat’), the focus is on processes of the steps of (non-trivial) aggregation and on dynamics over time. Topics covered are, for example, exchange, strategic action, collective action and the evolution of cooperation, mobilisation, diffusion, or segregation. While most of the models and examples chosen might already be fairly well known, this course puts specific emphasis on explaining the math behind them in more detail than usual and on practically ‘playing around’ with the models. Thus, it will provide some expertise and training in general formal skills, such as game theory, difference equations, differential equations, and agent-based simulation. The aim is to enable participants in principle to modify, extend or combine existing models according to their own research questions.

    Course requirements & assessment

    Participation, homework, presentations

    Written term paper (max. 5000 words)

    Schedule
    Seminar
    11.02.20 – 26.05.20 10:15 – 11:45 C 112 in A5, 6 entrance C
    MET: New Developments in Test Theory and Test Construction
    4 ECTS
    Lecturer(s)

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

    The seminar presents recent advances in the fields of linear test models and probabilistic test theory. The first part on linear test models builds on the tenets of classical test theory and focuses on the decomposition of observed values into true scores and error terms. The topics include congeneric measurement models, multitrait-multimethod models, bifactor models and latent state-trait models. The second part of the seminar covers models of item response theory, which specify the probability of observed responses as a function of latent person and item attributes. The topics include probabilistic test models for binary and ordinal responses, and extensions to mixture-distribution and multidimensional IRT models.

    The linear and probabilistic test models will be introduced with their theoretical and formal foundations, and applications will be discussed and illustrated in different fields of psychological research and practice.

    Course requirements & assessment
    Presentation
    Exam

    Schedule
    Seminar
    14.02.20 – 29.05.20 Friday 08:30 – 10:00 tbc 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 web page with full details

    SMiP courses open to CDSS doctoral students:

    Foundations 2: Modeling Intraindividual Variabilty and Change Instructors: Tanja Lischetzke & Sabine Sonnentag, Dates: 30.03. (10:00 – 17:00) and 31.03.2020 (09:00 – 16:00),  Location: Landau

    Foundations 2: Stochastic Models of Time-Dependent Cognitive Mechanisms Instructors: Andreas Voss & Rolf Ulrich, Dates: 28.05. (10:00 – 18:00) and 29.05.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Foundations 2: Multinomial-Processing-Tree (MPT) Modeling: Basic Methods and Recent Advances Instructors: Edgar Erdfelder &  Daniel Heck (University of Marburg), Dates: 25.06. (10:00 – 18:00) and 26.06.2020 (09:00 – 17:00), Location: remote, more information on the SMiP web page.

    Postponed to winter term 2020Academic Writing and Publishing Instructor: Benjamin Hilbig, Dates: 25.09. (10:00 – 18:00) and 26.09.2020 (09:00 – 17:00), Location: Mannheim

    Growth models and variants of growth models Instructor: Paul Bliese, Dates: 07.04. (10:00 – 18:00) and 08.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Python basics Instructors: Stefan Radev and Ulf Mertens, Dates: 27.04. (10:00 – 18:00) and 28.04.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Advanced / Modern R Instructor: David Izydorczyk, Dates: 12.05. (10:00 – 18:00) and 13.05.2020 (09:00 – 12:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    CXLD – IRT Modeling – Theory and Applications in R Instructor: Thorsten Meiser, Date: 13.05.2020 (13:00 – 17:00), Location: Mannheim (Building B6, 30–32, room 211) (NOTE: Online contents as prerequisites (workload about 4–5 hours) for attending the meeting in Mannheim!)

    Postponed to spring semester 2021 – Multilevel Structural Equation Modeling Instructor: Kristopher Preacher,

    Speed-accuracy mechanisms in continuous and discrete-state decision models Instructor: Jeffrey Starns, Dates: 26.05. (10:00 – 18:00) and 27.05.2020 (09:00 – 17:00), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants). The instructor will share material for self-study and preparation with the registered participants.

    CXLD – Bayesian hierarchical and factor analysis models Instructor: Jeffrey Rouder, Date: 17.06.2020 (10:00 – 18:00), Location: Mannheim

    Transparent Open Science Instructor: Jeffrey Rouder, Date: 24.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Introduction to Bayesian Modeling Instructor: Jeffrey Rouder, Dates: 29.06. (16:30 – 21:30) and 30.06.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Robust Modeling in Cognitive Science Instructor: Jeffrey Rouder, Dates: 06.07. (16:30 – 21:30) and 07.07.2020 (16:30 – 21:30), Location: ZOOM (Room SOWI-ZOOM-18, invitation via email for registered participants)

    Please register by sending an email to Annette Förster.

    Registration deadline: 29 February 2020

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

    Course Type: elective course
    Course Number: MET/POL
    Credits: 8
    Prerequisites

    Topics covered in introductory Game Theory class

    Course Content

    This course is a continuation of the intro into Game Theory and surveys key applications of game theory with a particular emphasis on the link of theories, methods and empirics. Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. Topics covered include electoral competition, delegation, political agency, governmental veto players, authoritarian politics, manipulation, war and crisis bargaining. While the focus is on understanding applied work, previous training in game theory is required. Students will build upon their previous game theory training to become informed consumers of scholarship utilizing the methodology and begin to learn how to apply game-theoretic logic to their own work. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

    Course requirements & assessment

    Class discussion, paper presentation, term paper

    Schedule
    Lecture
    12.02.20 – 27.05.20 Wednesday 10:15 – 11:45 B 317 in A5, 6 entrance B Link
    MET/PSY: Psychological interventions using diary designs
    4 ECTS
    Lecturer(s)

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

    During recent years interventions using diary methods became increasingly popular within several fields of psychology, including health psychology and organizational psychology. These interventions use „intensive longitudinal designs“ to apply the treatment and to assess the data and build on daily-survey approaches that aim at „capturing life as it is lived” (Bolger, Davis, Rafaeli, 2003, p. 579). Frequent assessments typically implemented in daily-survey approaches allow for modeling change in affect, attitude, and behavior over time.
    In this course we will discuss the nature of diary interventions, the research options they offer, as well as potential problems and challenges.

    Literature (a more comprehensive list will be available in the first meeting)

    Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616.
    Lischetzke, T., Reis, D., & Arndt, C. (2015). Data-analytic strategies for examining the effectiveness of daily interventions. Journal of Occupational and Organizational Psychology, 88, 587–622. doi:10.1111/joop.12104

    Course requirements & assessment

    Reading relevant literature
    Short oral presentation
    Group work

    Term paper

    Schedule
    Seminar
    13.02.20 – 28.05.20 Thursday 17:15 – 18:45 B 143 in A5,6 entrance B
    PSY: Advanced Social and Economic Cognition
    4 ECTS
    Lecturer(s)

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

    This seminar is targeted at doctoral students and postdoctoral researchers in Psychology.

    Course Content

    Students will present planned and on-going research (ideas, designs, results) and discuss it with the participants. In some sessions, papers on theoretical or methodological perspectives will be discussed. Some sessions can be dedicated to discussing participants' own drafts and get feedback before submission. The seminar also provides the opportunity to get feedback on practicing conference presentations/job talks etc. Topics may cover all areas of social psychology and consumer psychology.

    Schedule
    Seminar
    13.02.20 – 28.05.20 Thursday 10:15 – 11:45 tbc
    RES: Interdisciplinary Work in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES
    Credits: 5
    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS.

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by me, each participant will shortly (max 10 min, 3 slides per person) present the core idea of an interdisciplinary paper that involves her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. GESS Research Day

    The GESS research day – kindly co-organized by your student representatives – consists of presentations by PhD students from all three centers and discussion panels with senior experts. Participation at the GESS Research Day is mandatory for all course participants. You will give  presentation on a current working paper or research project of yours and you will  discuss a paper/presentation from one of your fellow students from another field (15–20 min presentation, 5 min discussion, 5 min Q&A). Additionally, you will participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

    3. Science Speed Dating

    The science speed dating event – also organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a students from another field.

    4. Project Presentations & Writeups

    The proposals will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in one week after the presentation.

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in one discussion panel at the GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a the write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline by sending title and abstract of the research project/topic you would like to present during the GESS research day to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first serve basis.


    Course Dates:

    March 11th, 2020, 10am-1pm, Kick-Off Meeting, Room 409 in L9, 1–2 (4. OG)        

    April 23th, 2020, Deadline to send paper to discussant (and in cc: to gess uni-mannheim.de)

    April 30th, 2020, Research Day (whole day symposium) – cancelled

    May 4th, 2020, time TBA – Science Speed Dating Event

    May 28th, 2020, Presentation of research proposal (half- to full day symposium)


    Course Room: B6, 30–32, room 211