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

Fall 2017

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science and sociology. CDSS faculty members will present an overview of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

Homework assignment (3 pages)

Talk schedule


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.09.17
Tuesday
08:30
11:45
308 in L9, 7
13.09.17
Wednesday
10:15
13:30
409 in L9, 1-2
13.10.17
Friday
10:15
13:30
308 in L9, 7
18.10.17
Wednesday
09:30
12:35
409 in L9, 1-2
24.10.17
Tuesday
08:30
11:45
409 in L9, 1-2

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

In recent decades, applications of statistics and formal modeling have become part of the main stream in the social sciences. Their contribution to our fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

The exam is scheduled for 12 December from 10.15-12.15am in room 211 in B6, 30-32.

Basic readings:

  • Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall


Additional readings:

  • Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
  • Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
  • Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
The course will not take place on 18 October!
06.09.17
06.12.17
Wednesday
08:30
10:00
A 305 Seminarraum in B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

All researchers face similar challenges with core issues of research design. A research design is a plan that specifies how you are going to carry out a research project and, particularly, how to use evidence to answer your research question. The goal of this course is to jump-start students with their dissertation proposal. This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.09.17
05.12.17
Tuesday
12:00
13:30
B243 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

This course begins with an overview and investigation of the types of explanations, and therefore the types of theory, that are developed and applied in the social sciences. This part of the course will have an emphasis on reading and discussion. The goal will be to form an integrated picture of theory, explanation, and the role of methods in the social sciences. We will therefore, whenever possible, connect these debates to the formal and practical aspects of research and methods. We will take a strongly interdisciplinary approach that includes discussion of fields outside the social sciences in order to give some comparative perspective on theory and explanation.

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We will operate largely within the unitary formal account of causation and causal inference that has emerged over the last decade in large part due to the work of Pearl and Rubin. Within this conceptual framework we will investigate experiments, both designed and natural, conditioning methods such as regression, and matching as complementary ways to identify causal effects. This part of the course will emphasize the applied computational aspects of these techniques. We will briefly discuss alternatives to the framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
04.09.17
16.10.17
Monday
14:00
18:00
P044 in L7, 3-5

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Participation is mandatory for second and third year CDSS students of Political Science.
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
06.09.17
06.12.17
Wednesday
12:00
13:30
A 104 in B6, 23-25 entrance A


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: 4

Prerequisites

You should be familiar with the basics of regression models and maximum likelihood estimation. No previous knowledge of software for Bayesian inference is required. However, we will use R as a front-end to JAGS and for graphical displays. Resources to learn R basics are UCLA’s Stat Consulting Site as well as the official manuals at http: //www.r-project.org/.


Course Content

This course introduces and extends the classical “workhorse” social science models – linear, logit, probit models and their multilevel extensions – from a Bayesian perspective.
The Bayesian approach to inference has attracted considerable attention in recent years. Mostly this is due to the increasingly complex models that it allows to fit. However, one might easily overlook the benefits that a Bayesian approach provides when estimating “standard” generalized linear models.
The course will introduce the basics of Bayesian inference, showing how its interpretation of probability differs from the classical approach and how it is actually closer to how social scientists think about their models. The course then introduces generalized linear models and shows how they can be easily fitted using modern software for Bayesian inference. It introduces Bayesian model diagnostics and fit measures, which allow straightforward model comparisons and examination of model misspecification.
The focus of the course will be on how to compute interesting quantities from those models, like predicted values or first differences in expected values for a changing covariate. Using the Bayesian approach to inference, their calculation is straightforward and one can easily construct appealing graphical displays.

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
30.10.17
04.12.17
Monday
14:00
18:00
308 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 10

Course Content

The course offers an overview and several hands-on experiences on some of the most relevant methods and tools developed in the field of natural language processing, which have been often adopted as basis for quantitative content analyses in political science research. Attention is dedicated to tasks such as collection building, topic modeling, sentiment analysis, text classification, clustering and scaling as well as to the application of methods such as latent Dirichlet allocations, word embeddings and entity linking. A brief introduction to practices such as web scraping and text pre-processing (e.g. tokenisation, part-of-speech tagging, lemmatisation and stemming) is also offered.
The programming language adopted is Python (and in particular the use of Jupyter Notebooks). No previous programming experience is needed.

 https://federiconanni.com/computational-text-analysis/


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
06.09.17
06.12.17
Wednesday
10:15
11:45
217, 2nd floor Parkring 47


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to a social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to first the maximum likelihood estimator and second to nonlinear models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes.

Literature:

  • Fox, John (1997). Applied regression analysis, linear models and related methods. London: Sage.
  • Greene, William H. (2003). Econometric analysis. 5. Auflage. Upper Saddle River: Prentice Hall.
  • Gujarati, Damodar N. (2003). Basic econometrics. 4. Auflage. Boston: McGraw-Hill.
  • Long, J. Scott (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.
  • Verbeek, Marno (2004). A guide to modern econometrics. 2. Auflage. Chichester: Wiley.

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
13:45
15:15
B 317 in A5,6 entrance B
Tutorial
05.09.17
05.12.17
Tuesday
15:30
17:00
C-108 (PC Lab) in A5,6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

This course gives an overview of the design and implementation of survey questionnaires from the operationalization of the research questions to their implementation in a full questionnaire. Topics covered include operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, ordering the questionnaire, the effect of survey modes and questionnaire design in cross-cultural research. The course will be taught in a mix of seminar-style sessions, where the literature on questionnaire design is presented and discussed, and hands-on practical sessions, where students design and test survey questions.

15 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
15 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B

29 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
29 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B
29 Sep 13:45-17:00 either in room B 143 or B 243 in A5, 6 entrance B (the group will be split)


Tutorial

Data Collection:Interviewers and Interview Effects


Interviewers occupy a central role in the implementation of face-to-face and telephone surveys. We now know that interviewers affect the survey process in various ways, both positively (e.g., increasing response rates) and negatively (e.g., introducing measurement errors). The Total Survey Error (TSE) framework describes the different ways that interviewers can affect the survey process. Interviewers can have variable effects on the coverage of the sampling frame during listing and screening operations, rates of contact and cooperation (nonresponse), the answers that cooperating respondents provide (measurement), and the coding and editing of the information provided (processing). This course considers the roles that interviewers play during the survey process and and reviews key literature on interviewer effects on bias and variance in survey estimates.

30 Sep 10:15-17:00 in room B 317 in A5, 6 entrance B
30 Sep 13:45-17:00 in room B 244 in A5, 6 entrance B

10. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
10. Nov 10:15-11:45 in room B 318 in A5, 6 entrance B
10. Nov 12:00-17:00 in room B 244 in A5, 6 entrance B

17. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
17. Nov 10:15-17:00 in room C-108 Methods lab in A5, 6 entrance C


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - 15 & 29 Sep
15.09.17
08:30
17:00
various
Tutorial
irregular - 30 Sep, 10 & 17 Nov
30.09.17
Friday & Saturday
08:30
00:00
various


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Data visualization is one of the most powerful tools to explore, understand and communicate
patterns in quantitative information. At the same time, good data visualization is a
surprisingly difficult task and demands three quite different skills: substantive knowledge,
statistical skill, and artistic sense. The course is intended to introduce participants to a) key
principles of graphical perception and analytic design, b) useful visualization techniques for
the exploration and presentation of univariate, multivariate, time series and geographic data
and c) new developments of data visualization for the social sciences, such as interactive data
visualization, visual inference, and visualizing statistical models. This course is highly applied
in nature and emphasizes the practical aspects of data visualization in the social sciences.
Students will learn how to evaluate data visualizations based on principles of analytic design,
how to construct compelling visualizations using the free statistics software R, and how to
explore and present their data and models with visual methods. In short, students will get
hands-on experience producing modern visualizations for their practical problems. This will
be especially helpful to those students with own datasets related to their research.

Suggested Reading

  • Few, Stephen (2009). Now you see it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
  • Tufte, Edward (2001). The Visual Display of Quantitative Information. (Second Edition).Graphics Press.
  • Murrell, Paul (2011). R Graphics. (Second Edition). Chapman & Hall/CRC.Unwin, Anthony (2015). Graphical Data Analysis with R. CRC Press.


Rough Course Outline

Part I: Foundations and Principles of Data Visualisation

Intro

  • Statistical Graphs vs. InfoVis
  • A Brief History of Visualisation
  • Graphs vs. Tables
  • Basic Graphs in R

Data Visualisation as a Methodology

  • Exploration vs. Presentation
  • Comparison, Comparison
  • Component Graph Design in R

Graphical Perception

  • Attributes of Preattentive Processing
  • Gestalt Principles of Visual Perception
  • General Graph Design

Part II: Tools and Methods of Data Visualisation

Visualisation of Bivariate and Time Series Data

  • Enhancing Scatterplots
  • Overplotting Reduction
  • loess: Scatterplot Smoothers
  • Slope Graphs

Visualisation of Multivariate Data

  • Small Multiples
  • Mosaic Plots
  • Heatmaps
  • Parallel Coordinate Plots

Visualisation of Geographical Data

  • Map Variations (Choropleth, Dot, Flow)
  • Linked Micro Maps


Part III: New Developments and Extensions of Data Visualization

Interactive Data Visualisation

  • Selection and Identification
  • Zooming and Filtering
  • Highlighting and Linking
  • Shiny

Visual Inference

  • Is what we see really “there”?
  • Simulation Inference
  • The Line-up Protocol

Visualisation of Statistical Models

  • Graphs instead of Regression Tables
  • Visualising Quantities of Interest
  • Visualising Inferential Uncertainty
  • Visual Diagnostics & Model Checking

Student Visualisation Projects


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
21.09.17
09.11.17
Thursday
12:00
15:15
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.

In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

Graded assignments include homeworks, a mid-term exam and data analysis projects.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
08:30
10:00
B244 in A5,6 entrance B
Tutorial
Sternberg, Sebastian
04.09.17
04.12.17
Monday
12:00
13:30
Room C -108 (PC Lab) in A5,6, entrance C
Neunhoeffer, Marcel
07.09.17
07.12.17
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Literature will be given during the course

Course achievement - regular participation of the course; non-graded test

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
08.09.17
01.12.17
Friday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of Freedman (2005, see below) or equivalent. Familiarity with notions of research design in the social sciences and the statistical package Stata or R.

If you need to review material on regression models, please consult this excellent textbook: Freedman, David. 2005. Statistical Models: Theory and Practice. Cambridge University Press.


Course Content

This course teaches the fundamental concepts behind the estimation of causal effects, including potential obstacles to causal inference, faulty measurement, spuriousness, specification errors, and other problems that can lead to inappropriate causal inferences. We will discuss the benefits and the difficulties of randomization in survey research in the first half of the class. The focus of the second half is on the design of observational studies. Real-world examples will be discussed, with an emphasis on examples from survey methodology. Students will come away with an understanding of how to estimate causal effects in both randomized and observational settings, with a particular focus on careful design of both types of studies.

There will be several assignments/Exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
13:45
15:15
B 244 in A5, 6 entrance B
Tutorial
07.09.17
07.12.17
Thursday
15:30
17:00
B 143 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Basic knowledge of Bayesian statistics.


Course Content

Diverse practical aspects of stochastic simulation in the Social Sciences. Specific content is adapted according to  student requests and presented in a mixture of a lecture, seminar and practical course.


Competences acquired

Deeper understanding of techniques of stochastic simulation and their application in the Social Sciences


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.17
06.12.17
Wednesday
15:30
17:00
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET/POL

Credits: 10

Prerequisites

Any intro into Game Theory.


Course Content

This course is a continuation of the intro into Game Theory and covers advanced topics in game theory with a particular emphasis on the link of theories, methods and empirics. At the core, we discuss techniques used to analyze settings of imperfect information and  covered topics include normal form games with incomplete information and Bayesian equilibrium, stochastic games and Markov-perfect equilibrium, behavioral game theory and quantal-response equilibrium, signalling games and cheap talk, information transmission, mechanism design, comparative statics, monotone comparative statics and structural estimation.

Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. As this is a seminar, the course allows students to pursue areas of individual interest in more depth, and therefore course content is to some extent determined based on the interests of the students. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

Academic assessment: term paper

Literatur

  • Tadelis, Steven. 2013. Game Theory, An Introduction. Princeton, NJ: Princeton University Press.
  • Gehlbach, Scott. 2013. Formal Models of Domestic Politics. Cambridge: Cambridge University Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
10:15
11:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Course Content

Game theory and other formal modelling techniques are powerful methodological tools that are widely employed in political science and the social sciences, in general. The associated mathematics and notation can, nevertheless, be bewildering and frustrating to the newcomer. This course exposes students to the mechanics of a variety of formal models used in political sciences, showing them the underlying logic of these models, as well as the surrounding notation and mathematics. The overall aim of the course is to put students in a position where they can more effectively read literature that employs game theoretical modelling, and actually make use of formal modelling techniques in their own work.

Literature
McCarty, Nolan/Adam Meirowitz, 2007, Political Game Theory. Cambridge: Cambridge University Press

Tutorial

The tutorial accompanies the graduate-level introductory lecture in game theory. Its main objective
is to practice solution concepts for static and dynamic games of complete and incomplete information.
The contents are centered around the material covered in the lecture. Thus, the following key areas will
be discussed: preferences and individual choices, decision theory, normal form games, Nash equilibria,
extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete
and imperfect information, Bayesian perfect equilibria, signalling games. At the substantial level, we
will use these concepts to study, for instance, candidate competition, political lobbying, and war and
deterrence. Students are required to submit weekly problem sets. Moreover, active participation in
class discussions is expected.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
04.12.17
Monday
10:15
11:45
B 317 in A5,6 entrance B
Tutorial
06.09.17
06.12.17
Wednesday
17:15
18:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free, open-source, and cross-platform software for creating laboratory experiments. Many standard tasks can be implemented in OpenSesame via drag and drop using its graphical user interface. In addition, complex tasks can be realized through the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. Besides, the workshop will introduce plug-ins that extend OpenSesame for specific purposes, e.g., the psynteract plug-ins that implement real-time interactions between participants (as required in many economic games), and the mousetrap plug-ins that implement mouse-tracking during decision tasks (a method that is becoming increasingly popular in the cognitive sciences to measure preference development). Additional topics will be covered depending on the preferences of the workshop participants. No prior knowledge of the software or Python is required.

As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

Software:
OpenSesame can be downloaded for free under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.

Literature:
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. https://dx.doi.org/10.3758/s13428-011-0168-7

Henninger, F., Kieslich, P. J., & Hilbig, B. E. (in press). Psynteract: A flexible, cross-platform, open framework for interactive experiments. Behavior Research Methods, 1–10. https://doi.org/10.3758/s13428-016-0801-6

Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods, 1–16. https://doi.org/10.3758/s13428-017-0900-z


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
29.09.17
Friday
12:00
17:00
EO 162 CIP Pool
30.09.17
Saturday
10:15
17:00
EO 162 CIP-Pool
13.10.17
Friday
10:15
15:15
EO 162 CIP-Pool
14.10.17
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

The goal of this course is to familiarize students with a range of statistical techniques that are
available for the analysis of one response variable (e.g., reaction time, or pupil dilation, pitch, accuracy)
that is to be modeled as a function of one or more predictors. These predictors can be factors
(e.g., native versus non-native speakers), numerical covariates (e.g., frequency of occurrence), or
combinations of factors and covariates. Modeling techniques will be introduced conceptually, and
emphasis will be on worked examples of their application. Hands on sessions provide training in
applying statistical models to real data sets from linguistics and psychology. For this course to
be pro table to them, participants should be familiar with basic concepts from statistics (random
variables, quantiles, mean, variance, normal distribution, t-distribution, t-test, hypothesis testing,
con dence intervals). As the course will make use of the R statistical programming environment,
participants should bring their laptops with R installed, and should know how to install packages
and how to load data into R.

The course has four blocks.

1. The (generalized) linear mixed model. This model is widely used for data with response
variables collected from multiple subjects and multiple items. It allows the analyst to take
into account how uncertainty about model estimates varies with subjects and items. The
vexed question of how complex a model should be to be minimally adequate will be discussed
in detail.

2. The generalized additive model: basic concepts. The generalized additive model (GAM) relaxes
the assumption that the functional relation between the response and one or more
predictors is linear. It is ideal for modeling wiggly curves and wiggly (hyper)surfaces. Model
criticism and tools for dealing with model residuals that are not identically and independently
distributed will be introduced.

3. Extending the generalized additive model. Factors may interact with numeric predictors,
resulting in wiggly curves and wiggly surfaces that may have a di erent shape depending on
the levels of that factor. Two ways of assessing such interactions are discussed, including the
modeling of a di erence curve or a di erence surface. Furthermore, examples are provided of
how ordinal data can be modeled with GAMs.

4. Quantile regression and survival analysis. The generalized linear model and the generalized
additive model predict the expected value (the mean) of the response. However, it is often
of interest to know whether the e ect of predictors is di erent depending on which quantile
(other than the median) is modeled. Quantile regression with GAMs on the one hand, and
dynamic survival analysis on the other, provide very di erent and complementary approaches
for coming to grips with the full distribution of the response.

Final assignment

Selected references (including references to studies applying regression methods):

linear regression: Baayen (2008), Harrell (2015), Anscombe (1973), Friedman and Wall (2005),
Wurm and Fisicaro (2014), Baayen (2013), Baayen (2010);
generalized linear model: Donnelly and Verkuilen (2017), Jaeger (2008), Fasold (1991), Arppe (2011), Cedergren and Sanko (1974);
linear mixed model: Pinheiro and Bates (2000), Baayen et al. (2008), Ga lecki and Burzykowski
(2013), Janda et al. (2010), Barr et al. (2013), Gonzalez et al. (2014), Bates et al. (2015), Lele et al.
(2012), Matuschek et al. (2017), Johnson (2009);
random forests: Breiman et al. (1984), Breiman (2001), Strobl et al. (2009), Tagliamonte and Baayen (2012);
generalized additive model: Wood (2006), Baayen et al. (2017), Baayen et al. (2016), Wieling et al. (2011), Nixon et al. (2016), Hendrix et al. (2016), Koesling et al. (2012), Wieling et al. (2016), Wieling et al. (2014), Tremblay and Newman (2014), Tomaschek et al. (2017);
ordinal regression: Baayen and Divjak (2017), Kapatsinski et al. (2017); quantile regression: Koenker (2005), Fasiolo et al. (2017), Baayen (2017) (chapter 7);
survival analysis: Scheike and Martinussen (2007), Scheike and Zhang (2011), Schmidtke
et al. (2017), Baayen (2017) (chapter 8).

Full references list


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.11.17
29.11.17
Wednesday
13:45
17:00
130 in B6, 30-32
26.01.18
Friday
13:00
17:00
130 in B6, 30-32

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

Elections are the central focus of political activity in democracies. The characteristics of politics, parties and electoral systems are fundamental to the outcome of elections, which differ across and within countries. To better understand elections we need to study them comparatively, therefore this course focuses on comparative research on elections. We will focus on the context in which elections are fought and how this affects electoral outcomes. A number of contextual effects of electoral behaviour will be covered, such as institutional configurations, election campaigns, the strategies of political parties and the importance of events in understanding the dynamics of electoral outcomes. We will consider competing theoretical and empirical explanations of the electoral process in democratic, as well as partially democratic and even non-democratic, countries.

 

Literature:

Collier, Paul. 2010. Wars, Guns, and Votes: Democracy in Dangerous Places. Harper Collins.

Farrell, David. 2011. Electoral Systems: A Comparative Introduction 2nd. ed. Palgrave Macmillan.

LeDuc, Lawrence, Richard Niemi & Pippa Norris. 2014. Comparing Democracies: Elections and Voting in a Changing World, 4th. ed. Sage.

Thomassen, Jacques. 2014. Elections and Democracy: Representation and Accountability. Oxford University Press.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
05.09.17
05.12.17
Tuesday
12:00
13:30
A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

This seminar examins the political economy of European integration. We start with the constitutional development by treaty amendments, continue with the institutional design and the recent trend of Euroscepticism.

Regular attendance and a presentation is expected.

Assessment: term paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.09.17
07.12.17
Thursday
12:00
13:00
B318 in A5,6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

This seminar focuses on the outbreak and the dynamics of violent conflict as well as ways to end wars and sustain peace and stability. We will start by looking at the concepts of "peace" and "war" and examine empirical trends. Is the world more violent or more peaceful than in the past? How can we measure peace and conflict? In the following we will take a closer look at the relationship between religion and war, as well as economics and war. Does religion cause war? Or are economic factors more important for explaining why people fight? We will devote sessions to study terrorism as an alternative way of fighting (as opposed to conventional and civil wars), migration as a source of peace and conflict and the various forms of violence used by different militant groups. Why are some groups so much more violent than others? What is the rational behind using different forms of violence?  Finally we will have a look at empirical evidence concerning ways to end conflicts and sustain peace. Are peace missions for example an effective tool to make and sustain peace? Does development assistance foster development? The seminar is designed to provide MA-students with an in-depth insight into peace and conflict research. We will thereby answer (if possible) practical questions with scientific examinations and evidence.

  • Regular attendance & active class participation
  • Class presentation & leading the class discussion. Every student is obliged to discuss his or her presentation one week in advance during my office ours with me.
  • Assessment : research paper

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
04.09.17
04.12.17
Monday
12:00
13:30
A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science and sociology. CDSS faculty members will present an overview of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

Homework assignment (3 pages)

Talk schedule


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.09.17
Tuesday
08:30
11:45
308 in L9, 7
13.09.17
Wednesday
10:15
13:30
409 in L9, 1-2
13.10.17
Friday
10:15
13:30
308 in L9, 7
18.10.17
Wednesday
09:30
12:35
409 in L9, 1-2
24.10.17
Tuesday
08:30
11:45
409 in L9, 1-2

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

In recent decades, applications of statistics and formal modeling have become part of the main stream in the social sciences. Their contribution to our fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

The exam is scheduled for 12 December from 10.15-12.15am in room 211 in B6, 30-32.

Basic readings:

  • Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall


Additional readings:

  • Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
  • Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
  • Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
The course will not take place on 18 October!
06.09.17
06.12.17
Wednesday
08:30
10:00
A 305 Seminarraum in B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

All researchers face similar challenges with core issues of research design. A research design is a plan that specifies how you are going to carry out a research project and, particularly, how to use evidence to answer your research question. The goal of this course is to jump-start students with their dissertation proposal. This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.09.17
05.12.17
Tuesday
12:00
13:30
B243 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

This course begins with an overview and investigation of the types of explanations, and therefore the types of theory, that are developed and applied in the social sciences. This part of the course will have an emphasis on reading and discussion. The goal will be to form an integrated picture of theory, explanation, and the role of methods in the social sciences. We will therefore, whenever possible, connect these debates to the formal and practical aspects of research and methods. We will take a strongly interdisciplinary approach that includes discussion of fields outside the social sciences in order to give some comparative perspective on theory and explanation.

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We will operate largely within the unitary formal account of causation and causal inference that has emerged over the last decade in large part due to the work of Pearl and Rubin. Within this conceptual framework we will investigate experiments, both designed and natural, conditioning methods such as regression, and matching as complementary ways to identify causal effects. This part of the course will emphasize the applied computational aspects of these techniques. We will briefly discuss alternatives to the framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
04.09.17
16.10.17
Monday
14:00
18:00
P044 in L7, 3-5


Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

Please check with individual chairs in the Psychology Department for dates and times of research colloquia as well as registration.


Lecturer(s)


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
04.09.17
04.12.17
Monday
15:30
17:00
EO 259


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

You should be familiar with the basics of regression models and maximum likelihood estimation. No previous knowledge of software for Bayesian inference is required. However, we will use R as a front-end to JAGS and for graphical displays. Resources to learn R basics are UCLA’s Stat Consulting Site as well as the official manuals at http: //www.r-project.org/.


Course Content

This course introduces and extends the classical “workhorse” social science models – linear, logit, probit models and their multilevel extensions – from a Bayesian perspective.
The Bayesian approach to inference has attracted considerable attention in recent years. Mostly this is due to the increasingly complex models that it allows to fit. However, one might easily overlook the benefits that a Bayesian approach provides when estimating “standard” generalized linear models.
The course will introduce the basics of Bayesian inference, showing how its interpretation of probability differs from the classical approach and how it is actually closer to how social scientists think about their models. The course then introduces generalized linear models and shows how they can be easily fitted using modern software for Bayesian inference. It introduces Bayesian model diagnostics and fit measures, which allow straightforward model comparisons and examination of model misspecification.
The focus of the course will be on how to compute interesting quantities from those models, like predicted values or first differences in expected values for a changing covariate. Using the Bayesian approach to inference, their calculation is straightforward and one can easily construct appealing graphical displays.

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
30.10.17
04.12.17
Monday
14:00
18:00
308 in L9, 7


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to a social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to first the maximum likelihood estimator and second to nonlinear models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes.

Literature:

  • Fox, John (1997). Applied regression analysis, linear models and related methods. London: Sage.
  • Greene, William H. (2003). Econometric analysis. 5. Auflage. Upper Saddle River: Prentice Hall.
  • Gujarati, Damodar N. (2003). Basic econometrics. 4. Auflage. Boston: McGraw-Hill.
  • Long, J. Scott (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.
  • Verbeek, Marno (2004). A guide to modern econometrics. 2. Auflage. Chichester: Wiley.

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
13:45
15:15
B 317 in A5,6 entrance B
Tutorial
05.09.17
05.12.17
Tuesday
15:30
17:00
C-108 (PC Lab) in A5,6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

This course gives an overview of the design and implementation of survey questionnaires from the operationalization of the research questions to their implementation in a full questionnaire. Topics covered include operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, ordering the questionnaire, the effect of survey modes and questionnaire design in cross-cultural research. The course will be taught in a mix of seminar-style sessions, where the literature on questionnaire design is presented and discussed, and hands-on practical sessions, where students design and test survey questions.

15 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
15 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B

29 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
29 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B
29 Sep 13:45-17:00 either in room B 143 or B 243 in A5, 6 entrance B (the group will be split)


Tutorial

Data Collection:Interviewers and Interview Effects


Interviewers occupy a central role in the implementation of face-to-face and telephone surveys. We now know that interviewers affect the survey process in various ways, both positively (e.g., increasing response rates) and negatively (e.g., introducing measurement errors). The Total Survey Error (TSE) framework describes the different ways that interviewers can affect the survey process. Interviewers can have variable effects on the coverage of the sampling frame during listing and screening operations, rates of contact and cooperation (nonresponse), the answers that cooperating respondents provide (measurement), and the coding and editing of the information provided (processing). This course considers the roles that interviewers play during the survey process and and reviews key literature on interviewer effects on bias and variance in survey estimates.

30 Sep 10:15-17:00 in room B 317 in A5, 6 entrance B
30 Sep 13:45-17:00 in room B 244 in A5, 6 entrance B

10. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
10. Nov 10:15-11:45 in room B 318 in A5, 6 entrance B
10. Nov 12:00-17:00 in room B 244 in A5, 6 entrance B

17. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
17. Nov 10:15-17:00 in room C-108 Methods lab in A5, 6 entrance C


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - 15 & 29 Sep
15.09.17
08:30
17:00
various
Tutorial
irregular - 30 Sep, 10 & 17 Nov
30.09.17
Friday & Saturday
08:30
00:00
various


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Data visualization is one of the most powerful tools to explore, understand and communicate
patterns in quantitative information. At the same time, good data visualization is a
surprisingly difficult task and demands three quite different skills: substantive knowledge,
statistical skill, and artistic sense. The course is intended to introduce participants to a) key
principles of graphical perception and analytic design, b) useful visualization techniques for
the exploration and presentation of univariate, multivariate, time series and geographic data
and c) new developments of data visualization for the social sciences, such as interactive data
visualization, visual inference, and visualizing statistical models. This course is highly applied
in nature and emphasizes the practical aspects of data visualization in the social sciences.
Students will learn how to evaluate data visualizations based on principles of analytic design,
how to construct compelling visualizations using the free statistics software R, and how to
explore and present their data and models with visual methods. In short, students will get
hands-on experience producing modern visualizations for their practical problems. This will
be especially helpful to those students with own datasets related to their research.

Suggested Reading

  • Few, Stephen (2009). Now you see it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
  • Tufte, Edward (2001). The Visual Display of Quantitative Information. (Second Edition).Graphics Press.
  • Murrell, Paul (2011). R Graphics. (Second Edition). Chapman & Hall/CRC.Unwin, Anthony (2015). Graphical Data Analysis with R. CRC Press.


Rough Course Outline

Part I: Foundations and Principles of Data Visualisation

Intro

  • Statistical Graphs vs. InfoVis
  • A Brief History of Visualisation
  • Graphs vs. Tables
  • Basic Graphs in R

Data Visualisation as a Methodology

  • Exploration vs. Presentation
  • Comparison, Comparison
  • Component Graph Design in R

Graphical Perception

  • Attributes of Preattentive Processing
  • Gestalt Principles of Visual Perception
  • General Graph Design

Part II: Tools and Methods of Data Visualisation

Visualisation of Bivariate and Time Series Data

  • Enhancing Scatterplots
  • Overplotting Reduction
  • loess: Scatterplot Smoothers
  • Slope Graphs

Visualisation of Multivariate Data

  • Small Multiples
  • Mosaic Plots
  • Heatmaps
  • Parallel Coordinate Plots

Visualisation of Geographical Data

  • Map Variations (Choropleth, Dot, Flow)
  • Linked Micro Maps


Part III: New Developments and Extensions of Data Visualization

Interactive Data Visualisation

  • Selection and Identification
  • Zooming and Filtering
  • Highlighting and Linking
  • Shiny

Visual Inference

  • Is what we see really “there”?
  • Simulation Inference
  • The Line-up Protocol

Visualisation of Statistical Models

  • Graphs instead of Regression Tables
  • Visualising Quantities of Interest
  • Visualising Inferential Uncertainty
  • Visual Diagnostics & Model Checking

Student Visualisation Projects


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
21.09.17
09.11.17
Thursday
12:00
15:15
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.

In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

Graded assignments include homeworks, a mid-term exam and data analysis projects.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
08:30
10:00
B244 in A5,6 entrance B
Tutorial
Sternberg, Sebastian
04.09.17
04.12.17
Monday
12:00
13:30
Room C -108 (PC Lab) in A5,6, entrance C
Neunhoeffer, Marcel
07.09.17
07.12.17
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Literature will be given during the course

Course achievement - regular participation of the course; non-graded test

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
08.09.17
01.12.17
Friday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of Freedman (2005, see below) or equivalent. Familiarity with notions of research design in the social sciences and the statistical package Stata or R.

If you need to review material on regression models, please consult this excellent textbook: Freedman, David. 2005. Statistical Models: Theory and Practice. Cambridge University Press.


Course Content

This course teaches the fundamental concepts behind the estimation of causal effects, including potential obstacles to causal inference, faulty measurement, spuriousness, specification errors, and other problems that can lead to inappropriate causal inferences. We will discuss the benefits and the difficulties of randomization in survey research in the first half of the class. The focus of the second half is on the design of observational studies. Real-world examples will be discussed, with an emphasis on examples from survey methodology. Students will come away with an understanding of how to estimate causal effects in both randomized and observational settings, with a particular focus on careful design of both types of studies.

There will be several assignments/Exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
13:45
15:15
B 244 in A5, 6 entrance B
Tutorial
07.09.17
07.12.17
Thursday
15:30
17:00
B 143 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Basic knowledge of Bayesian statistics.


Course Content

Diverse practical aspects of stochastic simulation in the Social Sciences. Specific content is adapted according to  student requests and presented in a mixture of a lecture, seminar and practical course.


Competences acquired

Deeper understanding of techniques of stochastic simulation and their application in the Social Sciences


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.17
06.12.17
Wednesday
15:30
17:00
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET/POL

Credits: 10

Prerequisites

Any intro into Game Theory.


Course Content

This course is a continuation of the intro into Game Theory and covers advanced topics in game theory with a particular emphasis on the link of theories, methods and empirics. At the core, we discuss techniques used to analyze settings of imperfect information and  covered topics include normal form games with incomplete information and Bayesian equilibrium, stochastic games and Markov-perfect equilibrium, behavioral game theory and quantal-response equilibrium, signalling games and cheap talk, information transmission, mechanism design, comparative statics, monotone comparative statics and structural estimation.

Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. As this is a seminar, the course allows students to pursue areas of individual interest in more depth, and therefore course content is to some extent determined based on the interests of the students. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

Academic assessment: term paper

Literatur

  • Tadelis, Steven. 2013. Game Theory, An Introduction. Princeton, NJ: Princeton University Press.
  • Gehlbach, Scott. 2013. Formal Models of Domestic Politics. Cambridge: Cambridge University Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
10:15
11:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Course Content

Game theory and other formal modelling techniques are powerful methodological tools that are widely employed in political science and the social sciences, in general. The associated mathematics and notation can, nevertheless, be bewildering and frustrating to the newcomer. This course exposes students to the mechanics of a variety of formal models used in political sciences, showing them the underlying logic of these models, as well as the surrounding notation and mathematics. The overall aim of the course is to put students in a position where they can more effectively read literature that employs game theoretical modelling, and actually make use of formal modelling techniques in their own work.

Literature
McCarty, Nolan/Adam Meirowitz, 2007, Political Game Theory. Cambridge: Cambridge University Press

Tutorial

The tutorial accompanies the graduate-level introductory lecture in game theory. Its main objective
is to practice solution concepts for static and dynamic games of complete and incomplete information.
The contents are centered around the material covered in the lecture. Thus, the following key areas will
be discussed: preferences and individual choices, decision theory, normal form games, Nash equilibria,
extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete
and imperfect information, Bayesian perfect equilibria, signalling games. At the substantial level, we
will use these concepts to study, for instance, candidate competition, political lobbying, and war and
deterrence. Students are required to submit weekly problem sets. Moreover, active participation in
class discussions is expected.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
04.12.17
Monday
10:15
11:45
B 317 in A5,6 entrance B
Tutorial
06.09.17
06.12.17
Wednesday
17:15
18:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free, open-source, and cross-platform software for creating laboratory experiments. Many standard tasks can be implemented in OpenSesame via drag and drop using its graphical user interface. In addition, complex tasks can be realized through the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. Besides, the workshop will introduce plug-ins that extend OpenSesame for specific purposes, e.g., the psynteract plug-ins that implement real-time interactions between participants (as required in many economic games), and the mousetrap plug-ins that implement mouse-tracking during decision tasks (a method that is becoming increasingly popular in the cognitive sciences to measure preference development). Additional topics will be covered depending on the preferences of the workshop participants. No prior knowledge of the software or Python is required.

As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

Software:
OpenSesame can be downloaded for free under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.

Literature:
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. https://dx.doi.org/10.3758/s13428-011-0168-7

Henninger, F., Kieslich, P. J., & Hilbig, B. E. (in press). Psynteract: A flexible, cross-platform, open framework for interactive experiments. Behavior Research Methods, 1–10. https://doi.org/10.3758/s13428-016-0801-6

Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods, 1–16. https://doi.org/10.3758/s13428-017-0900-z


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
29.09.17
Friday
12:00
17:00
EO 162 CIP Pool
30.09.17
Saturday
10:15
17:00
EO 162 CIP-Pool
13.10.17
Friday
10:15
15:15
EO 162 CIP-Pool
14.10.17
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

The goal of this course is to familiarize students with a range of statistical techniques that are
available for the analysis of one response variable (e.g., reaction time, or pupil dilation, pitch, accuracy)
that is to be modeled as a function of one or more predictors. These predictors can be factors
(e.g., native versus non-native speakers), numerical covariates (e.g., frequency of occurrence), or
combinations of factors and covariates. Modeling techniques will be introduced conceptually, and
emphasis will be on worked examples of their application. Hands on sessions provide training in
applying statistical models to real data sets from linguistics and psychology. For this course to
be pro table to them, participants should be familiar with basic concepts from statistics (random
variables, quantiles, mean, variance, normal distribution, t-distribution, t-test, hypothesis testing,
con dence intervals). As the course will make use of the R statistical programming environment,
participants should bring their laptops with R installed, and should know how to install packages
and how to load data into R.

The course has four blocks.

1. The (generalized) linear mixed model. This model is widely used for data with response
variables collected from multiple subjects and multiple items. It allows the analyst to take
into account how uncertainty about model estimates varies with subjects and items. The
vexed question of how complex a model should be to be minimally adequate will be discussed
in detail.

2. The generalized additive model: basic concepts. The generalized additive model (GAM) relaxes
the assumption that the functional relation between the response and one or more
predictors is linear. It is ideal for modeling wiggly curves and wiggly (hyper)surfaces. Model
criticism and tools for dealing with model residuals that are not identically and independently
distributed will be introduced.

3. Extending the generalized additive model. Factors may interact with numeric predictors,
resulting in wiggly curves and wiggly surfaces that may have a di erent shape depending on
the levels of that factor. Two ways of assessing such interactions are discussed, including the
modeling of a di erence curve or a di erence surface. Furthermore, examples are provided of
how ordinal data can be modeled with GAMs.

4. Quantile regression and survival analysis. The generalized linear model and the generalized
additive model predict the expected value (the mean) of the response. However, it is often
of interest to know whether the e ect of predictors is di erent depending on which quantile
(other than the median) is modeled. Quantile regression with GAMs on the one hand, and
dynamic survival analysis on the other, provide very di erent and complementary approaches
for coming to grips with the full distribution of the response.

Final assignment

Selected references (including references to studies applying regression methods):

linear regression: Baayen (2008), Harrell (2015), Anscombe (1973), Friedman and Wall (2005),
Wurm and Fisicaro (2014), Baayen (2013), Baayen (2010);
generalized linear model: Donnelly and Verkuilen (2017), Jaeger (2008), Fasold (1991), Arppe (2011), Cedergren and Sanko (1974);
linear mixed model: Pinheiro and Bates (2000), Baayen et al. (2008), Ga lecki and Burzykowski
(2013), Janda et al. (2010), Barr et al. (2013), Gonzalez et al. (2014), Bates et al. (2015), Lele et al.
(2012), Matuschek et al. (2017), Johnson (2009);
random forests: Breiman et al. (1984), Breiman (2001), Strobl et al. (2009), Tagliamonte and Baayen (2012);
generalized additive model: Wood (2006), Baayen et al. (2017), Baayen et al. (2016), Wieling et al. (2011), Nixon et al. (2016), Hendrix et al. (2016), Koesling et al. (2012), Wieling et al. (2016), Wieling et al. (2014), Tremblay and Newman (2014), Tomaschek et al. (2017);
ordinal regression: Baayen and Divjak (2017), Kapatsinski et al. (2017); quantile regression: Koenker (2005), Fasiolo et al. (2017), Baayen (2017) (chapter 7);
survival analysis: Scheike and Martinussen (2007), Scheike and Zhang (2011), Schmidtke
et al. (2017), Baayen (2017) (chapter 8).

Full references list


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.11.17
29.11.17
Wednesday
13:45
17:00
130 in B6, 30-32
26.01.18
Friday
13:00
17:00
130 in B6, 30-32

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

This seminar is targeted at doctoral students and post-docs in Psychology.


Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
05.09.17
05.12.17
Tuesday
10:15
11:45
324, Parkring 47


Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

This lecture series will present cutting edge research conducted in cognitive psychology at the University of Mannheim. After an introductory overview of cognitive psychology and its advanced methods (i.e., cognitive modeling) by B. Kuhlmann, various researchers will present their current work including research on judgment and decision making, memory, metacognition, and cognitive aging. The following researchers are planned as lecturers (changes possible): Dr. Nina Arnold, Dr. Martin Brandt, Prof. A. Bröder, Prof. E. Erdfelder, Prof. B. Kuhlmann, Dr. Lena Naderevic, und Dr. Monika Undorf. All lectures and materials will be in English.

Final test: written exam.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
15:30
17:00
EO 145

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

Knowledge in work and organizational psychology (as acquired during bachelor studies). It is expected that students know the content of a text book such as Spector (2008) or Landy & Conte (2010).


Course Content

N.B. This fall term the course will be taught in German by Dr. Kühnel covering for Prof. Sonnentag.
The course will be offered in English again in the fall term 2018.

This course provides an overview of core topic within work and organizational psychology. We will focus on recent theoretical approaches and empirical research findings (meta-analyses). In addition, we will discuss practical implications of core research findings. Topics include: Work motivation, stress and health, leadership, teams, personnel selection.

Methods comprise: Lecture, reading (as homework), teamwork assignments during class.

Literature

Journal papers; reading assignments will be given at the beginning of the semester.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
17:15
18:45
EO 242


Course Type: core course

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Colloquium
Keusch/Kreuter
04.09.17
04.12.17
Monday
12:00
13:30
tbc
Gautschi/Hillmann
05.09.17
05.12.17
Tuesday
15:30
17:00
tbc
Kalter/Kogan
05.09.17
05.12.17
Tuesday
15:30
17:00
A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The course "Current Research Perspectives" introduces first year doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science and sociology. CDSS faculty members will present an overview of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will have the opportunity to discuss the short talks with the respective lecturer during the remaining discussion time.

Homework assignment (3 pages)

Talk schedule


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.09.17
Tuesday
08:30
11:45
308 in L9, 7
13.09.17
Wednesday
10:15
13:30
409 in L9, 1-2
13.10.17
Friday
10:15
13:30
308 in L9, 7
18.10.17
Wednesday
09:30
12:35
409 in L9, 1-2
24.10.17
Tuesday
08:30
11:45
409 in L9, 1-2

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

In recent decades, applications of statistics and formal modeling have become part of the main stream in the social sciences. Their contribution to our fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

The exam is scheduled for 12 December from 10.15-12.15am in room 211 in B6, 30-32.

Basic readings:

  • Knut Sydsaeter and Peter Hammond. 2008. Essential Mathematics for Economic Analysis. 3rd edition. Harlow: Prentice Hall


Additional readings:

  • Alpha C. Chiang and Kevin Wainwright. 2005. Fundamental Methods of Mathematical Economics. 4th edition. Boston, Mass.: McGraw-Hill
  • Jeff Gill. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
  • Malcolm Pemberton and Nicholas Rau. 2007. Mathematics for Economists. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
The course will not take place on 18 October!
06.09.17
06.12.17
Wednesday
08:30
10:00
A 305 Seminarraum in B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

All researchers face similar challenges with core issues of research design. A research design is a plan that specifies how you are going to carry out a research project and, particularly, how to use evidence to answer your research question. The goal of this course is to jump-start students with their dissertation proposal. This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.09.17
05.12.17
Tuesday
12:00
13:30
B243 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

This course begins with an overview and investigation of the types of explanations, and therefore the types of theory, that are developed and applied in the social sciences. This part of the course will have an emphasis on reading and discussion. The goal will be to form an integrated picture of theory, explanation, and the role of methods in the social sciences. We will therefore, whenever possible, connect these debates to the formal and practical aspects of research and methods. We will take a strongly interdisciplinary approach that includes discussion of fields outside the social sciences in order to give some comparative perspective on theory and explanation.

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We will operate largely within the unitary formal account of causation and causal inference that has emerged over the last decade in large part due to the work of Pearl and Rubin. Within this conceptual framework we will investigate experiments, both designed and natural, conditioning methods such as regression, and matching as complementary ways to identify causal effects. This part of the course will emphasize the applied computational aspects of these techniques. We will briefly discuss alternatives to the framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
04.09.17
16.10.17
Monday
14:00
18:00
P044 in L7, 3-5


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
06.09.17
06.12.17
Wednesday
12:00
13:30
A 302 in B6, 23-25, entrance A


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: 4

Prerequisites

You should be familiar with the basics of regression models and maximum likelihood estimation. No previous knowledge of software for Bayesian inference is required. However, we will use R as a front-end to JAGS and for graphical displays. Resources to learn R basics are UCLA’s Stat Consulting Site as well as the official manuals at http: //www.r-project.org/.


Course Content

This course introduces and extends the classical “workhorse” social science models – linear, logit, probit models and their multilevel extensions – from a Bayesian perspective.
The Bayesian approach to inference has attracted considerable attention in recent years. Mostly this is due to the increasingly complex models that it allows to fit. However, one might easily overlook the benefits that a Bayesian approach provides when estimating “standard” generalized linear models.
The course will introduce the basics of Bayesian inference, showing how its interpretation of probability differs from the classical approach and how it is actually closer to how social scientists think about their models. The course then introduces generalized linear models and shows how they can be easily fitted using modern software for Bayesian inference. It introduces Bayesian model diagnostics and fit measures, which allow straightforward model comparisons and examination of model misspecification.
The focus of the course will be on how to compute interesting quantities from those models, like predicted values or first differences in expected values for a changing covariate. Using the Bayesian approach to inference, their calculation is straightforward and one can easily construct appealing graphical displays.

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
30.10.17
04.12.17
Monday
14:00
18:00
308 in L9, 7


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to a social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to first the maximum likelihood estimator and second to nonlinear models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes.

Literature:

  • Fox, John (1997). Applied regression analysis, linear models and related methods. London: Sage.
  • Greene, William H. (2003). Econometric analysis. 5. Auflage. Upper Saddle River: Prentice Hall.
  • Gujarati, Damodar N. (2003). Basic econometrics. 4. Auflage. Boston: McGraw-Hill.
  • Long, J. Scott (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.
  • Verbeek, Marno (2004). A guide to modern econometrics. 2. Auflage. Chichester: Wiley.

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
13:45
15:15
B 317 in A5,6 entrance B
Tutorial
05.09.17
05.12.17
Tuesday
15:30
17:00
C-108 (PC Lab) in A5,6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

This course gives an overview of the design and implementation of survey questionnaires from the operationalization of the research questions to their implementation in a full questionnaire. Topics covered include operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, ordering the questionnaire, the effect of survey modes and questionnaire design in cross-cultural research. The course will be taught in a mix of seminar-style sessions, where the literature on questionnaire design is presented and discussed, and hands-on practical sessions, where students design and test survey questions.

15 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
15 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B

29 Sep 08:30-10:00 in room B 143 in A5, 6 entrance B
29 Sep 10:15- 13:30 in room B 317 in A5, 6 entrance B
29 Sep 13:45-17:00 either in room B 143 or B 243 in A5, 6 entrance B (the group will be split)


Tutorial

Data Collection:Interviewers and Interview Effects


Interviewers occupy a central role in the implementation of face-to-face and telephone surveys. We now know that interviewers affect the survey process in various ways, both positively (e.g., increasing response rates) and negatively (e.g., introducing measurement errors). The Total Survey Error (TSE) framework describes the different ways that interviewers can affect the survey process. Interviewers can have variable effects on the coverage of the sampling frame during listing and screening operations, rates of contact and cooperation (nonresponse), the answers that cooperating respondents provide (measurement), and the coding and editing of the information provided (processing). This course considers the roles that interviewers play during the survey process and and reviews key literature on interviewer effects on bias and variance in survey estimates.

30 Sep 10:15-17:00 in room B 317 in A5, 6 entrance B
30 Sep 13:45-17:00 in room B 244 in A5, 6 entrance B

10. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
10. Nov 10:15-11:45 in room B 318 in A5, 6 entrance B
10. Nov 12:00-17:00 in room B 244 in A5, 6 entrance B

17. Nov 08:30-10:00 in room B 143 in A5, 6 entrance B
17. Nov 10:15-17:00 in room C-108 Methods lab in A5, 6 entrance C


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - 15 & 29 Sep
15.09.17
08:30
17:00
various
Tutorial
irregular - 30 Sep, 10 & 17 Nov
30.09.17
Friday & Saturday
08:30
00:00
various


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Data visualization is one of the most powerful tools to explore, understand and communicate
patterns in quantitative information. At the same time, good data visualization is a
surprisingly difficult task and demands three quite different skills: substantive knowledge,
statistical skill, and artistic sense. The course is intended to introduce participants to a) key
principles of graphical perception and analytic design, b) useful visualization techniques for
the exploration and presentation of univariate, multivariate, time series and geographic data
and c) new developments of data visualization for the social sciences, such as interactive data
visualization, visual inference, and visualizing statistical models. This course is highly applied
in nature and emphasizes the practical aspects of data visualization in the social sciences.
Students will learn how to evaluate data visualizations based on principles of analytic design,
how to construct compelling visualizations using the free statistics software R, and how to
explore and present their data and models with visual methods. In short, students will get
hands-on experience producing modern visualizations for their practical problems. This will
be especially helpful to those students with own datasets related to their research.

Suggested Reading

  • Few, Stephen (2009). Now you see it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
  • Tufte, Edward (2001). The Visual Display of Quantitative Information. (Second Edition).Graphics Press.
  • Murrell, Paul (2011). R Graphics. (Second Edition). Chapman & Hall/CRC.Unwin, Anthony (2015). Graphical Data Analysis with R. CRC Press.


Rough Course Outline

Part I: Foundations and Principles of Data Visualisation

Intro

  • Statistical Graphs vs. InfoVis
  • A Brief History of Visualisation
  • Graphs vs. Tables
  • Basic Graphs in R

Data Visualisation as a Methodology

  • Exploration vs. Presentation
  • Comparison, Comparison
  • Component Graph Design in R

Graphical Perception

  • Attributes of Preattentive Processing
  • Gestalt Principles of Visual Perception
  • General Graph Design

Part II: Tools and Methods of Data Visualisation

Visualisation of Bivariate and Time Series Data

  • Enhancing Scatterplots
  • Overplotting Reduction
  • loess: Scatterplot Smoothers
  • Slope Graphs

Visualisation of Multivariate Data

  • Small Multiples
  • Mosaic Plots
  • Heatmaps
  • Parallel Coordinate Plots

Visualisation of Geographical Data

  • Map Variations (Choropleth, Dot, Flow)
  • Linked Micro Maps


Part III: New Developments and Extensions of Data Visualization

Interactive Data Visualisation

  • Selection and Identification
  • Zooming and Filtering
  • Highlighting and Linking
  • Shiny

Visual Inference

  • Is what we see really “there”?
  • Simulation Inference
  • The Line-up Protocol

Visualisation of Statistical Models

  • Graphs instead of Regression Tables
  • Visualising Quantities of Interest
  • Visualising Inferential Uncertainty
  • Visual Diagnostics & Model Checking

Student Visualisation Projects


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
21.09.17
09.11.17
Thursday
12:00
15:15
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

The course introduces students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to understand the logic of statistical inference, to recognize and understand the basics of the linear regression model, to develop the skills necessary to work with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for more advanced statistical methods.

In the accompanying course "Tutorial Multivariate Analyses" students will develop the necessary expertise in using statistical software to conduct quantitative research in political science.

Graded assignments include homeworks, a mid-term exam and data analysis projects.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
08:30
10:00
B244 in A5,6 entrance B
Tutorial
Sternberg, Sebastian
04.09.17
04.12.17
Monday
12:00
13:30
Room C -108 (PC Lab) in A5,6, entrance C
Neunhoeffer, Marcel
07.09.17
07.12.17
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The software R is a computer programming language designed for statistical analysis and graphics. The first part of the course deals with a basic introduction to R, i.e. data handling, basic statistical analyses, the creation of graphics, and linear modeling including test for specially designed hypotheses. In the second part we use R as a programming language for cognitive modeling. We will simulate data based on mathematical models of cognitive functions and analyze these data with maximum likelihood parameter estimation techniques. At the end, I will introduce some advanced techniques, for example the creation of statistical reports with R.
The software package R is free and available on all major platforms (www.r-project.org). I also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under:

http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Literature will be given during the course

Course achievement - regular participation of the course; non-graded test

Academic assessment - graded homework


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
08.09.17
01.12.17
Friday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Knowledge of multiple linear regression and some familiarity with generalised linear models, to the level of Freedman (2005, see below) or equivalent. Familiarity with notions of research design in the social sciences and the statistical package Stata or R.

If you need to review material on regression models, please consult this excellent textbook: Freedman, David. 2005. Statistical Models: Theory and Practice. Cambridge University Press.


Course Content

This course teaches the fundamental concepts behind the estimation of causal effects, including potential obstacles to causal inference, faulty measurement, spuriousness, specification errors, and other problems that can lead to inappropriate causal inferences. We will discuss the benefits and the difficulties of randomization in survey research in the first half of the class. The focus of the second half is on the design of observational studies. Real-world examples will be discussed, with an emphasis on examples from survey methodology. Students will come away with an understanding of how to estimate causal effects in both randomized and observational settings, with a particular focus on careful design of both types of studies.

There will be several assignments/Exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
13:45
15:15
B 244 in A5, 6 entrance B
Tutorial
07.09.17
07.12.17
Thursday
15:30
17:00
B 143 in A5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 5

Prerequisites

Basic knowledge of Bayesian statistics.


Course Content

Diverse practical aspects of stochastic simulation in the Social Sciences. Specific content is adapted according to  student requests and presented in a mixture of a lecture, seminar and practical course.


Competences acquired

Deeper understanding of techniques of stochastic simulation and their application in the Social Sciences


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.17
06.12.17
Wednesday
15:30
17:00
A 305 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET/POL

Credits: 10

Prerequisites

Any intro into Game Theory.


Course Content

This course is a continuation of the intro into Game Theory and covers advanced topics in game theory with a particular emphasis on the link of theories, methods and empirics. At the core, we discuss techniques used to analyze settings of imperfect information and  covered topics include normal form games with incomplete information and Bayesian equilibrium, stochastic games and Markov-perfect equilibrium, behavioral game theory and quantal-response equilibrium, signalling games and cheap talk, information transmission, mechanism design, comparative statics, monotone comparative statics and structural estimation.

Emphasis will be placed on prominent applications of those concepts in political science, in both comparative and international politics. As this is a seminar, the course allows students to pursue areas of individual interest in more depth, and therefore course content is to some extent determined based on the interests of the students. The course is partly taught from lecture notes, at other times students present a research paper and stimulate discussion in class.

Academic assessment: term paper

Literatur

  • Tadelis, Steven. 2013. Game Theory, An Introduction. Princeton, NJ: Princeton University Press.
  • Gehlbach, Scott. 2013. Formal Models of Domestic Politics. Cambridge: Cambridge University Press.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
10:15
11:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Course Content

Game theory and other formal modelling techniques are powerful methodological tools that are widely employed in political science and the social sciences, in general. The associated mathematics and notation can, nevertheless, be bewildering and frustrating to the newcomer. This course exposes students to the mechanics of a variety of formal models used in political sciences, showing them the underlying logic of these models, as well as the surrounding notation and mathematics. The overall aim of the course is to put students in a position where they can more effectively read literature that employs game theoretical modelling, and actually make use of formal modelling techniques in their own work.

Literature
McCarty, Nolan/Adam Meirowitz, 2007, Political Game Theory. Cambridge: Cambridge University Press

Tutorial

The tutorial accompanies the graduate-level introductory lecture in game theory. Its main objective
is to practice solution concepts for static and dynamic games of complete and incomplete information.
The contents are centered around the material covered in the lecture. Thus, the following key areas will
be discussed: preferences and individual choices, decision theory, normal form games, Nash equilibria,
extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete
and imperfect information, Bayesian perfect equilibria, signalling games. At the substantial level, we
will use these concepts to study, for instance, candidate competition, political lobbying, and war and
deterrence. Students are required to submit weekly problem sets. Moreover, active participation in
class discussions is expected.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
04.12.17
Monday
10:15
11:45
B 317 in A5,6 entrance B
Tutorial
06.09.17
06.12.17
Wednesday
17:15
18:45
B 317 in A5,6 entrance B


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free, open-source, and cross-platform software for creating laboratory experiments. Many standard tasks can be implemented in OpenSesame via drag and drop using its graphical user interface. In addition, complex tasks can be realized through the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. In doing so, the workshop involves both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. Besides, the workshop will introduce plug-ins that extend OpenSesame for specific purposes, e.g., the psynteract plug-ins that implement real-time interactions between participants (as required in many economic games), and the mousetrap plug-ins that implement mouse-tracking during decision tasks (a method that is becoming increasingly popular in the cognitive sciences to measure preference development). Additional topics will be covered depending on the preferences of the workshop participants. No prior knowledge of the software or Python is required.

As an assignment, participants will create their own experiment based on the requirements discussed in the workshop.

Software:
OpenSesame can be downloaded for free under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.

Literature:
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. https://dx.doi.org/10.3758/s13428-011-0168-7

Henninger, F., Kieslich, P. J., & Hilbig, B. E. (in press). Psynteract: A flexible, cross-platform, open framework for interactive experiments. Behavior Research Methods, 1–10. https://doi.org/10.3758/s13428-016-0801-6

Kieslich, P. J., & Henninger, F. (2017). Mousetrap: An integrated, open-source mouse-tracking package. Behavior Research Methods, 1–16. https://doi.org/10.3758/s13428-017-0900-z


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
29.09.17
Friday
12:00
17:00
EO 162 CIP Pool
30.09.17
Saturday
10:15
17:00
EO 162 CIP-Pool
13.10.17
Friday
10:15
15:15
EO 162 CIP-Pool
14.10.17
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

The goal of this course is to familiarize students with a range of statistical techniques that are
available for the analysis of one response variable (e.g., reaction time, or pupil dilation, pitch, accuracy)
that is to be modeled as a function of one or more predictors. These predictors can be factors
(e.g., native versus non-native speakers), numerical covariates (e.g., frequency of occurrence), or
combinations of factors and covariates. Modeling techniques will be introduced conceptually, and
emphasis will be on worked examples of their application. Hands on sessions provide training in
applying statistical models to real data sets from linguistics and psychology. For this course to
be pro table to them, participants should be familiar with basic concepts from statistics (random
variables, quantiles, mean, variance, normal distribution, t-distribution, t-test, hypothesis testing,
con dence intervals). As the course will make use of the R statistical programming environment,
participants should bring their laptops with R installed, and should know how to install packages
and how to load data into R.

The course has four blocks.

1. The (generalized) linear mixed model. This model is widely used for data with response
variables collected from multiple subjects and multiple items. It allows the analyst to take
into account how uncertainty about model estimates varies with subjects and items. The
vexed question of how complex a model should be to be minimally adequate will be discussed
in detail.

2. The generalized additive model: basic concepts. The generalized additive model (GAM) relaxes
the assumption that the functional relation between the response and one or more
predictors is linear. It is ideal for modeling wiggly curves and wiggly (hyper)surfaces. Model
criticism and tools for dealing with model residuals that are not identically and independently
distributed will be introduced.

3. Extending the generalized additive model. Factors may interact with numeric predictors,
resulting in wiggly curves and wiggly surfaces that may have a di erent shape depending on
the levels of that factor. Two ways of assessing such interactions are discussed, including the
modeling of a di erence curve or a di erence surface. Furthermore, examples are provided of
how ordinal data can be modeled with GAMs.

4. Quantile regression and survival analysis. The generalized linear model and the generalized
additive model predict the expected value (the mean) of the response. However, it is often
of interest to know whether the e ect of predictors is di erent depending on which quantile
(other than the median) is modeled. Quantile regression with GAMs on the one hand, and
dynamic survival analysis on the other, provide very di erent and complementary approaches
for coming to grips with the full distribution of the response.

Final assignment

Selected references (including references to studies applying regression methods):

linear regression: Baayen (2008), Harrell (2015), Anscombe (1973), Friedman and Wall (2005),
Wurm and Fisicaro (2014), Baayen (2013), Baayen (2010);
generalized linear model: Donnelly and Verkuilen (2017), Jaeger (2008), Fasold (1991), Arppe (2011), Cedergren and Sanko (1974);
linear mixed model: Pinheiro and Bates (2000), Baayen et al. (2008), Ga lecki and Burzykowski
(2013), Janda et al. (2010), Barr et al. (2013), Gonzalez et al. (2014), Bates et al. (2015), Lele et al.
(2012), Matuschek et al. (2017), Johnson (2009);
random forests: Breiman et al. (1984), Breiman (2001), Strobl et al. (2009), Tagliamonte and Baayen (2012);
generalized additive model: Wood (2006), Baayen et al. (2017), Baayen et al. (2016), Wieling et al. (2011), Nixon et al. (2016), Hendrix et al. (2016), Koesling et al. (2012), Wieling et al. (2016), Wieling et al. (2014), Tremblay and Newman (2014), Tomaschek et al. (2017);
ordinal regression: Baayen and Divjak (2017), Kapatsinski et al. (2017); quantile regression: Koenker (2005), Fasiolo et al. (2017), Baayen (2017) (chapter 7);
survival analysis: Scheike and Martinussen (2007), Scheike and Zhang (2011), Schmidtke
et al. (2017), Baayen (2017) (chapter 8).

Full references list


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
08.11.17
29.11.17
Wednesday
13:45
17:00
130 in B6, 30-32
26.01.18
Friday
13:00
17:00
130 in B6, 30-32

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

This course uncovers organizational arrangements of expert knowledge and asks how arcane expertise shapes public and private life. We investigate this relationship in three problem areas: (1) Bureaucratic administration, including law and economics, as well as the EU and WTO; (2) health and science, including medicine, mental health, autism and HIV; and (3) technology and the Internet, including power plants, programmers and artificial intelligence. Across these empirical settings we analyze different processes by which abstract knowledge gains lay salience: Informal relationships between mentors and students, or doctors and patients; formal organizations, ranging from labs and firms to governments and NGOs; and occupations, which regulate medical doctors, architects and others; and we finally ask whether expertise could unfold systematically outside of specific relationships and institutionalized boundaries, such as in open discourse and arguments. On the basis of these processes we aim to understand today’s transition from bureaucratic to technological contexts of knowledge production and application.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
06.09.17
06.12.17
Wednesday
10:15
11:45
B143 in A 5, 6 entrance B

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
07.09.17
07.12.17
Thursday
10:15
11:45
A102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Prerequisites

Knowledge of basic concepts and theoretical models in sociology, in particular in the field of social stratification and social inequality, is beneficial. In addition sound methodological competencies are advantageous.


Course Content

Content: Sex and gender belong to the most basic categories recognized in all human societies.
Also sex and gender affect all aspects of human life. This course will focus on how sex and
gender are related to and affect main dimensions of social inequality, namely education, work,
income, and the welfare state. The course begins by introducing how social scientists
conceptualize sex and gender and ends with reflections on how to theorize these concepts.
Objectives: In this course you will learn how sex and gender shape social inequalities in
Western societies. You will be introduced to the main theories as well as to important findings
in this field. Through your course work, your presentation and your term paper you will gain
experience in writing scientific texts and presenting scientific results.
Organization: During each session topics will be introduced by the instructor and then specific
aspects will be highlighted in presentations by students (see dates below). Topics for
presentations can be arranged with the instructor either before the start of the course via email
or at the first session.
It is assumed that all participants read the literature given below, actively follow the course,
engage in discussions and do a presentation. The term paper should not exceed 5,000 words
and should be delivered electronically via e‐mail preferably in pfd‐format to the instructor.

Deadline for delivery of the term paper is midnight January 7, 2018.

Dates and topics

08.09. Concepts of sex, gender and gender roles (Davis/Greenstein 2009; Twenge 1997)

22.09. Theorizing gender (Walby et al. 2012; West/Zimmerman 1987)

06.10. Gender and education (Buchmann et al. 2008; Barone 2011)

20.10. Gender and paid work (Jarman et al. 2012; Stier/Yaish 2014)

10.11. Gender and housework (Bianchi et al. 2000; Neilson/Stanfors 2014)

17.11. Gender and income (Weichselbaumer/Winter‐Ebmer 2005; Cha/Weeden 2014)

08.12. Gender and the welfare state (Mandel 2012; Korpi et al. 2013)

Literature

  • Barone, Carlo. 2011. Some things never change: Gender segregation in higher education across eight nations and three decades. Sociology of Education 84:157‐176.
  • Bianchi, Suzanne M., Melissa A. Milkie, Liana C. Sayer and John P. Robinson. 2000. Is anyone doing the housework? Trends in the gender division of household labor. Social Forces 79:191‐228.
  • Buchmann, Claudia, Thomas A. Diprete and Anne McDaniel. 2008. Gender inequalities in education. Annual Review of Sociology 34:319‐337.
  • Cha, Youngjoo, and Kim A. Weeden. 2014. Overwork and the slow convergence in the gender gap in wages. American Social Review pre‐print.
  • Davis, Shannon, and Theodore N. Greenstein. 2009. Gender Ideology: Components, Predictors, and Consequences. Annual Review of Sociology 35:87–105.
  • Jarman, Jennifer, Robert M. Blackburn and Girts Racko. 2012. The dimensions of occupational gender segregation in industrial countries. Sociology 46:1003‐1019.
  • Korpi, Walter, Tommy Ferrarini and Stefan Englund. 2013. Women's opportunities under different family policy constellations: Gender, class, and inequality tradeoffs in western countries re‐examined. Social Politics 20:1‐40.
  • Mandel, Hadas. 2012. Winners and losers: The consequences of welfare state policies for gender wage inequality. European Sociological Review 28:241‐262.
  • Neilson, Jeffrey, and Maria Stanfors. 2014. It's about time! Gender, parenthood, and household division of labor under different welfare regimes. Journal of Family Issues 35:1066‐1088.
  • Stier, Haya, and Meir Yaish. 2014. Occupational segregation and gender inequality in job quality: a multi‐level approach. Work, Employment and Society 28:225‐246.
  • Twenge, Jean M. 1997. Changes in masculine and feminine traits over time: a meta‐analysis. Sex Roles 36:305‐325.
  • Walby, Sylvia, Jo Armstrong and Sofia Strid. 2012. Intersectionality: Multiple Inequalities in Social Theory. Sociology 46:224‐240.
  • Weichselbaumer, Doris, and Rudolf Winter‐Ebmer. 2005. A meta‐analysis of the international gender wage gap. Journal of Economic Surveys 19:479‐511.
  • West, Candace, and Don H. Zimmerman. 1987. Doing Gender. Gender & Society 1:125‐151.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - 8 & 22 Sep, 6 & 20 Oct, 10 & 17 Nov, 8 Dec
08.09.17
Fridays
10:15
13:30
B 317 in A 5, 6 entrance B

Register

Social Sciences Fall 2017

Dissertation Tutorial: Sociology (Gautschi&Hillmann / Keusch&Kreuter / Kogan&Kalter)
BAS
Current Research Perspectives
BAS
Mathematics for Social Scientists
MET
Crafting Social Science Research
MET
Theory Building and Causal Inference
RES
CDSS Workshop: Sociology
RES
MZES A Colloquium "European Societies and their Integration"
MET
Bayesian Statistics for Social Scientists I
MET
Cross Sectional Data Analysis (Lecture + Tutorial)
MET
Data and Measurement: Questionnaire Design and Implementation (Theory + Lab Course)
MET
Data Visualization for Social Scientists
MET
Multivariate Analyses (Theory + Lab Course)
MET
Programming in R and beyond
MET
Research Design (lecture & tutorial)
MET
Stochastic Simulation
MET/POL
Advanced Topics in Comparative Politics: Game Theory II
MET/POL
Game Theory (Theory + Tutorial)
MET/PSY
Creating experiments with OpenSesame
MET/PSY
Hierarchical Linear Models - Regression Modeling Strategies for the Analysis of Linguistic and Psycholinguistic Data
SOC
Economy & the Welfare State: Experts and public problems
SOC
Economy & the Welfare State: Organisational Theory
SOC
Family, Education & Labour Market: Sex / Gender as Dimension of Social Stratification
RES
CDSS Workshop: Political Science
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
MET
Computational Text Analysis for Political Science
POL
Advanced Topics in Comparative Politics: Elections in Comparative Perspective
POL
Advanced Topics in International Politics: The Political Economy of European Integration
POL
Advanced Topics in International Politics: Violence, conflict and the prospect for peace
RES
AC2/BC3 Colloquia I
RES
CDSS Workshop: Research in Psychology
PSY
Advanced Social and Economic Cognition
PSY
Advanced topics in Cognitive Psychology
PSY
Advanced Topics in Work and Organizational Psychology