Spring 2023

  • Sociology

    Dissertation Tutorial: Sociology
    0 ECTS
    Lecturer(s)

    Course Type: core course
    Course Content

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

    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
    further dates tbd 14.02.23 Tuesday 10:15 – 11:45 Zoom Link
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    biweekly 17.02.23 – 31.03.23 Friday 13:45 – 17:00 211 in B6, 30–32
    biweekly 21.04.23 – 02.06.23 Friday 13:45 – 17:00 211 in B6, 30–32
    RES: CDSS Workshop: Sociology
    2 ECTS
    Lecturer(s)
    Lars Leszczensky

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

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

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

    Workshop times will be determined by Lars Leszczensky closer to the start of the semester.

    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
    16.02.23 – 01.06.23 Thursday 12:00 – 13:30 A 103 in B6, 23–25 Link
    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: 12th GESIS Summer School in Survey Methodology & GESIS Workshops
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

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

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

    The GESIS summer school takes place in Cologne from tbc. Detailed information about the summer school program is available on the GESIS website.

     

     

     

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

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

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

    Knowledge of Multivariate Analysis

    Course Content

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

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

    Literature

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

    Course requirements & assessment

    Homework assignements, research paper (all graded)

     

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. 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
    Lecture
    15.02.23 – 31.05.23 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    Oliver Rittmann 16.02.23 – 01.06.23 Thursday 10:15 – 11:45 tbc
    Domantas Undzenas 16.02.23 – 01.06.23 Thursday 15:30 – 17:00 A102 in B6 23–25
    MET: AI & Machine Learning for Social Scientists
    6 ECTS
    Lecturer(s)

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

    Tbc

    Course requirements & assessment

    • Participation
    • Weekly reading and preparation of materials and exercises
    • (Individual) Presentation of the planned term paper towards the end of the term
    • Written term paper (graded)
    Schedule
    Seminar
    15.02.23 – 31.05.23 Wednesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: Experimental Designs in the Social Sciences
    6 ECTS
    Lecturer(s)

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

    Experimental research designs are called the silver bullet or ‘Königsweg’ for causal identification. In recent years, the growing interest in causal identification and mechanism testing made experimental designs a regular empirical research tool in the social sciences – most recently in political science and sociology. This seminar shall give a broad overview of the range of experimental methods such as survey, field, lab-in-the-field, and laboratory experiments. We will discuss classical and recent work, including shortcomings and best practices like transparency (open science) and ethical considerations in experimental research methods. In addition, students will learn to think critically about different (experimental) research designs and design their own experiment to answer a research question they have developed. 

    Course requirements & assement

    Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar, presentation of the Exposé of the seminar paper (graded (incl. peer-feedback)), research design seminar paper (graded)

    Schedule
    Seminar
    18.04.23 – 30.05.23 Tuesday 08:30 – 11:45 C 116 in A5, 6 entrance C Link
    MET: Fundamentals in Survey Design
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of survey design. The course covers the major steps of implementing and conducting a survey and design decisions at each step. In addition, sources of error at each step are discussed. For illustration purposes and exercise, the course will draw on well-known large-scale surveys such as the German General Survey (ALLBUS), European Social Survey (ESS), European Values Study (EVS), and the German Socio-economic Panel (SOEP).

    Course requirements & assessment

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

    Schedule
    Seminar
    16.02.23 – 01.06.23 Thursday 13:45 – 15:15 tbc
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture

    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.

    It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models  are applied to real datasets using the statistical programming language R. Although prior knowledge of R is not a prerequisite for attending the lecture/tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com"

    Tutorial

    Using R, we apply methods of longitudinal data analysis (especially first-difference-models and random/fixed effects-models) to real survey data. Although prior knowledge of R is not a prerequisite for attending the tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Successful participation in the tutorial (active participation, short oral presentation, short assignments (graded), written exam (graded)

     

    Schedule
    Lecture
    13.02.23 – 22.05.23 Monday 15:30 – 17:00 B 143 in A5, 6 entrance B
    Tutorial
    14.02.23 – 30.05.23 Tuesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue – further information to follow in due course.

    MET: Statistics, Data Science and Machine Learning in Python
    6 ECTS
    Lecturer(s)
    Alexander Scherf

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

    Some programming skills (Python, R, JAVA, C, HTML, BASH, etc.) OR the motivation to learn some python (and some other languages) on your own.

    **Requirement: Students should bring their own laptop (on which you can also install programmes, not just apps).**

    Course Content

    This course is intended to show you all the major steps involved in completing a statistical analysis within the fields of exploratory data analysis and data science.

    This seminar is divided into 3 parts:
    First, we will go through the basics of Python and the most important libraries for data science with excursuses into “programming paradigms” and “big data”.

    Second, we will learn data exploration, data visualisation and statistical modelling with python.
    Third, we will go through the basics of machine learning (supervised, unsupervised and semi-supervised) and neural networks with excususes into the fields of “computer vision”, “computer linguistics” and “AI”.

    And finally, we will apply all of this to real-world projects.
    For this course, I’ve chosen several different statistical problems to be solved with regression and classification in python.

    Course requirements & assessment

    Coding-homework, Data analysis project written in python including data transformation, visualisation and analysis (graded)

    Schedule
    Seminar
    biweekly 17.02.23 – 31.03.23 Friday 08:30 – 13:30 tbc
    biweekly 28.04.23 – 26.05.23 Friday 08:30 – 13:30 tbc
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

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

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

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

    Course Content

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

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

    Learning outcomes:

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

    • Analytical Skills/Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools.
    • Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
    • Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses.
    • Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
    • Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
    • Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
    • Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.

    Form of assessment: Paper (technical report) (optional), Presentation (50 %), Class Participation (50 %)

     


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule
    Lecture
    Lecture 14.02.23 – 30.05.23 Tuesday 10:15 – 11:45
    RES / IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES / IntRes
    Credits: 5
    Prerequisites

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll. As a necessary requirement you need to make a working paper draft available to all of us that you present in our ‘Mini Research Day’.

    Course Content

    This course will introduce students to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    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 the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in 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. Alternatively, you can also present a dataset or a methodology and highlight how students from other GESS centers might take advantage of it.

    2. Mini-Research-Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to other course participants. You will give a 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 (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – 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 student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. Each research team will also prepare a short 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 two weeks after the presentation. Moreover, you will also discuss another team project.

    Objectives

    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 received feedback from senior researchers from all three centers.
    • 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.

    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 the ‘Mini 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 a week 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 write-up.
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a prize.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to gess.registration 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, Political Science) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first served basis.

    Course dates

    • January 31st, 2023 – Course Registration Deadline
    • February 15th, 2023, 10:00 – 14:00 – Kick-Off Meeting
    • February 23th – Deadline to send paper to discussant (and in cc to: gess.office@uni-mannheim.de)
    • March 2nd, 2023, 10:00 – 19:00 – Mini Research Day
    • March 30th, 2023, exact time TBA – Science Speed Dating event
    • May 25, 2023, 10:00 – 19:00 – Presentation of Research Proposal
    • June 8th, 2023 – Deadline to hand in interdisciplinary research proposal (to: gess.office@uni-mannheim.de)
    Competences acquired

    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 received feedback from senior researchers from all three centers.
    • 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.
    SOC: Digital Transformations of Work
    6 ECTS
    Lecturer(s)

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

    Digital transformations in companies, in sectors of the economy, in the labor force, and in the world of work in general are one of the most fundamental societal transformations in contemporary history. Digital transformations of work and beyond shape our daily lives and might trigger fundamental challenges to the organization of work and beyond. How do we conceptualize these digital transformations? Are these rather social or rather technical transformations? What are the main characteristics of these transformations? How does digitalization permeate the world of work? Is it a perpetuating process? How can we measure digital transformations? What are the drivers of digital transformations? And what are the consequences for individuals and families? The seminar will address these questions and offers conceptual and empirical insights in the discussion of the digital transformations of work.

    Course requirements & assessment

    Regular small assignments (developing research questions based on the readings, short presentations); compulsory attendance; participating in active discussion.
    Written term paper (graded, max. 5000 words), deadline: July 31, 2023

     

    Schedule
    Seminar
    biweekly 15.02.23 – 29.03.23 Wednesday 08:30 – 11:45 tbc
    biweekly 19.04.23 – 03.05.23 Wednesday 08:30 – 11:45 tbc
    24.05.23 Wednesday 08:30 – 11:45 tbc
    SOC: Field Experiments: A Hands-On Introduction
    6 ECTS
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    Field experiments are powerful tools for investigating causal claims about social phenomenon in real-life contexts.  This block seminar will provide students with a practice-based introduction to field experiments.  While we cover the logic behind experimentation and the potential outcomes framework, the heart of the course will focus around analyzing examples of actual experimental designs.  In this way, students will gain hands-on experience in navigating the myriad issues that may arise when conducting, analyzing, and interpreting field experiments.  Students will also have the opportunity to obtain feedback on their own experimental research projects.

    The seminar will be taught by Nan Zhang, PhD

    Course requirements & assessment

    Oral participation, homework, presentations, compulsory attendance
    Term paper (graded, 5000 words)

    Schedule
    Seminar
    biweekly 02.03.23 – 30.03.23 Thursday 10:15 – 13:30 tbc
    biweekly 20.04.23 – 01.06.23 Thursday 10:15 – 13:30 tbc
    SOC: Measuring and explaining xenophobic and right-wing populist attitudes
    6 ECTS
    Lecturer(s)

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

    In the age of increasing migration and the raise of right-wing populist parties the question of how to measure and explain xenophobic and populist attitudes becomes very important. While xenophobia has already been investigated for a long time, even if it still constitutes a controversial issue how to measure it, research on populist attitudes has started only very recently. In this seminar current and innovative approaches as well as ideas for further developments will be discussed. Moreover, existing studies will be replicated to explore them more deeply.

    Course requirements & assessment

    Participation, weekly reading, presentation of an empirical study, term paper (graded)

    Schedule
    Seminar
    15.02.23 – 31.05.23 Wednesday 10:15 – 11:45 B 317 in A5, 6 entrance B
    SOC: Political Networks
    6 ECTS
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    Tbc

    This course will be taught by Benjamin Rohr

    Schedule
    Seminar
    13.02.23 – 22.05.23 Monday 13:45 – 15:15 C 217 in A5, 6 entrance C
    SOC: Social Determinants of Health
    6 ECTS
    Course Type: elective course
    Course Number: SOC
    Credits: 6
    Course Content

    What makes people healthy or ill? Individual health is surely shaped by individual decisions regarding lifestyle or use of healthcare. However, the systematic social inequalities in health are large and persist over time. People's social position plays a fundamental role in shaping their health. The characteristics of the society as a whole are likely important too. This course offers an introduction to the health consequences of people’s social position and social circumstances.This 3-part course introduces students to selected topics in health sociology.
    The first part discusses key notions of health sociology and the role of social factors in the historical development of population health.
    In the second part, we tackle the topic of individual factors associated with health inequalities. We begin by reviewing the role of socioeconomic status and education and discuss the empirical patterns in light of the selection vs. social causation hypothesis. Subsequently, we address the role of gender, work, and migration in creating and sustaining health differences.
    In the third part, the course shifts the focus to the macro determinants of health. We begin by reviewing the discussion on income inequalities and health and address the role of gender inequality. To address the underlying mechanisms, we look at the role of perceived (vs. objective) inequality. Subsequently, we discuss the role of social capital, and the role played by policies.

    Course requirements and assessment

    Students are required to attend all classes (two absences will be excused). Credits will be granted for active participation, an oral presentation, and a paper on one of the themes of the seminar. Final paper (graded, 4,000–4,500 words) Submission deadline 12 June 2023

    Syllabus

    Schedule
    Seminar
    15.02.23 – 31.05.23 Wednesday 13:45 – 15:15 A 103 in B6, 23–25 Link
  • 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
    further dates tbd 14.02.23 Tuesday 10:15 – 11:45 Zoom Link
    MET: Advanced Quantitative Methods
    6+2 ECTS
    Lecturer(s)

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

    Knowledge of Multivariate Analysis

    Course Content

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

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

    Literature

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

    Course requirements & assessment

    Homework assignements, research paper (all graded)

     

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. 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
    Lecture
    15.02.23 – 31.05.23 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    Oliver Rittmann 16.02.23 – 01.06.23 Thursday 10:15 – 11:45 tbc
    Domantas Undzenas 16.02.23 – 01.06.23 Thursday 15:30 – 17:00 A 102, in B6, 23–25
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    biweekly 17.02.23 – 31.03.23 Friday 13:45 – 17:00 211 in B6, 30–32
    biweekly 21.04.23 – 02.06.23 Friday 13:45 – 17:00 211 in B6, 30–32
    RES: CDSS Workshop: Political Science
    2 ECTS
    Lecturer(s)

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

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

    Other young researchers in the social sciences affiliated with the University of Mannheim (incl. MZES 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
    13.02.23 – 27.06.22 Monday 15:30 – 17:00 211 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
    16.02.23 – 01.06.23 Thursday 12:00 – 13:30 A 103 in B6, 23–25 Link
    RES: MZES B Colloquium “European Political Systems and their Integration”
    2 ECTS
    Course Type: core course
    Course Number: RES
    Credits: 2
    Course Content

    Please refer to the MZES webpages for dates and times.

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

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

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

    The GESIS summer school takes place in Cologne from tbc. 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: AI & Machine Learning for Social Scientists
    6 ECTS
    Lecturer(s)

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

    Tbc

    Course requirements & assessment

    • Participation
    • Weekly reading and preparation of materials and exercises
    • (Individual) Presentation of the planned term paper towards the end of the term
    • Written term paper (graded)
    Schedule
    Seminar
    15.02.23 – 31.05.23 Wednesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: Experimental Designs in the Social Sciences
    6 ECTS
    Lecturer(s)

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

    Experimental research designs are called the silver bullet or ‘Königsweg’ for causal identification. In recent years, the growing interest in causal identification and mechanism testing made experimental designs a regular empirical research tool in the social sciences – most recently in political science and sociology. This seminar shall give a broad overview of the range of experimental methods such as survey, field, lab-in-the-field, and laboratory experiments. We will discuss classical and recent work, including shortcomings and best practices like transparency (open science) and ethical considerations in experimental research methods. In addition, students will learn to think critically about different (experimental) research designs and design their own experiment to answer a research question they have developed. 

    Course requirements & assement

    Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar, presentation of the Exposé of the seminar paper (graded (incl. peer-feedback)), research design seminar paper (graded)

    Schedule
    Seminar
    18.04.23 – 30.05.23 Tuesday 08:30 – 11:45 C 116 in A5, 6 entrance C Link
    MET: Fundamentals in Survey Design
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of survey design. The course covers the major steps of implementing and conducting a survey and design decisions at each step. In addition, sources of error at each step are discussed. For illustration purposes and exercise, the course will draw on well-known large-scale surveys such as the German General Survey (ALLBUS), European Social Survey (ESS), European Values Study (EVS), and the German Socio-economic Panel (SOEP).

    Course requirements & assessment

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

    Schedule
    Seminar
    16.02.23 – 01.06.23 Thursday 13:45 – 15:15 tbc
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture

    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.

    It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models  are applied to real datasets using the statistical programming language R. Although prior knowledge of R is not a prerequisite for attending the lecture/tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com"

    Tutorial

    Using R, we apply methods of longitudinal data analysis (especially first-difference-models and random/fixed effects-models) to real survey data. Although prior knowledge of R is not a prerequisite for attending the tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Successful participation in the tutorial (active participation, short oral presentation, short assignments (graded), written exam (graded)

     

    Schedule
    Lecture
    13.02.23 – 22.05.23 Monday 15:30 – 17:00 B 143 in A5, 6 entrance B
    Tutorial
    14.02.23 – 30.05.23 Tuesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue – further information to follow in due course.

    MET: Statistics, Data Science and Machine Learning in Python
    6 ECTS
    Lecturer(s)
    Alexander Scherf

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

    Some programming skills (Python, R, JAVA, C, HTML, BASH, etc.) OR the motivation to learn some python (and some other languages) on your own.

    **Requirement: Students should bring their own laptop (on which you can also install programmes, not just apps).**

    Course Content

    This course is intended to show you all the major steps involved in completing a statistical analysis within the fields of exploratory data analysis and data science.

    This seminar is divided into 3 parts:
    First, we will go through the basics of Python and the most important libraries for data science with excursuses into “programming paradigms” and “big data”.

    Second, we will learn data exploration, data visualisation and statistical modelling with python.
    Third, we will go through the basics of machine learning (supervised, unsupervised and semi-supervised) and neural networks with excususes into the fields of “computer vision”, “computer linguistics” and “AI”.

    And finally, we will apply all of this to real-world projects.
    For this course, I’ve chosen several different statistical problems to be solved with regression and classification in python.

    Course requirements & assessment

    Coding-homework, Data analysis project written in python including data transformation, visualisation and analysis (graded)

    Schedule
    Seminar
    biweekly 17.02.23 – 31.03.23 Friday 08:30 – 13:30 tbc
    biweekly 28.04.23 – 26.05.23 Friday 08:30 – 13:30 tbc
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

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

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

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

    Course Content

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

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

    Learning outcomes:

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

    • Analytical Skills/Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools.
    • Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
    • Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses.
    • Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
    • Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
    • Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
    • Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.

    Form of assessment: Paper (technical report) (optional), Presentation (50 %), Class Participation (50 %)

     


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule
    Lecture
    Lecture 14.02.23 – 30.05.23 Tuesday 10:15 – 11:45
    POL: Selected Topics in Comparative Politics: Hot Topics in Economics and Politics
    8 ECTS
    Lecturer(s)

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

    tbc

    Schedule
    Seminar
    21.04.23 – 02.06.23 Friday 10:15 – 13:30 tbc Link
    POL: Selected Topics in Comparative Politics: Volatile, Capricious, Unpredictable? Studying stability and change in voting behavior
    8 ECTS
    Lecturer(s)

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

    Elections are key institutions in democracies and provide opportunities to bring about changes in the partisan balance which, in turn, can affect government policies. This seminar focuses on the analysis of changes in voting behavior at the individual and aggregate level. Thereby, it tackles questions such as how and why such changes occur or not. It will address key concepts and theories, substantive and methodological issues in the field. Students will review empirical studies in the field and prepare research papers in which they analyze specific questions using available data.

    Course requirements & assessment

    Active participation, oral presentation, regular attendance is recommended
    Term paper (graded)

    Schedule
    Seminar
    16.02.23 – 01.06.23 Thursday 12:00 – 13:30 B 318 in A5, 6 entrance B
    POL: Selected Topics in International Politics: Global Inequality
    8 ECTS
    Lecturer(s)

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

    In this course, we study economic inequality from a political economy perspective. First, we will discuss various concepts of economic inequality and different ways to measure it. Then, we will investigate general trends in these various forms of economic inequality across the world. Second, we will discuss the scholarly literature on the determinants of economic inequality, focusing on both political and economic factors. In a third section, we will examine the literature on the implications of economic inequality as regards a variety of political and economic outcomes. The methodological focus of this seminar will be on quantative methods for causal inference.

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Seminar
    14.02.23 – 30.05.23 Tuesday 13:45 – 15:15 B 143 in A5, 6 entrance B Link
    RES / IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES / IntRes
    Credits: 5
    Prerequisites

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll. As a necessary requirement you need to make a working paper draft available to all of us that you present in our ‘Mini Research Day’.

    Course Content

    This course will introduce students to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    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 the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in 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. Alternatively, you can also present a dataset or a methodology and highlight how students from other GESS centers might take advantage of it.

    2. Mini-Research-Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to other course participants. You will give a 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 (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – 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 student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. Each research team will also prepare a short 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 two weeks after the presentation. Moreover, you will also discuss another team project.

    Objectives

    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 received feedback from senior researchers from all three centers.
    • 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.

    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 the ‘Mini 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 a week 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 write-up.
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a prize.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to gess.registration 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, Political Science) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first served basis.

    Course dates

    • January 31st, 2023 – Course Registration Deadline
    • February 15th, 2023, 10:00 – 14:00 – Kick-Off Meeting
    • February 23th – Deadline to send paper to discussant (and in cc to: gess.office@uni-mannheim.de)
    • March 2nd, 2023, 10:00 – 19:00 – Mini Research Day
    • March 30th, 2023, exact time TBA – Science Speed Dating event
    • May 25, 2023, 10:00 – 19:00 – Presentation of Research Proposal
    • June 8th, 2023 – Deadline to hand in interdisciplinary research proposal (to: gess.office@uni-mannheim.de)
    Competences acquired

    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 received feedback from senior researchers from all three centers.
    • 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.
  • 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
    further dates tbd 14.02.23 Tuesday 10:15 – 11:45 Zoom Link
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    Course requirements & assessment

    Active participation, term paper (graded)

    Schedule
    Workshop
    biweekly 17.02.23 – 31.03.23 Friday 13:45 – 17:00 211 in B6, 30–32
    biweekly 21.04.23 – 02.06.23 Friday 13:45 – 17:00 211 in B6, 30–32
    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 ECTS
    Lecturer(s)

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

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

    Research in Psychology: Research projects cognitive psychology and neuropsychology are planned, conducted, analyzed, and discussed.

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

    Literature: References will be given during the course.

    Talk schedule

    Competences acquired

    Improvement in research skills and communication of research results.

    Schedule
    Workshop
    13.02.23 – 29.05.23 Monday 15:30 – 17:00 tbc
    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
    16.02.23 – 01.06.23 Thursday 12:00 – 13:30 A 103 in B6, 23–25 Link
    MET: 12th GESIS Summer School in Survey Methodology & GESIS Workshops
    up to 12 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: up to 12
    Prerequisites

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

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

    The GESIS summer school takes place in Cologne from tbc. Detailed information about the summer school program is available on the GESIS website.

     

     

     

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

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

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

    Knowledge of Multivariate Analysis

    Course Content

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

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

    Literature

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

    Course requirements & assessment

    Homework assignements, research paper (all graded)

     

    Tutorial

    The tutorial accompanies the course “Advanced Quantitative Methods” in Political Science. 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
    Lecture
    15.02.23 – 31.05.23 Wednesday 08:30 – 10:00 B 244 in A5, 6 entrance B Link
    Tutorial
    Oliver Rittmann 16.02.23 – 01.06.23 Thursday 10:15 – 11:45 tbc
    Domantas Undzenas 16.02.23 – 01.06.23 Thursday 15:30 – 17:00 A102 in B6 23–25
    MET: AI & Machine Learning for Social Scientists
    6 ECTS
    Lecturer(s)

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

    Tbc

    Course requirements & assessment

    • Participation
    • Weekly reading and preparation of materials and exercises
    • (Individual) Presentation of the planned term paper towards the end of the term
    • Written term paper (graded)
    Schedule
    Seminar
    15.02.23 – 31.05.23 Wednesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: Experimental Designs in the Social Sciences
    6 ECTS
    Lecturer(s)

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

    Experimental research designs are called the silver bullet or ‘Königsweg’ for causal identification. In recent years, the growing interest in causal identification and mechanism testing made experimental designs a regular empirical research tool in the social sciences – most recently in political science and sociology. This seminar shall give a broad overview of the range of experimental methods such as survey, field, lab-in-the-field, and laboratory experiments. We will discuss classical and recent work, including shortcomings and best practices like transparency (open science) and ethical considerations in experimental research methods. In addition, students will learn to think critically about different (experimental) research designs and design their own experiment to answer a research question they have developed. 

    Course requirements & assement

    Weekly preparation of two discussion-questions, one presentation (allocated text(s), discussion preparation), active participation in seminar, presentation of the Exposé of the seminar paper (graded (incl. peer-feedback)), research design seminar paper (graded)

    Schedule
    Seminar
    18.04.23 – 30.05.23 Tuesday 08:30 – 11:45 C 116 in A5, 6 entrance C Link
    MET: Fundamentals in Survey Design
    6 ECTS
    Lecturer(s)

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

    Surveys are a major data source for quantitative social science research. This graduate-level course will teach the fundamentals of survey design. The course covers the major steps of implementing and conducting a survey and design decisions at each step. In addition, sources of error at each step are discussed. For illustration purposes and exercise, the course will draw on well-known large-scale surveys such as the German General Survey (ALLBUS), European Social Survey (ESS), European Values Study (EVS), and the German Socio-economic Panel (SOEP).

    Course requirements & assessment

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

    Schedule
    Seminar
    16.02.23 – 01.06.23 Thursday 13:45 – 15:15 tbc
    MET: Longitudinal Data Analysis (Lecture + Tutorial)
    6+3 ECTS
    Course Type: elective course
    Course Number: MET
    Credits: 6+3
    Course Content

    Lecture

    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.

    It is highly recommended to participate in the parallel exercises to this lecture, in which the presented models  are applied to real datasets using the statistical programming language R. Although prior knowledge of R is not a prerequisite for attending the lecture/tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com"

    Tutorial

    Using R, we apply methods of longitudinal data analysis (especially first-difference-models and random/fixed effects-models) to real survey data. Although prior knowledge of R is not a prerequisite for attending the tutorial, we recommend that students without any knowledge of R work through one or more of the introductory R tutorials prior to or during the first weeks of the course. Some resources can be found here and we will point to additional resources during the first weeks of the course:  https://rstudio.cloud/learn/primers ; http://www.statmethods.net/ ; https://swirlstats.com/ ; https://datacamp.com

    The course will be taught by Dr. Danielle Martin

    Course requirements & assessment

    Successful participation in the tutorial (active participation, short oral presentation, short assignments (graded), written exam (graded)

     

    Schedule
    Lecture
    13.02.23 – 22.05.23 Monday 15:30 – 17:00 B 143 in A5, 6 entrance B
    Tutorial
    14.02.23 – 30.05.23 Tuesday 15:30 – 17:00 A 102 in B6, 23–25
    MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    Course Type: elective course
    Course Number: MET
    Course Content

    SMiP course catalogue – further information to follow in due course.

    MET: Statistics, Data Science and Machine Learning in Python
    6 ECTS
    Lecturer(s)
    Alexander Scherf

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

    Some programming skills (Python, R, JAVA, C, HTML, BASH, etc.) OR the motivation to learn some python (and some other languages) on your own.

    **Requirement: Students should bring their own laptop (on which you can also install programmes, not just apps).**

    Course Content

    This course is intended to show you all the major steps involved in completing a statistical analysis within the fields of exploratory data analysis and data science.

    This seminar is divided into 3 parts:
    First, we will go through the basics of Python and the most important libraries for data science with excursuses into “programming paradigms” and “big data”.

    Second, we will learn data exploration, data visualisation and statistical modelling with python.
    Third, we will go through the basics of machine learning (supervised, unsupervised and semi-supervised) and neural networks with excususes into the fields of “computer vision”, “computer linguistics” and “AI”.

    And finally, we will apply all of this to real-world projects.
    For this course, I’ve chosen several different statistical problems to be solved with regression and classification in python.

    Course requirements & assessment

    Coding-homework, Data analysis project written in python including data transformation, visualisation and analysis (graded)

    Schedule
    Seminar
    biweekly 17.02.23 – 31.03.23 Friday 08:30 – 13:30 tbc
    biweekly 28.04.23 – 26.05.23 Friday 08:30 – 13:30 tbc
    MET 931: Topics in Advanced Sampling Methods: Design and Causal Inference
    5 ECTS
    Lecturer(s)

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

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

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

    Course Content

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

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

    Learning outcomes:

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

    • Analytical Skills/Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools.
    • Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
    • Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses.
    • Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
    • Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
    • Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
    • Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.

    Form of assessment: Paper (technical report) (optional), Presentation (50 %), Class Participation (50 %)

     


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule
    Lecture
    Lecture 14.02.23 – 30.05.23 Tuesday 10:15 – 11:45
    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

    Participation, presentation, term paper (graded)

    Schedule
    Seminar
    16.02.23 – 01.06.23 Thursday 17:15 – 18:45 tbc
    PSY: Research in Clinical Psychology
    4 ECTS
    Lecturer(s)

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

    We invite CDSS doctoral candidates to discuss their research with experts in the field. The chair of Clinical Psychology and Biological Psychology and Psychotherapy pursues a wide range of topics and brings together a large spectrum of research approaches. We address open questions regarding each step of creative research and prolific publication of our scientific results. Each week we select one or two of our own projects for discussion.

    Schedule
    Seminar
    14.02.23 – 30.05.23 Tuesday 09:00 – 10:00 016–017 in L13, 15–17
    RES / IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: RES / IntRes
    Credits: 5
    Prerequisites

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll. As a necessary requirement you need to make a working paper draft available to all of us that you present in our ‘Mini Research Day’.

    Course Content

    This course will introduce students to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    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 the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in 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. Alternatively, you can also present a dataset or a methodology and highlight how students from other GESS centers might take advantage of it.

    2. Mini-Research-Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to other course participants. You will give a 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 (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – 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 student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. Each research team will also prepare a short 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 two weeks after the presentation. Moreover, you will also discuss another team project.

    Objectives

    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 received feedback from senior researchers from all three centers.
    • 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.

    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 the ‘Mini 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 a week 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 write-up.
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a prize.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to gess.registration 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, Political Science) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first served basis.

    Course dates

    • January 31st, 2023 – Course Registration Deadline
    • February 15th, 2023, 10:00 – 14:00 – Kick-Off Meeting
    • February 23th – Deadline to send paper to discussant (and in cc to: gess.office@uni-mannheim.de)
    • March 2nd, 2023, 10:00 – 19:00 – Mini Research Day
    • March 30th, 2023, exact time TBA – Science Speed Dating event
    • May 25, 2023, 10:00 – 19:00 – Presentation of Research Proposal
    • June 8th, 2023 – Deadline to hand in interdisciplinary research proposal (to: gess.office@uni-mannheim.de)
    Competences acquired

    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 received feedback from senior researchers from all three centers.
    • 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.

Register

Social Sciences Spring 2023