BE INSPIRED

"The Graduate School provides vast resources for doctoral students, including a wide range of courses conducive to training and thesis work." Dominic Ponattu, CDSS

Course Catalog

Spring 2019

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 and locations 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

Type
From
To
Weekday
From
To
Room
Material
Workshop
1st meeting
12.02.19
Tuesday
10:15
11:45
211 in B6, 30-32
Further dates: 22 Mar, 3 & 16 May
08.03.19
Friday
08:30
12:30
C 212 in A5, 6 entrance C

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Crafting Social Science Research, Theory Building and Causal Inference, English Academic Writing, Literature Review and CDSS Dissertation Proposal Workshop.

Participation is mandatory for second and third year CDSS Political Science students.
Participation is recommended for first year CDSS and visiting PhD students, as well as 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.


Course Content

The goal of this course is to provide support and crucial feedback for second and third year CDSS students 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.

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.02.19
29.05.19
Wednesday
12:00
13:30
B 317 in A5, 6 entrance B

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
14.02.19
23.05.19
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.02.20
28.05.20
Thursday
12:00
13:30
C 112 in A 5, 6 entrance C


Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.



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.



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.

The courses take place in Cologne and run from 1 to 23 August 2019. Detailed information about the summer school program is available on the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
02.08.18
24.08.18
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

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.

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).

The course will include a flipped classroom component with both synchronous and asynchronous learning. Students on both sites will individually prepare learning materials provided on an online course platform before attending class. Class lectures will then be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. The project work of groups will be facilitated through Canvas and Zoom.
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. Students will virtually attend the same class/lecture and then collaborate via online tools on their projects.

To successfully pass this course, students need to…

  • submit a question before the start of each class about the required readings for that week
  • actively participate in discussion during the meetings to demonstrate understanding of the required readings
  • provide periodic progress updates about their group project
  • schedule two online sessions with one of the instructors to review their group’s progress
  • work on a group project through the course and present their work orally and in writing.

Grading will solely be based on a final written project report provided by the group.

 


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

Type
From
To
Weekday
From
To
Room
Material
Seminar
not on 7 & 21 March
14.02.19
04.04.19
Thursday
15:30
17:10
C 212 in A5, 6 (entrance C)

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 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 develop an experimental design of her/his own or in collaboration with one other student of the seminar.

Course Requirements
Your evaluation in the course is based on the following:
1. Weekly response papers (Week 2- 12) (50%)
2. In-class presentation(s) of research design/article (20%)
3. Research Design as final paper (30%)

Each week each participant will be required to complete all the obligatory readings assigned in that week. Each student must submit a short response paper (one to two 2 paragraphs) on that week’s readings, including one or two discussion questions by 12pm on Monday before our class meeting.
Students are also expected to present one of the readings either alone or together with another participant in one of the sessions (short and concise 10-15 min presentation) and to email the instructor the presentation slides/materials by 12pm on Monday prior to the seminar.

In addition, students will be given the opportunity to design their own experiment, relevant to their dissertation research, which will be presented and discussed in class in the last session(s) of the seminar. This research design should also be the basis for the final paper, which should be written in the style of a scientific paper for a generalinterest sociology journal, such as American Journal of Sociology (AJS), American Sociological Review (ASR), European Sociological Review (ESR), Social Forces or Sociological Science. The paper should include the following components (1) Title and abstract, (2) an introduction/literature review, (3) a data and methods section, (4) a section shortly describing what results you would present and how (5) concluding section summarizing the expected findings, acknowledges limitation and identifies avenues for future work. 12-point font, 1.5 spaced and not exceed 12-15 pages, including references.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.19
29.05.19
Wednesday
12:00
13:30
A 102 in B6, 23-25 entrance A

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:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
13:45
15:15
A 103 in B6, 23-25, entrance A

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 318 in A 5, 6 entrance B
Tutorial
14.02.19
23.05.19
Thursday
12:00
13:30
C -108 in A 5, 6 entrance C

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. Some resources can be found here:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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 data-driven 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 and evaluate prediction models using the statistical programming language R.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
10:15
11:45
B 317 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Literature:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata. 3nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2017). Stata Multilevel Mixed-Effects. Reference Manual. Release 15. College Station, TX: Stata Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - 13 & 20 Feb / 6 & 13 Mar / 8, 15, and 22 May
13.02.19
Wednesday
13:45
17:00
A 302 in B6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:

  • * Generalized linear mixed models with lme4 (also known as hierarchical models)
  • Simple structural equation models
  • Basic set-up of Monte-Carlo simulations
  • Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

 
It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.

Course achievement - regular participation of the course

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
15.02.19
24.05.19
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Some experience with R, interest in text as data 


Course Content

The course “Quantitative Text Analysis” provides an introduction to the retrieval, preparation, visualization and analysis of text as data using R. We draw on social science and other text examples, namely European election manifestos, books by Mark Twain, massive amounts of Tweets and selected Wikipedia entries. The course covers some web scraping to obtain text, preparation including the construction of word frequency matrixes or dictionaries and visualization tools beyond word clouds. For the analysis of texts, topic models such as LDA (latent Dirichlet allocation), scaling models including Wordscores and Wordfish as well as alternatives based on natural language processing tools (e.g. word embeddings) are discussed. One further theme is the cross-lingual and -contextual analysis of text. The participants also have the unique opportunity of helping to shape a textbook on the topic, which is contracted with SAGE and scheduled to appear in 2020.

/Instructor/
Julian Bernauer is a Postdoctoral Fellow at the Data and Methods Unit of the MZES. He is currently working on a research project measuring populism from political text.

Assessment
Exercises, presentation of paper proposal, paper with text-as-data application (4000-5000 words)


Competences acquired

Abilities to obtain, describe and evaluate text as data, visualize textual information, classify and scale texts, all using R (as well as Python called from R) for these tasks


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
11.02.19
27.05.19
Monday
17:15
18:45
211 in B6, 30-32


Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

An Introduction to modern R, Statistical Modeling, and Mixed Models (Instructor: Henrik Singmann, Date: 15.01. and 16.01.2019, Location: Freiburg)

Introduction to Bayesian Inference: Core Principles and Application in Stan (Instructor: Daniel Heck, Date: 29.03., 10:00-18:00 & 30.03.2019, 09:00-17:00, Location: Mannheim)

Foundations II: Multinomial-Processing-Tree (MPT) Modeling (Instructors: Prof. Dr. Edgar Erdfelder und Dr. Daniel Heck, 02.05., 10:00-18:00 & 03.05.2019, 09:00-16:00, Location: Mannheim)

Hypothesis Evaluation Using the Bayes Factor (Instructor: Prof. Herbert Hoijtink, 16.05.2019, 11:00-18:00 & 17.05.2019, 09:00-15:00, Location: Mannheim)

IRT Modeling - Theory and Applications in R (Instructor: Thorsten Meiser, Date: 2 days in May / June 2019, Location: Mannheim)

Further details and registration.


Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Social network analysis is on the rise. Yet while social network analysis provides researchers with advanced and exciting tools to study social processes, it also involves considerable methodological challenges. This seminar introduces students to social network analysis, with the overarching aim of enabling them to understand when and how social network analysis can be used to advance our understanding of social phenomena.
In the first weeks, we will cover theoretical and methodological basics of social network analysis. Based on this knowledge, we then will approach methods of cross-sectional (ERGM) as well as longitudinal (SAOM) social network analysis. We will deepen our understanding of these methods by discussing exemplary empirical studies on network formation as well as social influence.
In the final weeks, participants will develop a network-related research idea in a field of their choice. They will elaborate on their idea in a conceptual/theoretical term paper that has to be submitted after the end of the seminar. To facilitate the development of the term papers, students will present and discuss each other’s ideas in the last weeks in class.

Requirements:
Weekly reading and preparation of materials; (Group) Presentation of a published empirical study; (Individual) Presentation of a network-related research proposal towards the end of the term; Submission of term paper (after the seminar ended)


Competences acquired

Participants will learn when, how, and why social network analysis helps to advance our understanding of social phenomena. This includes basic knowledge of different statistical methods and their promises and pitfalls.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
08:30
10:00
B 318 in A5, 6 (entrance B)


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

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

Course Readings:

  • 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.

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.


Competences acquired

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
07.03.19
Thursday
10:15
13:00
B 317, A 5, 6 entrance B
08.03.19
Friday
10:15
13:00
B 317, A 5, 6 entrance B
not on 21 March
14.03.19
23.05.19
Thursday
10:15
11:45
B 317, A 5, 6 entrance B
25.03.19
Monday
10:15
11:45
211 in B6, 30-32

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.

Regular class attendance is recommended, mandatory reading

Term paper (ca. 5000 words, plus references)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.02.19
27.05.19
10:15
11:45
B 244 in A5, 6 entrance B


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

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
13:45
15:15
B 243 in A5, 6 entrance B


Course Type: elective course

Course Number: POL

Credits: 8

Prerequisites

Familiarity with the basics of multivariate statistics and at least low level proficiency in R.


Course Content

This course offers an introduction into legislative politics with a particular emphasis on the link of theories and methods: what can we learn about legislators and their preferences when we looking at their behavior? Our key focus will be on roll call voting and ideal point estimation (which is a useful tool in other areas as well, for instance in International Politics or International Political Economy) using spatial voting theory. While we read some of the seminal literature in this field we will spend equal time and effort in the lab to become trained in programming and using different methods out there (NOMINATE, optimal classification, Bayesian methods). Computing is done in R and JAGS. As this is a research seminar, the course allows students to pursue areas of individual interest in more depth, and therefore the course content is to some extent determined based on the interests of the students.

Course achievement: Oral presentation of a literature review and active participation during the sessions.

Academic assessment: Essay of 9000 words or so.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.19
29.05.19
Wednesday
10:15
11:45
A305 in B6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

Public opinion plays an important role in European integration, e.g. by constraining elite decisions. Understanding European integration requires thus a proper understanding of the nature and origins of public support for European integration. This seminar will address key concepts and theories for the analysis of public support for European integration and its behavioral consequences. 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 and active participation during the sessions.

Term Paper (ca. 8.000 words)

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
12:00
13:30
B 317 in A5, 6 entrance B

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.

Required readings are indicated in the course schedule, which are based on seminal and current research on human rights, peace and security. Each session requires a significant amount of reading. Focus on the key arguments. You are not expected to know the details of all readings, or specific empirical strategies, results or facts. The seminar sessions will help you identify priorities. The specific topics and readings may change based on the interests of the class.

Requirements:

  • 1x leading class discussion.
  • 1x review of research proposal.

Assessment: Research paper

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
13:45
15:15
211 in B6, 30-32


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The course of European integration has become the subject of heated political debate. The failed EU constitutional treaty in 2005, the divided reactions to the economic and refugee crises across and within member states over the past decade, and, ultimately, the EU exit vote in the UK referendum in 2016 all reflect the end of the era of permissive consensus. This course examines the impact of the EU’s politicization on electoral behavior at domestic and European elections, the positions of mainstream and fringe political parties and the responsiveness of national governments and EU institution to public attitudes towards specific EU policies, integration steps or the overall EU regime.

The full syllabus will be circulated during the first class. Readings will be available through the ILIAS or the Semesteraparat in the A5 university library.

Recommended reading:

Hobolt, S. and de Vries, C. E. (2016) ‘Public Support for European Integration’, Annual Review of Political Science 19: 413-32.

Term paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
not May 9
14.02.19
23.05.19
Thursday
13:45
15:15
B 318 in A5, 6 entrance B


Course Type: elective course

Course Number: POL

Course Content

To openly express one’s views is the most fundamental civil liberty. While this basic right is under constant and serious threat in authoritarian contexts, the question of how free speech should be regulated is also a concern in liberal democracies (as exemplified by debates over “political correctness” and “hate speech”). Is particularly, important in globalized conditions of cultural diversity and the unprecedented levels of communication facilitated by the digital revolution. The aim of this course is to discuss contemporary scholarly research on the politics of free speech and censorship. Why is free expression so important, what are citizens’ opinions and preferences over these issues, why and how do states actually regulate free speech and what are the effects of this regulation? How does cultural diversity and digital communication impact on these questions? Next to substantive discussion the course will place great emphasis on the practice of quantitative political research and provide ample space for students’ projects.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
21.02.19
23.05.19
Thursday
12:00
13:30
A 102 in B6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for 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'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and 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.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at 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.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, 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 served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

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 and locations 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

Type
From
To
Weekday
From
To
Room
Material
Workshop
1st meeting
12.02.19
Tuesday
10:15
11:45
211 in B6, 30-32
Further dates: 22 Mar, 3 & 16 May
08.03.19
Friday
08:30
12:30
C 212 in A5, 6 entrance C


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.



Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Crafting Social Science Research, Theory Building and Causal Inference, English Academic Writing, Literature Review and CDSS Dissertation Proposal Workshop.

Participation is mandatory for second and third year CDSS Psychology students.


Course Content

Recent and ongoing psychological and neuropsychological research projects are discussed, including possible research plans, frameworks for data analysis, and interpretation of results.

Literature: References will be given during the course.

Course material will be provided in ILIAS.


Competences acquired

Improvement in research skills and communication of research results.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
11.02.19
27.05.19
Monday
15:30
17:00
EO 259

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
14.02.19
23.05.19
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.02.20
28.05.20
Thursday
12:00
13:30
C 112 in A 5, 6 entrance C

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

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.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.05.19
Friday
10:00
18:00
Landau
11.05.19
Saturday
10:00
18:00
Landau
07.06.19
Friday
10:00
18:00
Landau


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.

The courses take place in Cologne and run from 1 to 23 August 2019. Detailed information about the summer school program is available on the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
02.08.18
24.08.18
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

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.

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).

The course will include a flipped classroom component with both synchronous and asynchronous learning. Students on both sites will individually prepare learning materials provided on an online course platform before attending class. Class lectures will then be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. The project work of groups will be facilitated through Canvas and Zoom.
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. Students will virtually attend the same class/lecture and then collaborate via online tools on their projects.

To successfully pass this course, students need to…

  • submit a question before the start of each class about the required readings for that week
  • actively participate in discussion during the meetings to demonstrate understanding of the required readings
  • provide periodic progress updates about their group project
  • schedule two online sessions with one of the instructors to review their group’s progress
  • work on a group project through the course and present their work orally and in writing.

Grading will solely be based on a final written project report provided by the group.

 


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

Type
From
To
Weekday
From
To
Room
Material
Seminar
not on 7 & 21 March
14.02.19
04.04.19
Thursday
15:30
17:10
C 212 in A5, 6 (entrance C)

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 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 develop an experimental design of her/his own or in collaboration with one other student of the seminar.

Course Requirements
Your evaluation in the course is based on the following:
1. Weekly response papers (Week 2- 12) (50%)
2. In-class presentation(s) of research design/article (20%)
3. Research Design as final paper (30%)

Each week each participant will be required to complete all the obligatory readings assigned in that week. Each student must submit a short response paper (one to two 2 paragraphs) on that week’s readings, including one or two discussion questions by 12pm on Monday before our class meeting.
Students are also expected to present one of the readings either alone or together with another participant in one of the sessions (short and concise 10-15 min presentation) and to email the instructor the presentation slides/materials by 12pm on Monday prior to the seminar.

In addition, students will be given the opportunity to design their own experiment, relevant to their dissertation research, which will be presented and discussed in class in the last session(s) of the seminar. This research design should also be the basis for the final paper, which should be written in the style of a scientific paper for a generalinterest sociology journal, such as American Journal of Sociology (AJS), American Sociological Review (ASR), European Sociological Review (ESR), Social Forces or Sociological Science. The paper should include the following components (1) Title and abstract, (2) an introduction/literature review, (3) a data and methods section, (4) a section shortly describing what results you would present and how (5) concluding section summarizing the expected findings, acknowledges limitation and identifies avenues for future work. 12-point font, 1.5 spaced and not exceed 12-15 pages, including references.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.19
29.05.19
Wednesday
12:00
13:30
A 102 in B6, 23-25 entrance A

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:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
13:45
15:15
A 103 in B6, 23-25, entrance A

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 318 in A 5, 6 entrance B
Tutorial
14.02.19
23.05.19
Thursday
12:00
13:30
C -108 in A 5, 6 entrance C

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. Some resources can be found here:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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 data-driven 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 and evaluate prediction models using the statistical programming language R.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
10:15
11:45
B 317 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Literature:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata. 3nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2017). Stata Multilevel Mixed-Effects. Reference Manual. Release 15. College Station, TX: Stata Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - 13 & 20 Feb / 6 & 13 Mar / 8, 15, and 22 May
13.02.19
Wednesday
13:45
17:00
A 302 in B6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:

  • * Generalized linear mixed models with lme4 (also known as hierarchical models)
  • Simple structural equation models
  • Basic set-up of Monte-Carlo simulations
  • Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

 
It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.

Course achievement - regular participation of the course

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
15.02.19
24.05.19
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Some experience with R, interest in text as data 


Course Content

The course “Quantitative Text Analysis” provides an introduction to the retrieval, preparation, visualization and analysis of text as data using R. We draw on social science and other text examples, namely European election manifestos, books by Mark Twain, massive amounts of Tweets and selected Wikipedia entries. The course covers some web scraping to obtain text, preparation including the construction of word frequency matrixes or dictionaries and visualization tools beyond word clouds. For the analysis of texts, topic models such as LDA (latent Dirichlet allocation), scaling models including Wordscores and Wordfish as well as alternatives based on natural language processing tools (e.g. word embeddings) are discussed. One further theme is the cross-lingual and -contextual analysis of text. The participants also have the unique opportunity of helping to shape a textbook on the topic, which is contracted with SAGE and scheduled to appear in 2020.

/Instructor/
Julian Bernauer is a Postdoctoral Fellow at the Data and Methods Unit of the MZES. He is currently working on a research project measuring populism from political text.

Assessment
Exercises, presentation of paper proposal, paper with text-as-data application (4000-5000 words)


Competences acquired

Abilities to obtain, describe and evaluate text as data, visualize textual information, classify and scale texts, all using R (as well as Python called from R) for these tasks


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
11.02.19
27.05.19
Monday
17:15
18:45
211 in B6, 30-32


Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

An Introduction to modern R, Statistical Modeling, and Mixed Models (Instructor: Henrik Singmann, Date: 15.01. and 16.01.2019, Location: Freiburg)

Introduction to Bayesian Inference: Core Principles and Application in Stan (Instructor: Daniel Heck, Date: 29.03., 10:00-18:00 & 30.03.2019, 09:00-17:00, Location: Mannheim)

Foundations II: Multinomial-Processing-Tree (MPT) Modeling (Instructors: Prof. Dr. Edgar Erdfelder und Dr. Daniel Heck, 02.05., 10:00-18:00 & 03.05.2019, 09:00-16:00, Location: Mannheim)

Hypothesis Evaluation Using the Bayes Factor (Instructor: Prof. Herbert Hoijtink, 16.05.2019, 11:00-18:00 & 17.05.2019, 09:00-15:00, Location: Mannheim)

IRT Modeling - Theory and Applications in R (Instructor: Thorsten Meiser, Date: 2 days in May / June 2019, Location: Mannheim)

Further details and registration.


Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Social network analysis is on the rise. Yet while social network analysis provides researchers with advanced and exciting tools to study social processes, it also involves considerable methodological challenges. This seminar introduces students to social network analysis, with the overarching aim of enabling them to understand when and how social network analysis can be used to advance our understanding of social phenomena.
In the first weeks, we will cover theoretical and methodological basics of social network analysis. Based on this knowledge, we then will approach methods of cross-sectional (ERGM) as well as longitudinal (SAOM) social network analysis. We will deepen our understanding of these methods by discussing exemplary empirical studies on network formation as well as social influence.
In the final weeks, participants will develop a network-related research idea in a field of their choice. They will elaborate on their idea in a conceptual/theoretical term paper that has to be submitted after the end of the seminar. To facilitate the development of the term papers, students will present and discuss each other’s ideas in the last weeks in class.

Requirements:
Weekly reading and preparation of materials; (Group) Presentation of a published empirical study; (Individual) Presentation of a network-related research proposal towards the end of the term; Submission of term paper (after the seminar ended)


Competences acquired

Participants will learn when, how, and why social network analysis helps to advance our understanding of social phenomena. This includes basic knowledge of different statistical methods and their promises and pitfalls.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
08:30
10:00
B 318 in A5, 6 (entrance B)


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

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

Course Readings:

  • 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.

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.


Competences acquired

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
07.03.19
Thursday
10:15
13:00
B 317, A 5, 6 entrance B
08.03.19
Friday
10:15
13:00
B 317, A 5, 6 entrance B
not on 21 March
14.03.19
23.05.19
Thursday
10:15
11:45
B 317, A 5, 6 entrance B
25.03.19
Monday
10:15
11:45
211 in B6, 30-32

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

Reading relevant literature
Short oral presentation
Group work

Written report (Developing study design)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
17:15
18:45
EO 256 Schloss Ehrenhof Ost
19.03.19
Tuesday
19:00
20:30
EO 157 Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for 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'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and 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.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at 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.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, 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 served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas


Course Type: core course

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Colloquium
Keusch/Kreuter
11.02.19
27.05.19
Monday
15:30
17:00
tbc
Kalter/Kogan/Möhring (Lars Leszczensky)
12.02.19
28.05.19
Tuesday
15:30
17:00
tbc
Kalter/Kogan/Möhring (Katja Möhring)
12.02.19
28.05.19
Tuesday
10:15
11:45
tbd
Gautschi/Hillmann
13.02.19
29.05.19
Wednesday
17:15
18:45
tbc

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 and locations 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

Type
From
To
Weekday
From
To
Room
Material
Workshop
1st meeting
12.02.19
Tuesday
10:15
11:45
211 in B6, 30-32
Further dates: 22 Mar, 3 & 16 May
08.03.19
Friday
08:30
12:30
C 212 in A5, 6 entrance C

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Crafting Social Science Research, Theory Building and Causal Inference, English Academic Writing, Literature Review and CDSS Dissertation Proposal Workshop.

Participation is mandatory for second and third year CDSS Sociology students.


Course Content

The goal of this course is to provide support and crucial feedback for second and third year CDSS PhD 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

Type
From
To
Weekday
From
To
Room
Material
Workshop
12.02.19
Tuesday
15:00
16:00
tbd
08.05.19
Wednesday
09:30
12:00
tbd
22.05.19
Wednesday
09:30
12:00
tbd

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
14.02.19
23.05.19
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.02.20
28.05.20
Thursday
12:00
13:30
C 112 in A 5, 6 entrance C


Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.



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.

The courses take place in Cologne and run from 1 to 23 August 2019. Detailed information about the summer school program is available on the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
02.08.18
24.08.18
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

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.

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).

The course will include a flipped classroom component with both synchronous and asynchronous learning. Students on both sites will individually prepare learning materials provided on an online course platform before attending class. Class lectures will then be hosted by instructors on both partner sites simultaneously with one partner taking the lead on each lecture. The lectures will be video mediated. The project work of groups will be facilitated through Canvas and Zoom.
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. Students will virtually attend the same class/lecture and then collaborate via online tools on their projects.

To successfully pass this course, students need to…

  • submit a question before the start of each class about the required readings for that week
  • actively participate in discussion during the meetings to demonstrate understanding of the required readings
  • provide periodic progress updates about their group project
  • schedule two online sessions with one of the instructors to review their group’s progress
  • work on a group project through the course and present their work orally and in writing.

Grading will solely be based on a final written project report provided by the group.

 


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

Type
From
To
Weekday
From
To
Room
Material
Seminar
not on 7 & 21 March
14.02.19
04.04.19
Thursday
15:30
17:10
C 212 in A5, 6 (entrance C)

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 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 develop an experimental design of her/his own or in collaboration with one other student of the seminar.

Course Requirements
Your evaluation in the course is based on the following:
1. Weekly response papers (Week 2- 12) (50%)
2. In-class presentation(s) of research design/article (20%)
3. Research Design as final paper (30%)

Each week each participant will be required to complete all the obligatory readings assigned in that week. Each student must submit a short response paper (one to two 2 paragraphs) on that week’s readings, including one or two discussion questions by 12pm on Monday before our class meeting.
Students are also expected to present one of the readings either alone or together with another participant in one of the sessions (short and concise 10-15 min presentation) and to email the instructor the presentation slides/materials by 12pm on Monday prior to the seminar.

In addition, students will be given the opportunity to design their own experiment, relevant to their dissertation research, which will be presented and discussed in class in the last session(s) of the seminar. This research design should also be the basis for the final paper, which should be written in the style of a scientific paper for a generalinterest sociology journal, such as American Journal of Sociology (AJS), American Sociological Review (ASR), European Sociological Review (ESR), Social Forces or Sociological Science. The paper should include the following components (1) Title and abstract, (2) an introduction/literature review, (3) a data and methods section, (4) a section shortly describing what results you would present and how (5) concluding section summarizing the expected findings, acknowledges limitation and identifies avenues for future work. 12-point font, 1.5 spaced and not exceed 12-15 pages, including references.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.19
29.05.19
Wednesday
12:00
13:30
A 102 in B6, 23-25 entrance A

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:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
13:45
15:15
A 103 in B6, 23-25, entrance A

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 318 in A 5, 6 entrance B
Tutorial
14.02.19
23.05.19
Thursday
12:00
13:30
C -108 in A 5, 6 entrance C

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. Some resources can be found here:

www.rstudio.com/online-learning/
cran.r-project.org/manuals.html
www.statmethods.net


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 data-driven 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 and evaluate prediction models using the statistical programming language R.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
10:15
11:45
B 317 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Literature:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Skrondal, A. (2012). Multilevel and Longitudinal Modeling Using Stata. 3nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2017). Stata Multilevel Mixed-Effects. Reference Manual. Release 15. College Station, TX: Stata Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - 13 & 20 Feb / 6 & 13 Mar / 8, 15, and 22 May
13.02.19
Wednesday
13:45
17:00
A 302 in B6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:

  • * Generalized linear mixed models with lme4 (also known as hierarchical models)
  • Simple structural equation models
  • Basic set-up of Monte-Carlo simulations
  • Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

 
It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.

Course achievement - regular participation of the course

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
15.02.19
24.05.19
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Some experience with R, interest in text as data 


Course Content

The course “Quantitative Text Analysis” provides an introduction to the retrieval, preparation, visualization and analysis of text as data using R. We draw on social science and other text examples, namely European election manifestos, books by Mark Twain, massive amounts of Tweets and selected Wikipedia entries. The course covers some web scraping to obtain text, preparation including the construction of word frequency matrixes or dictionaries and visualization tools beyond word clouds. For the analysis of texts, topic models such as LDA (latent Dirichlet allocation), scaling models including Wordscores and Wordfish as well as alternatives based on natural language processing tools (e.g. word embeddings) are discussed. One further theme is the cross-lingual and -contextual analysis of text. The participants also have the unique opportunity of helping to shape a textbook on the topic, which is contracted with SAGE and scheduled to appear in 2020.

/Instructor/
Julian Bernauer is a Postdoctoral Fellow at the Data and Methods Unit of the MZES. He is currently working on a research project measuring populism from political text.

Assessment
Exercises, presentation of paper proposal, paper with text-as-data application (4000-5000 words)


Competences acquired

Abilities to obtain, describe and evaluate text as data, visualize textual information, classify and scale texts, all using R (as well as Python called from R) for these tasks


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
11.02.19
27.05.19
Monday
17:15
18:45
211 in B6, 30-32


Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

An Introduction to modern R, Statistical Modeling, and Mixed Models (Instructor: Henrik Singmann, Date: 15.01. and 16.01.2019, Location: Freiburg)

Introduction to Bayesian Inference: Core Principles and Application in Stan (Instructor: Daniel Heck, Date: 29.03., 10:00-18:00 & 30.03.2019, 09:00-17:00, Location: Mannheim)

Foundations II: Multinomial-Processing-Tree (MPT) Modeling (Instructors: Prof. Dr. Edgar Erdfelder und Dr. Daniel Heck, 02.05., 10:00-18:00 & 03.05.2019, 09:00-16:00, Location: Mannheim)

Hypothesis Evaluation Using the Bayes Factor (Instructor: Prof. Herbert Hoijtink, 16.05.2019, 11:00-18:00 & 17.05.2019, 09:00-15:00, Location: Mannheim)

IRT Modeling - Theory and Applications in R (Instructor: Thorsten Meiser, Date: 2 days in May / June 2019, Location: Mannheim)

Further details and registration.


Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Social network analysis is on the rise. Yet while social network analysis provides researchers with advanced and exciting tools to study social processes, it also involves considerable methodological challenges. This seminar introduces students to social network analysis, with the overarching aim of enabling them to understand when and how social network analysis can be used to advance our understanding of social phenomena.
In the first weeks, we will cover theoretical and methodological basics of social network analysis. Based on this knowledge, we then will approach methods of cross-sectional (ERGM) as well as longitudinal (SAOM) social network analysis. We will deepen our understanding of these methods by discussing exemplary empirical studies on network formation as well as social influence.
In the final weeks, participants will develop a network-related research idea in a field of their choice. They will elaborate on their idea in a conceptual/theoretical term paper that has to be submitted after the end of the seminar. To facilitate the development of the term papers, students will present and discuss each other’s ideas in the last weeks in class.

Requirements:
Weekly reading and preparation of materials; (Group) Presentation of a published empirical study; (Individual) Presentation of a network-related research proposal towards the end of the term; Submission of term paper (after the seminar ended)


Competences acquired

Participants will learn when, how, and why social network analysis helps to advance our understanding of social phenomena. This includes basic knowledge of different statistical methods and their promises and pitfalls.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.19
23.05.19
Thursday
08:30
10:00
B 318 in A5, 6 (entrance B)


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

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

Course Readings:

  • 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.

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.


Competences acquired

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
07.03.19
Thursday
10:15
13:00
B 317, A 5, 6 entrance B
08.03.19
Friday
10:15
13:00
B 317, A 5, 6 entrance B
not on 21 March
14.03.19
23.05.19
Thursday
10:15
11:45
B 317, A 5, 6 entrance B
25.03.19
Monday
10:15
11:45
211 in B6, 30-32

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for 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'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and 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.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at 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.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, 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 served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

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 organizational behavior and development. We will consider a variety of research questions that tap into both formal and informal ways of organizing: what kinds of institutions are necessary to make economic organization work? Where do such institutions come from? Why do we observe very different outcomes across contexts even though they share the same market-supporting institutions? Why do some organizations survive even though they face the most unfavorable environments? How do conditions at the time of an organization's birth shape its development? To address these and further questions, we will rely both on recent theoretical advances and on empirical studies in a various settings.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.19
28.05.19
Tuesday
12:00
13:30
B 143 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

This seminar deals with identity, religion, and intergroup relations. Broadly speaking, we will start by focusing on mechanisms that drive the development of ethnic, national, and religious identities and end by examining the ways in which these identities affect intergroup attitudes and behavior.
In the first half of the seminar, we will introduce the concept of social identity and its theoretical foundations and implications. We will discuss the development and peculiarities of ethnic, national, and religious identities. Reading both conceptual and empirical papers, we are particularly interested in how identity development is shaped by intergroup relations. Considering the perspective of both minority and majority group members, we will examine how minority members react to perceived discrimination as well as what attitudes majority group members hold towards members of different minority groups.
In the second half of the seminar, we will ask how identities affect intergroup relations and discuss empirical studies on intergroup attitudes and friendship. In the final weeks, participants will develop an own research idea that will result in a term paper. The term paper has to be submitted after the end of the seminar, and it can be either an empirical study or a theoretical elaboration. To facilitate the writing of the term papers, students will present and discuss each other’s ideas in the last weeks in class.

Requirements:
Weekly reading and preparation of materials; (Group) Presentation of an empirical study; ) (Individual) Presentation of the planned term paper towards the end of the term; Submission of term paper (after the seminar ended)


Competences acquired

Participants will acquire knowledge of key research questions, theories, and findings with regard to identity and intergroup relations. At the end of the seminar, they should be able to formulate and pursue a related research question.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.19
29.05.19
Wednesday
10:15
11:45
A103 in B6, 23-25

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

During the seminar the students will have an opportunity to address several controversies found in migration and integration literature. One is related to immigrants’ (self-)selection. Are immigrants positively or negatively selected towards succeeding in the host country? Does selection of immigrants matter for their integration outcomes as well as integration prospects of their offspring? The second refers to immigrants’ aspirations. Do immigrants always have higher aspirations regarding educational and occupational attainment? The third concerns the link between immigrants’ education and labour market outcomes. Do highly-educated immigrants have better labour market prospects than lower-educated? In the end of the seminar, the participants are expected to select yet another controversy and discuss it either in a theoretical or empirical seminar paper.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
11.02.19
27.05.19
Monday
12:00
13:30
309 in B6, 30-32

Register

Social Sciences Spring 2019

Dissertation Tutorial: Sociology (Gautschi&Hillmann / Keusch&Kreuter / Kogan, Kalter & Möhring)
DIS
Dissertation Proposal Workshop
RES
CDSS Workshop: Sociology
RES
English Academic Writing
RES
English Academic Writing
RES
MZES A Colloquium "European Societies and their Integration"
MET
8th GESIS Summer School in Survey Methodology
MET
Big Data in Immigration Research - registration must be made via the lecturer and the CDSS
MET
Experimental Methods in Sociology and the Social Sciences
MET
Fundamentals of Computing and Data Display
MET
Longitudinal Data Analysis (Lecture + Lab Course)
MET
Machine Learning for Social Science
MET
Multilevel Modeling
MET
Programming in R
MET
Quantitative Text Analysis with R
MET
SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
MET
Social Network Analysis
MET/POL
Advanced Quantitative Methods
RES
Interdisciplinary Work in Economics and Social Sciences (Bridge Course)
SOC
Advanced Topics in Organizational Theory
SOC
Identity, Religion, and Intergroup Relations
SOC
Migration & Integration: Controversies of Migration and Integration Sociology
Dissertation Tutorial: Political Science
RES
CDSS Workshop: Political Science
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
POL
Comparative Political Behavior
POL
International Political Economy
POL
Selected Topics in Comparative Politics: Roll call voting. Theory, Methods and Applications
POL
Selected Topics in Comparative Politics: Understanding Public Opinion on European Integration
POL
Selected Topics in International Politics: Repression, security and peace
POL
Selected Topics in International Politics: The Politicization of European Integration
POL
The Politics of Free Speech and Censorship
RES
AC3/BC4: Colloquia II
RES
CDSS Workshop: Research in Psychology
RES
English Academic Writing for Psychologists (for CDSS students only)
MET/PSY
Psychological interventions using diary designs