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 2018


Course Type: core course

Course Content

Doctoral theses supervised by Thomas Gautschi, Henning Hillmann, Frank Kalter, Florian Keusch, Irena Kogan, Frauke Kreuter, and Alexander Schmidt-Catran respectively, will be discussed.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Colloquium
Keusch/Kreuter
12.02.18
28.05.18
Monday
12:00
13:30
tbc
Kalter/Kogan/Schmidt-Catran
13.02.18
29.05.18
Tuesday
15:30
17:00
tbc
Gautschi/Hillmann
14.02.18
30.05.18
Wednesday
17:15
18:45
tbc

Lecturer(s)


Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

CSSR, 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
13.02.18
Tuesday
10:15
11:45
12.03.18
19.03.18
Monday
08:30
12:30
C 212 in A5, 6 entrance C
30.04.18
07.05.18
Monday
08:30
12:30
C 212 in A5, 6 entrance C


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, EAW, Literature Review, Dissertation Proposal


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
05.03.18
Monday
11:00
13:00
tbd
30.05.18
Wednesday
11:00
14:00

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
15.02.18
31.05.18
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B


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 Content

Bring your (methods) problems. Thursdays between 10-11am, please make an appointment by sending an e-mail to rtraunmu@mail.uni-mannheim.de.



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. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to 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 world has changed for empirical social scientists. The new types of “big data” have generated an entire new research field—that of data science. That world is dominated by computer scientists who have generated new ways of creating and collecting data, developed new analytical and statistical techniques, and provided new ways of visualizing and presenting information. These new sources of data and techniques have the potential to transform the way applied social science is done. Research has certainly changed. Researchers draw on data that are “found” rather than “made” by federal agencies; those publishing in leading academic journals are much less likely today to draw on preprocessed survey data. And the jobs have changed. The new job title of “data scientist” is highlighted in job advertisements on CareerBuilder.com and Burning-glass.com—in the same category as statisticians, economists, and other quantitative social scientists if starting salaries are useful indicators.

The goal of this course is to provide social scientists with an understanding of the key elements of this new science, its value, and the opportunities for doing better work. The goal is also to identify the many ways in which the analytical toolkits possessed by social scientists can be brought to bear to enhance the generalizability of the work done by computer scientists.

We meet four-five times for an extended period of time in this seminar. In the first sessions we will introduce new data sources and tools to tackle them. We will also discuss extensively what research questions can be answered with which data source. In the month following we expect participants to engage in their own data collection and present and critically discuss their results in the following meetings. We advise all students to set aside time in those weeks, to fully work on the assigned projects.

Literature

We will use the following books and require familiarity with the books in preparation for the course.

  • Foster, I., Ghani, R., Jarmin R.S., Kreuter F., Lane J. (Eds.) (2016): Big Data and Social Science: A Practical Guide to Methods and Tools.
  • Chapman and Hall/CRC Press.Salganik, M. (2017): Bit by Bit. Social Research in the Digital Age. Princeton University
    Online version: http://www.bitbybitbook.com/en/preface/
  • Stephens-Davidowitz, S. (2017). Everybody lies: Big Data, New Data, and What the internet can tell us about who we really are. Dey Street Books.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.18
Wednesday
15:30
17:00
310 in B 6, 30-32 entrance E-F
15.03.18
16.03.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A
12.04.18
13.04.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

 


Course Content

This course continues where last semester’s course Introduction to Bayesian Statistics for Social Scientists I left off. But newcomers are welcome. We will extend our Bayesian tool kit by adding Bayesian models of ordered and un-ordered categorical outcomes, Bayesian measurement models, and Bayesian Non-parametric methods. We will also spend some time to further discuss Prior specifications and issues of Bayesian model checking, selection and averaging.

Recommended Reading

  • Gill, J. (2015). Bayesian Methods. A Social and Behavioral Sciences Approach (3rd Edition). CRC Press.
  • Jackman, S. (2009). Bayesian Analysis for the Social Sciences. Wiley.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
15.02.18
22.03.18
Thursday
13:45
17:00
211
16.02.18
23.03.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Not taking place on 24.04 and 29.05.2018
13.02.18
22.05.18
Tuesday
13:45
15:15
A 103 in B 6, 23-25 entrance A
15.05.18
22.05.18
Tuesday
15:30
17:00
A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.04.18
Friday
10:15
17:00
A 102 in B6, 23-25 entrance A
20.04.18
Friday
10:15
18:45
A 102 in B6, 23-25 entrance A
04.05.18
Friday
10:15
15:15
A 102 in B6, 23-25 entrance A

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 - 14, 21, and 28 Feb / 14 Mar / 11, 18, and 25 Apr
14.02.18
25.04.18
Wednesday
13:45
17:00
B 318 in A5, 6 entrance B

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
23.02.18
01.06.18
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Registration for this course is no longer possible.


Course Content

This course offers an introduction to the Python programming language with a focus on solving social science problems. The course is structured around a series of practical exercises for working with basic Python functionalities, reading structured and “unstructured” datasets into Python, using statistical and network analytic methods and designing visualizations. The course demonstrates the utility of working with Python in two ways. We are going to replicate quantitative analyses from publications in social science journals and we will take advantage of Python’s compatibility with application programming interfaces (APIs) in order to explore several new data sources (e.g., Twitter, Wikipedia, Meetup, Github, Discogs). The replication exercises require some mandatory reading and supplementary materials will be suggested. The main focus of the course will concentrate on practicing Python programming and discussing when and how it might benefit social science research. We will also take time to discuss the applicability of Python to ongoing research interests among the participants. No prior programming experience is required.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.02.18
Thursday
08:30
11:45
C -108 in A 5, 6 entrance C
09.02.18
Friday
08:30
17:00
C -108 in A 5, 6 entrance C
15.02.18
Thursday
08:30
17:00
C 109 PC Pool in A 5, 6 entrance C


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

All statistical models are false. Instead of searching for the “best” or the “true” model specification, the logic of robustness testing is to accept model uncertainty and to study how model estimates and inferences react to changes in model assumptions. After looking at classical contributions (Leamer, Rosenbaum, Manski, and Frank), we will discuss in greater detail the new robustness framework proposed by Neumayer and Plümper. This covers robustness testing problems of measurement uncertainty, omitted variables, functional form, causal and temporal heterogeneity, dynamics and spatial dependence. This course will rely heavily on students’ presentations of specific robustness tests. 

Recommended Reading:

Neumayer, E. & Plümper, T. (2017). Robustness Tests for Quantitative Research. Cambridge University Press.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.04.18
01.06.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The first law of geography states that “everything is related to everything else, but near things are more related than distant things” (Tobler 1970: 236). In the Social Sciences, geographic data and spatial analyses offer rich insights into a variety of relevant research questions (Franzese and Hays 2008). The course covers crucial concepts involved in spatial analysis, introduces a toolbox of statistical models and pays particular attention to the accessible implementation of spatial analysis in free software (working with R and packages for spatial analysis). This implies that participants should bring their Laptop with R (required) and RStudio (recommended) installed.

Parts

I.                   Concepts

The first part of the course deals with the concepts involved in spatial analysis. We will spend some time getting to know “W”, the connectivity matrix which defines spatial dependencies (Neumayer and Plümper 2016). Alternative conceptions of neighborhood and the weighting of connections are discussed. For example, spatial proximity does not necessarily imply geographic proximity, as for instance trade or information exchange can bring distance things closely together. Further concepts handled include the geo-referencing of data, regarding both its use and generation.

II.                Models

To test hypotheses based on spatial data, tailored statistical tools are needed. The second part of the course is dedicated to spatial correlation coefficients (such as Moran’s I), varieties of spatial regression models (variants of spatial lags; categorical, count and duration specifications), spatio-temporal models as well as extensions to the multilevel case. The options offered by a Bayesian approach to spatial data analysis are also discussed.

III.             Implementation

As mentioned, one focus of the course is the accessible implementation of spatial analysis. To this end, the free statistical software R is used (https://www.r-project.org/). Its advantages in addition to the open source character are the provision of user-written packages, including several on geodata and spatial analysis (such as sp, maptools or spdep) as well as powerful graphical capabilities (see Bivand et al. 2013). Recommended is the combination of R with the RStudio editor/environment (https://www.rstudio.com/). For the Bayesian models, we rely on JAGS (http://mcmc-jags.sourceforge.net/) or Stan (http://mc-stan.org/). During this part of the course, geographic data are handled, autocorrelations and spatial regression models are estimated, and everything is thoroughly visualized.

IV.             Applications

The last part of the course is devoted to the participants’ own applications in the field of geographic data and spatial analysis. Anticipated throughout the course, they will look for and handle their own geographic data and we will jointly identify adequate spatial models to test hypotheses. The final meeting is dedicated to the presentation and discussion of the paper outlines, with a focus on a (preliminary) spatial analysis of geographic data.

Assessment

Active participation, exercises, presentation with preliminary analysis, paper on spatial analysis of geographic data (4000-5000 words)

References

Bivand, R. S., E. Pebesma and V. Gómez-Rubio (2013): Applied Spatial Data Analysis with R (2nd Edition). New York: Springer.

Franzese, R. J. and J. C. Hays (2008): Interdependence in Comparative Politics. Substance, Theory, Empirics, Substance. Comparative Political Studies 41(4/5): 742-780.

Neumayer, E. and T. Plümper (2016): W. Political Science Research and Methods 4(1): 175-193.

Tobler, W. R. (1970): A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46(Suppl.): 234-340.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
irregular dates - 20 & 27 Feb, 06 & 20 Mar, 24 Apr
20.02.18
Tuesday
08:30
11:45
212/13 in B6, 30-32


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Survey experiments are an increasingly popular tool in the social sciences. The attractiveness of this hybrid methodology stems from the fact that it combines the internal validity of experimental research with the external validity of survey sampling. After a brief review of the key concepts and benefits of these two foundations, the course will discuss both the design and the analysis of modern survey experiments. In particular, we will take an in-depth look into the design and analysis of factorial surveys/conjoint experiments for complex judgments and of list experiments for sensitive questions. Some time will also be spent on discussing the pro and cons of online surveys and crowd-sourced experiments.

Assessment: Take home exam or paper

Recommended Reading: Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton University Press.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
12.04.18
31.05.18
Thursday
13:45
17:00
211


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

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 “Multivariate Analyses” in the M.A. program 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
14.02.18
30.05.18
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
15.02.18
24.05.18
Thursday
10:15
11:45
B 317, A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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.
Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Course requirements

Reading relevant literature
Short oral presentation
Groupwork

Written report (Developing study design)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
15.02.18
24.05.18
Thursday
12:00
13:30
EO 256 Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

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 and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers with matching research interests. Within these panels, the students should talk about their research interests 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 and discussion at GESS Research Day
  • Three pages referee report
  • Participation in discussion panel during the Research Day
  • Three pages individual reflexion of the Research Day
  • Ongoing meetings with discussion panel group
  • Participation at Science Speed Dating event
  • Five pages interdisciplinary research proposal (group of two students)
  • Presentation of research proposal
  • Only pass/fail

 

If you are interested please register until January31st,2018, by sending a title and an extended abstract of your research project you would like to present to your respective center representative. Please indicate in your e-mail your fields of interest and if you have any, mention fields you would like to collaborate with.

 

Course dates:

-          Research Day: March 5th, 2018

-          Speed Dating: May 15th, 2018

-          Presentation of research proposal: tbd, around mid-June


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

tbd

Course requirements

(1) Required readings

(2) Three written reaction memos

(3) Lead class discussion at least once


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.18
28.05.18
Monday
12:00
13:30
B 318 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Prerequisites

Knowledge of Stata is helpful but not required.


Course Content

The number of foreign-born persons in Western Europe increased dramatically over the past three decades. Also in Eastern European countries, immigrants, particularly refugees, are increasing in numbers for the first time in recent history. This produces many social outcomes that link to trust, voting, inequality and the role of the state. For example, having more foreigners in a country or region may reduce trust and increase intergroup conflicts. In Germany, for the first time since WWII, an anti-immigrant, populist party gained substantial political power. Also, many immigrants, in particular refugees or family reunification immigrants, face substantial labor market disadvantage and poverty. Many states were not equipped to deal with immigration and refugee asylum seeking on the scale that developed in recent history. All of these features of European societies affect public preferences and public opinion. At the same time, public preferences and opinion shapes how governments react to immigration.


This course considers the many linkages between public preferences and social policies as they relate to immigrants, immigration and refugee seeking. It considers how the public reacts to immigration in voting, group dynamics and social movements (protests, violence). It considers how immigrants react to their statuses in European countries. It looks at how public reactions link to state policies and the distribution of power among political parties, and how states interact with each other in the European Union. All of this will take place with a focus on survey data. We will read relevant literature and analyze relevant data as part of the course work. This data analysis will take place using Stata statistical software.

Literature to frame the course:

  • Kulin, Joakim, Maureen A. Eger, and Mikael Hjerm. 2016. “Immigration or Welfare? The Progressive’s Dilemma Revisited.” Socius 2:1–15.
  • Bay, Ann-Helén and Axel West Pedersen. 2006. “The Limits of Social Solidarity: Basic Income, Immigration and the Legitimacy of the Universal Welfare State.” Acta Sociologica 49(4):419–36.

 

- Active and regular participation
- Short oral presentation

The course grade is based on a research project to be submitted in the form of a final paper.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.18
29.05.18
Tuesday
12:00
13:30
310 in B 6, 30-32 entrance E-F

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

tbd


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.18
29.05.18
Tuesday
13:45
15:15
B 318 in A 5, 6 entrance B

Lecturer(s)


Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

CSSR, 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
13.02.18
Tuesday
10:15
11:45
12.03.18
19.03.18
Monday
08:30
12:30
C 212 in A5, 6 entrance C
30.04.18
07.05.18
Monday
08:30
12:30
C 212 in A5, 6 entrance C

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

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
14.02.18
30.05.18
Wednesday
12:00
13:30
B 317 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
15.02.18
31.05.18
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B


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 Content

Bring your (methods) problems. Thursdays between 10-11am, please make an appointment by sending an e-mail to rtraunmu@mail.uni-mannheim.de.



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. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to 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 world has changed for empirical social scientists. The new types of “big data” have generated an entire new research field—that of data science. That world is dominated by computer scientists who have generated new ways of creating and collecting data, developed new analytical and statistical techniques, and provided new ways of visualizing and presenting information. These new sources of data and techniques have the potential to transform the way applied social science is done. Research has certainly changed. Researchers draw on data that are “found” rather than “made” by federal agencies; those publishing in leading academic journals are much less likely today to draw on preprocessed survey data. And the jobs have changed. The new job title of “data scientist” is highlighted in job advertisements on CareerBuilder.com and Burning-glass.com—in the same category as statisticians, economists, and other quantitative social scientists if starting salaries are useful indicators.

The goal of this course is to provide social scientists with an understanding of the key elements of this new science, its value, and the opportunities for doing better work. The goal is also to identify the many ways in which the analytical toolkits possessed by social scientists can be brought to bear to enhance the generalizability of the work done by computer scientists.

We meet four-five times for an extended period of time in this seminar. In the first sessions we will introduce new data sources and tools to tackle them. We will also discuss extensively what research questions can be answered with which data source. In the month following we expect participants to engage in their own data collection and present and critically discuss their results in the following meetings. We advise all students to set aside time in those weeks, to fully work on the assigned projects.

Literature

We will use the following books and require familiarity with the books in preparation for the course.

  • Foster, I., Ghani, R., Jarmin R.S., Kreuter F., Lane J. (Eds.) (2016): Big Data and Social Science: A Practical Guide to Methods and Tools.
  • Chapman and Hall/CRC Press.Salganik, M. (2017): Bit by Bit. Social Research in the Digital Age. Princeton University
    Online version: http://www.bitbybitbook.com/en/preface/
  • Stephens-Davidowitz, S. (2017). Everybody lies: Big Data, New Data, and What the internet can tell us about who we really are. Dey Street Books.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.18
Wednesday
15:30
17:00
310 in B 6, 30-32 entrance E-F
15.03.18
16.03.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A
12.04.18
13.04.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

 


Course Content

This course continues where last semester’s course Introduction to Bayesian Statistics for Social Scientists I left off. But newcomers are welcome. We will extend our Bayesian tool kit by adding Bayesian models of ordered and un-ordered categorical outcomes, Bayesian measurement models, and Bayesian Non-parametric methods. We will also spend some time to further discuss Prior specifications and issues of Bayesian model checking, selection and averaging.

Recommended Reading

  • Gill, J. (2015). Bayesian Methods. A Social and Behavioral Sciences Approach (3rd Edition). CRC Press.
  • Jackman, S. (2009). Bayesian Analysis for the Social Sciences. Wiley.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
15.02.18
22.03.18
Thursday
13:45
17:00
211
16.02.18
23.03.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Not taking place on 24.04 and 29.05.2018
13.02.18
22.05.18
Tuesday
13:45
15:15
A 103 in B 6, 23-25 entrance A
15.05.18
22.05.18
Tuesday
15:30
17:00
A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.04.18
Friday
10:15
17:00
A 102 in B6, 23-25 entrance A
20.04.18
Friday
10:15
18:45
A 102 in B6, 23-25 entrance A
04.05.18
Friday
10:15
15:15
A 102 in B6, 23-25 entrance A

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 - 14, 21, and 28 Feb / 14 Mar / 11, 18, and 25 Apr
14.02.18
25.04.18
Wednesday
13:45
17:00
B 318 in A5, 6 entrance B

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
23.02.18
01.06.18
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Registration for this course is no longer possible.


Course Content

This course offers an introduction to the Python programming language with a focus on solving social science problems. The course is structured around a series of practical exercises for working with basic Python functionalities, reading structured and “unstructured” datasets into Python, using statistical and network analytic methods and designing visualizations. The course demonstrates the utility of working with Python in two ways. We are going to replicate quantitative analyses from publications in social science journals and we will take advantage of Python’s compatibility with application programming interfaces (APIs) in order to explore several new data sources (e.g., Twitter, Wikipedia, Meetup, Github, Discogs). The replication exercises require some mandatory reading and supplementary materials will be suggested. The main focus of the course will concentrate on practicing Python programming and discussing when and how it might benefit social science research. We will also take time to discuss the applicability of Python to ongoing research interests among the participants. No prior programming experience is required.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.02.18
Thursday
08:30
11:45
C -108 in A 5, 6 entrance C
09.02.18
Friday
08:30
17:00
C -108 in A 5, 6 entrance C
15.02.18
Thursday
08:30
17:00
C 109 PC Pool in A 5, 6 entrance C


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

All statistical models are false. Instead of searching for the “best” or the “true” model specification, the logic of robustness testing is to accept model uncertainty and to study how model estimates and inferences react to changes in model assumptions. After looking at classical contributions (Leamer, Rosenbaum, Manski, and Frank), we will discuss in greater detail the new robustness framework proposed by Neumayer and Plümper. This covers robustness testing problems of measurement uncertainty, omitted variables, functional form, causal and temporal heterogeneity, dynamics and spatial dependence. This course will rely heavily on students’ presentations of specific robustness tests. 

Recommended Reading:

Neumayer, E. & Plümper, T. (2017). Robustness Tests for Quantitative Research. Cambridge University Press.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.04.18
01.06.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The first law of geography states that “everything is related to everything else, but near things are more related than distant things” (Tobler 1970: 236). In the Social Sciences, geographic data and spatial analyses offer rich insights into a variety of relevant research questions (Franzese and Hays 2008). The course covers crucial concepts involved in spatial analysis, introduces a toolbox of statistical models and pays particular attention to the accessible implementation of spatial analysis in free software (working with R and packages for spatial analysis). This implies that participants should bring their Laptop with R (required) and RStudio (recommended) installed.

Parts

I.                   Concepts

The first part of the course deals with the concepts involved in spatial analysis. We will spend some time getting to know “W”, the connectivity matrix which defines spatial dependencies (Neumayer and Plümper 2016). Alternative conceptions of neighborhood and the weighting of connections are discussed. For example, spatial proximity does not necessarily imply geographic proximity, as for instance trade or information exchange can bring distance things closely together. Further concepts handled include the geo-referencing of data, regarding both its use and generation.

II.                Models

To test hypotheses based on spatial data, tailored statistical tools are needed. The second part of the course is dedicated to spatial correlation coefficients (such as Moran’s I), varieties of spatial regression models (variants of spatial lags; categorical, count and duration specifications), spatio-temporal models as well as extensions to the multilevel case. The options offered by a Bayesian approach to spatial data analysis are also discussed.

III.             Implementation

As mentioned, one focus of the course is the accessible implementation of spatial analysis. To this end, the free statistical software R is used (https://www.r-project.org/). Its advantages in addition to the open source character are the provision of user-written packages, including several on geodata and spatial analysis (such as sp, maptools or spdep) as well as powerful graphical capabilities (see Bivand et al. 2013). Recommended is the combination of R with the RStudio editor/environment (https://www.rstudio.com/). For the Bayesian models, we rely on JAGS (http://mcmc-jags.sourceforge.net/) or Stan (http://mc-stan.org/). During this part of the course, geographic data are handled, autocorrelations and spatial regression models are estimated, and everything is thoroughly visualized.

IV.             Applications

The last part of the course is devoted to the participants’ own applications in the field of geographic data and spatial analysis. Anticipated throughout the course, they will look for and handle their own geographic data and we will jointly identify adequate spatial models to test hypotheses. The final meeting is dedicated to the presentation and discussion of the paper outlines, with a focus on a (preliminary) spatial analysis of geographic data.

Assessment

Active participation, exercises, presentation with preliminary analysis, paper on spatial analysis of geographic data (4000-5000 words)

References

Bivand, R. S., E. Pebesma and V. Gómez-Rubio (2013): Applied Spatial Data Analysis with R (2nd Edition). New York: Springer.

Franzese, R. J. and J. C. Hays (2008): Interdependence in Comparative Politics. Substance, Theory, Empirics, Substance. Comparative Political Studies 41(4/5): 742-780.

Neumayer, E. and T. Plümper (2016): W. Political Science Research and Methods 4(1): 175-193.

Tobler, W. R. (1970): A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46(Suppl.): 234-340.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
irregular dates - 20 & 27 Feb, 06 & 20 Mar, 24 Apr
20.02.18
Tuesday
08:30
11:45
212/13 in B6, 30-32


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Survey experiments are an increasingly popular tool in the social sciences. The attractiveness of this hybrid methodology stems from the fact that it combines the internal validity of experimental research with the external validity of survey sampling. After a brief review of the key concepts and benefits of these two foundations, the course will discuss both the design and the analysis of modern survey experiments. In particular, we will take an in-depth look into the design and analysis of factorial surveys/conjoint experiments for complex judgments and of list experiments for sensitive questions. Some time will also be spent on discussing the pro and cons of online surveys and crowd-sourced experiments.

Assessment: Take home exam or paper

Recommended Reading: Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton University Press.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
12.04.18
31.05.18
Thursday
13:45
17:00
211


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

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 “Multivariate Analyses” in the M.A. program 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
14.02.18
30.05.18
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
15.02.18
24.05.18
Thursday
10:15
11:45
B 317, A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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.
Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Course requirements

Reading relevant literature
Short oral presentation
Groupwork

Written report (Developing study design)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
15.02.18
24.05.18
Thursday
12:00
13:30
EO 256 Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

tbd


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
12.02.18
19.03.18
Monday
12:00
15:15
A 301 in B 6, 23-25 entrance A
17.03.18
Saturday
12:00
15:15
A 301 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The idea of deliberative democracy is the most influential and controversial normative conception of modern democratic theory. According to this model, deliberative communication is at the heart of a truly democratic political process. It involves carefully examining political problems and arriving at well-reasoned solutions after dialogical, inclusive and respectful consideration of diverse points of view. In various ways, deliberative communication is believed to enhance the quality of democracy. Ordinary people’s everyday communication about political matters is often seen as the foundation of a truly deliberative democracy. But can ordinary citizens’ meet the high standards of deliberative communication? To what extent does their communication correspond to the deliberative ideal, and which consequences follow from this? Especially in times of increasing party-political and ideological polarization, spurred by the rise of right-wing populism, these are important questions. The seminar offers a unique opportunity to get first-hand insights on whether citizens can and do deliberate. Following a discussion of the concept of democratic deliberation and its measurement participants will analyze freshly collected data from an extensive survey on citizens’ (offline and online) everyday communication conducted in an MZES project. It will be explored how citizens discuss politics, which factors promote a more or less deliberative style of everyday communication, and which consequences this has for citizens’ political attitudes and behaviors.

Literature

  • Floridia, A. (2017). From Participation to Deliberation. A Critical Genealogy of Deliberative Democracy. Colchester: ECPR Press.
  • Gastil, J. (2008). Political Communication and Deliberation. Thousand Oaks: Sage.
  • Mutz, D.C. (2008). Is deliberative democracy a falsifiable theory? Annual Review of Political Science 11: 521-538.
  • Schmitt-Beck, R. and Lup, O. (2013): Seeking the Soul of Democracy: A Review of Recent Research into Citizens’ Political Talk Culture. Swiss Political Science Review 19: 513-538
  • Steiner, J. (2012). The Foundations of Deliberative Democracy. Empirical Research and Normative Implications. Cambridge: Cambridge University Press.

 

Oral presentation of the preparatory work of the term paper as well as active participation during the sessions and regular attendance. Term paper (6000 words).


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.18
29.05.18
Tuesday
12:00
13:30
B 317 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

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 and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers with matching research interests. Within these panels, the students should talk about their research interests 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 and discussion at GESS Research Day
  • Three pages referee report
  • Participation in discussion panel during the Research Day
  • Three pages individual reflexion of the Research Day
  • Ongoing meetings with discussion panel group
  • Participation at Science Speed Dating event
  • Five pages interdisciplinary research proposal (group of two students)
  • Presentation of research proposal
  • Only pass/fail

 

If you are interested please register until January31st,2018, by sending a title and an extended abstract of your research project you would like to present to your respective center representative. Please indicate in your e-mail your fields of interest and if you have any, mention fields you would like to collaborate with.

 

Course dates:

-          Research Day: March 5th, 2018

-          Speed Dating: May 15th, 2018

-          Presentation of research proposal: tbd, around mid-June


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

CSSR, 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
13.02.18
Tuesday
10:15
11:45
12.03.18
19.03.18
Monday
08:30
12:30
C 212 in A5, 6 entrance C
30.04.18
07.05.18
Monday
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

CSSR, TBCI, Dissertation Proposal Workshop


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
12.02.18
28.05.18
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
15.02.18
31.05.18
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B

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
04.05.18
Friday
10:00
18:00
tbd
19.05.18
Saturday
10:00
18:00
tbd
16.06.18
Saturday
10:00
18:00
tbd


Course Type: elective course

Course Content

Bring your (methods) problems. Thursdays between 10-11am, please make an appointment by sending an e-mail to rtraunmu@mail.uni-mannheim.de.



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. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to 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 world has changed for empirical social scientists. The new types of “big data” have generated an entire new research field—that of data science. That world is dominated by computer scientists who have generated new ways of creating and collecting data, developed new analytical and statistical techniques, and provided new ways of visualizing and presenting information. These new sources of data and techniques have the potential to transform the way applied social science is done. Research has certainly changed. Researchers draw on data that are “found” rather than “made” by federal agencies; those publishing in leading academic journals are much less likely today to draw on preprocessed survey data. And the jobs have changed. The new job title of “data scientist” is highlighted in job advertisements on CareerBuilder.com and Burning-glass.com—in the same category as statisticians, economists, and other quantitative social scientists if starting salaries are useful indicators.

The goal of this course is to provide social scientists with an understanding of the key elements of this new science, its value, and the opportunities for doing better work. The goal is also to identify the many ways in which the analytical toolkits possessed by social scientists can be brought to bear to enhance the generalizability of the work done by computer scientists.

We meet four-five times for an extended period of time in this seminar. In the first sessions we will introduce new data sources and tools to tackle them. We will also discuss extensively what research questions can be answered with which data source. In the month following we expect participants to engage in their own data collection and present and critically discuss their results in the following meetings. We advise all students to set aside time in those weeks, to fully work on the assigned projects.

Literature

We will use the following books and require familiarity with the books in preparation for the course.

  • Foster, I., Ghani, R., Jarmin R.S., Kreuter F., Lane J. (Eds.) (2016): Big Data and Social Science: A Practical Guide to Methods and Tools.
  • Chapman and Hall/CRC Press.Salganik, M. (2017): Bit by Bit. Social Research in the Digital Age. Princeton University
    Online version: http://www.bitbybitbook.com/en/preface/
  • Stephens-Davidowitz, S. (2017). Everybody lies: Big Data, New Data, and What the internet can tell us about who we really are. Dey Street Books.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.18
Wednesday
15:30
17:00
310 in B 6, 30-32 entrance E-F
15.03.18
16.03.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A
12.04.18
13.04.18
Thursday & Friday
13:45
17:00
A 204 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

 


Course Content

This course continues where last semester’s course Introduction to Bayesian Statistics for Social Scientists I left off. But newcomers are welcome. We will extend our Bayesian tool kit by adding Bayesian models of ordered and un-ordered categorical outcomes, Bayesian measurement models, and Bayesian Non-parametric methods. We will also spend some time to further discuss Prior specifications and issues of Bayesian model checking, selection and averaging.

Recommended Reading

  • Gill, J. (2015). Bayesian Methods. A Social and Behavioral Sciences Approach (3rd Edition). CRC Press.
  • Jackman, S. (2009). Bayesian Analysis for the Social Sciences. Wiley.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
15.02.18
22.03.18
Thursday
13:45
17:00
211
16.02.18
23.03.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Not taking place on 24.04 and 29.05.2018
13.02.18
22.05.18
Tuesday
13:45
15:15
A 103 in B 6, 23-25 entrance A
15.05.18
22.05.18
Tuesday
15:30
17:00
A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Course Description: 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. The discussed methods will be implemented using the statistical programming language R.

References:


Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2017). Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: CRC Press Taylor & Francis Group.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. New York, NY: Springer.

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York, NY: Springer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.04.18
Friday
10:15
17:00
A 102 in B6, 23-25 entrance A
20.04.18
Friday
10:15
18:45
A 102 in B6, 23-25 entrance A
04.05.18
Friday
10:15
15:15
A 102 in B6, 23-25 entrance A

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 - 14, 21, and 28 Feb / 14 Mar / 11, 18, and 25 Apr
14.02.18
25.04.18
Wednesday
13:45
17:00
B 318 in A5, 6 entrance B

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
23.02.18
01.06.18
Friday
10:15
13:30
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Registration for this course is no longer possible.


Course Content

This course offers an introduction to the Python programming language with a focus on solving social science problems. The course is structured around a series of practical exercises for working with basic Python functionalities, reading structured and “unstructured” datasets into Python, using statistical and network analytic methods and designing visualizations. The course demonstrates the utility of working with Python in two ways. We are going to replicate quantitative analyses from publications in social science journals and we will take advantage of Python’s compatibility with application programming interfaces (APIs) in order to explore several new data sources (e.g., Twitter, Wikipedia, Meetup, Github, Discogs). The replication exercises require some mandatory reading and supplementary materials will be suggested. The main focus of the course will concentrate on practicing Python programming and discussing when and how it might benefit social science research. We will also take time to discuss the applicability of Python to ongoing research interests among the participants. No prior programming experience is required.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.02.18
Thursday
08:30
11:45
C -108 in A 5, 6 entrance C
09.02.18
Friday
08:30
17:00
C -108 in A 5, 6 entrance C
15.02.18
Thursday
08:30
17:00
C 109 PC Pool in A 5, 6 entrance C


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

All statistical models are false. Instead of searching for the “best” or the “true” model specification, the logic of robustness testing is to accept model uncertainty and to study how model estimates and inferences react to changes in model assumptions. After looking at classical contributions (Leamer, Rosenbaum, Manski, and Frank), we will discuss in greater detail the new robustness framework proposed by Neumayer and Plümper. This covers robustness testing problems of measurement uncertainty, omitted variables, functional form, causal and temporal heterogeneity, dynamics and spatial dependence. This course will rely heavily on students’ presentations of specific robustness tests. 

Recommended Reading:

Neumayer, E. & Plümper, T. (2017). Robustness Tests for Quantitative Research. Cambridge University Press.

 

Assessment: Take home exam or paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.04.18
01.06.18
Friday
13:45
17:00
211

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The first law of geography states that “everything is related to everything else, but near things are more related than distant things” (Tobler 1970: 236). In the Social Sciences, geographic data and spatial analyses offer rich insights into a variety of relevant research questions (Franzese and Hays 2008). The course covers crucial concepts involved in spatial analysis, introduces a toolbox of statistical models and pays particular attention to the accessible implementation of spatial analysis in free software (working with R and packages for spatial analysis). This implies that participants should bring their Laptop with R (required) and RStudio (recommended) installed.

Parts

I.                   Concepts

The first part of the course deals with the concepts involved in spatial analysis. We will spend some time getting to know “W”, the connectivity matrix which defines spatial dependencies (Neumayer and Plümper 2016). Alternative conceptions of neighborhood and the weighting of connections are discussed. For example, spatial proximity does not necessarily imply geographic proximity, as for instance trade or information exchange can bring distance things closely together. Further concepts handled include the geo-referencing of data, regarding both its use and generation.

II.                Models

To test hypotheses based on spatial data, tailored statistical tools are needed. The second part of the course is dedicated to spatial correlation coefficients (such as Moran’s I), varieties of spatial regression models (variants of spatial lags; categorical, count and duration specifications), spatio-temporal models as well as extensions to the multilevel case. The options offered by a Bayesian approach to spatial data analysis are also discussed.

III.             Implementation

As mentioned, one focus of the course is the accessible implementation of spatial analysis. To this end, the free statistical software R is used (https://www.r-project.org/). Its advantages in addition to the open source character are the provision of user-written packages, including several on geodata and spatial analysis (such as sp, maptools or spdep) as well as powerful graphical capabilities (see Bivand et al. 2013). Recommended is the combination of R with the RStudio editor/environment (https://www.rstudio.com/). For the Bayesian models, we rely on JAGS (http://mcmc-jags.sourceforge.net/) or Stan (http://mc-stan.org/). During this part of the course, geographic data are handled, autocorrelations and spatial regression models are estimated, and everything is thoroughly visualized.

IV.             Applications

The last part of the course is devoted to the participants’ own applications in the field of geographic data and spatial analysis. Anticipated throughout the course, they will look for and handle their own geographic data and we will jointly identify adequate spatial models to test hypotheses. The final meeting is dedicated to the presentation and discussion of the paper outlines, with a focus on a (preliminary) spatial analysis of geographic data.

Assessment

Active participation, exercises, presentation with preliminary analysis, paper on spatial analysis of geographic data (4000-5000 words)

References

Bivand, R. S., E. Pebesma and V. Gómez-Rubio (2013): Applied Spatial Data Analysis with R (2nd Edition). New York: Springer.

Franzese, R. J. and J. C. Hays (2008): Interdependence in Comparative Politics. Substance, Theory, Empirics, Substance. Comparative Political Studies 41(4/5): 742-780.

Neumayer, E. and T. Plümper (2016): W. Political Science Research and Methods 4(1): 175-193.

Tobler, W. R. (1970): A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46(Suppl.): 234-340.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
irregular dates - 20 & 27 Feb, 06 & 20 Mar, 24 Apr
20.02.18
Tuesday
08:30
11:45
212/13 in B6, 30-32


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Survey experiments are an increasingly popular tool in the social sciences. The attractiveness of this hybrid methodology stems from the fact that it combines the internal validity of experimental research with the external validity of survey sampling. After a brief review of the key concepts and benefits of these two foundations, the course will discuss both the design and the analysis of modern survey experiments. In particular, we will take an in-depth look into the design and analysis of factorial surveys/conjoint experiments for complex judgments and of list experiments for sensitive questions. Some time will also be spent on discussing the pro and cons of online surveys and crowd-sourced experiments.

Assessment: Take home exam or paper

Recommended Reading: Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton University Press.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
12.04.18
31.05.18
Thursday
13:45
17:00
211


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

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 “Multivariate Analyses” in the M.A. program 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
14.02.18
30.05.18
Wednesday
08:30
10:00
B 244 in A 5, 6 entrance B
Tutorial
15.02.18
24.05.18
Thursday
10:15
11:45
B 317, A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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.
Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Course requirements

Reading relevant literature
Short oral presentation
Groupwork

Written report (Developing study design)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
15.02.18
24.05.18
Thursday
12:00
13:30
EO 256 Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

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 and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers with matching research interests. Within these panels, the students should talk about their research interests 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 and discussion at GESS Research Day
  • Three pages referee report
  • Participation in discussion panel during the Research Day
  • Three pages individual reflexion of the Research Day
  • Ongoing meetings with discussion panel group
  • Participation at Science Speed Dating event
  • Five pages interdisciplinary research proposal (group of two students)
  • Presentation of research proposal
  • Only pass/fail

 

If you are interested please register until January31st,2018, by sending a title and an extended abstract of your research project you would like to present to your respective center representative. Please indicate in your e-mail your fields of interest and if you have any, mention fields you would like to collaborate with.

 

Course dates:

-          Research Day: March 5th, 2018

-          Speed Dating: May 15th, 2018

-          Presentation of research proposal: tbd, around mid-June


Competences acquired

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

Register

Social Sciences Spring 2018

Dissertation Tutorial: Sociology (Gautschi&Hillmann / Keusch&Kreuter / Kogan,Kalter&Schmidt-Catran)
DIS
Dissertation Proposal Workshop
RES
CDSS Workshop: Sociology
RES
English Academic Writing
RES
MZES A Colloquium "European Societies and their Integration"
CDSS Methods Clinic
MET
7th GESIS Summer School in Survey Methodology
MET
Big Data in the Social Sciences
MET
Introduction to Bayesian Statistics for Social Scientists II
MET
Machine Learning in the Social Sciences
MET
Machine Learning in the Social Sciences (block course) - COURSE IS COMPLETE waiting list only
MET
Multilevel Modeling
MET
Programming in R and beyond
MET
Python programming for social sciences
MET
Robustness Analysis
MET
Spatial Analysis of Geographic Data
MET
Survey Experiments
MET/POL
Advanced Quantitative Methods
MET/PSY
Psychological interventions using diary designs
RES
Interdisciplinary Work in Economics and Social Sciences (Bridge Course)
SOC
Economy & the Welfare State: Structuralist Theory
SOC
Migration & Integration: Public Preferences and Social Policy: The Impacts of Immigration in Europe
SOC
Migration, Integration and Ethnic Diversity
RES
CDSS Workshop: Political Science
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
POL
Selected Topics in Comparative Politics:
POL
Selected Topics in Comparative Politics: Democratic deliberation in times of populism and political polarization
RES
AC3/BC4: Colloquia II
RES
CDSS Workshop: Research in Psychology
RES
English Academic Writing for Psychologists