Course Catalog

BAS: Mathematics for Social Scientists

Lecturer(s)

Thomas Gschwend

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 outline 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.

Assignment: Come-up with a research project idea that is informed theoretically or methodologically by insights from one or several of the presentations (outside of your particular field) in this class. Write-up your idea and describe a potential research design in a short 3-page paper. The paper is due October 1st.

Talk schedule

Schedule

Lecture
	06.09.19 – 13.09.19	Friday	08:30 – 11:45	211 in B 6, 30–32
	12.09.19	Thursday	08:30 – 11:45	211 in B 6, 30–32

MET: Crafting Social Science Research

Lecturer(s)

Anna-Sophie Kurella

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

It is increasingly important for modern social scientists to have a level of mathematical literacy, as mathematical research methods such as statistics and formal modelling have entered the main stream. This course is intended to provide an introduction to mathematical logic and rigour, and to some fundamental mathematical concepts that form the foundation of the modern subject. The course covers introductory set and function theory, including analysis of functions, and includes sections on both probability and linear algebra, which together are the basis of data analysis.

The exam is scheduled for 16 December from 10.30am to 12.30pm 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

Lecture
	04.10.19	Friday	10:15 – 13:30	211 in B 6, 30–32
	18.10.19	Friday	10:15 – 13:30	406 in B6, 30–32
	25.10.19	Friday	10:15 – 13:30	211 in B6, 30–32
	15.11.19 – 29.11.19	Friday	10:15 – 13:30	211 in B6, 30–32

MET: Theory Building and Causal Inference

Lecturer(s)

Thomas Gschwend

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

Workshop
	03.09.19 – 03.12.19	Tuesday	12:00 – 13:30	A 103 in B6, 23–25

RES: CDSS Workshop: Sociology

Lecturer(s)

Richard Traunmüller

Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

Schedule

Workshop
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	211 in B6, 30–32 on 17.10 in room 310 in B6, 30–32

2 or 3 depending on applicable study regulations ECTS

Lecturer(s)

Florian Keusch

Course Type: core course

Course Number: RES

Credits: 2 or 3 depending on applicable study regulations

Course Content

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

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

Schedule

Workshop
	11.09.19	Wednesday	15:30 – 17:00	C112 in A5, 6 entrance C
	16.10.19	Wednesday	17:00 – 19:00	310 in B6, 30–32
	06.11.19 – 13.11.19	Wednesday	17:00 – 19:00	310 in B6, 30–32
	27.11.19	Wednesday	17:00 – 19:00	310 in B6, 30–32

RES: MZES A Colloquium “European Societies and their Integration”

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.

MET: Data and Measurement (Lecture + Tutorial)

Lecturer(s)

Thomas Gautschi

Katrin Paula

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

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 rst the maximum likelihood estimator and second to generalized linear models (GLS) 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. We will use the statistical package Stata.

Assessment type (Prüfungsleistung): written exam (90 min)

Credits (9 ECTS) are awarded on a passed written exam. Participation in the final exam is subject to having passed all requirements (Studienleistungen).

Requirements (Studienleistungen):

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	13:45 – 15:15	008.2 in B6, 30–32
Tutorial
	03.09.19 – 03.12.19	Tuesday	15:30 – 17:00	C-108 in A5,6 entrance C
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	C-108 in A5,6 entrance C

MET: Design and Implementation of Online Behavioral Experiments

Lecturer(s)

Annelies Blom

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

Summary

This course gives an overview of data used in political science and their measurement properties. At the beginning of the course we will focus on survey data and traditional statistics and move to data science approaches for big data towards the end of the course. Topics covered include the Total Survey Error (TSE) framework, operationalizing research questions, guidelines for writing survey questions, testing questions with cognitive interviews and eye-tracking, sampling, coverage, and nonresponse of survey and big data, and data analytics approaches in data science.

Examination (lecture): Term paper

Course work (tutorial): 3 mock exams (Übungsarbeit)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	B318 in A5, 6 entrance B	Link
	31.10.19	Thursday	13:45 – 15:15	B244 in A5, 6 entrance B
Tutorial
	03.09.19 – 03.12.19	Tuesday	10:15 – 11:45	C-108 in A 5, 6 entrance C

MET: Lineare Optimierung (MAC 404 – in German)

Lecturer(s)

Stefano Balietti

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

1. Principles of Experimental Method: Validity, causality, lab-vs-online-vs-field, review of landmark experiments.

2. Programming for Online Experiments: The JavaScript language for the browser and the server (Node.JS), the nodeGame platform, R for data analysis and visualization.

3. Modeling Human Behavior: Introduction to game theory, main game theory games, social influence processes, heuristics and biases and how human behavior can be mapped into experiments.

4. Advanced Topics: Bots, recruiting strategies, the MTurk platform, crowdsourcing, optimal experimental design, ethical considerations.

5. Do it yourself: Define a research question meaningful for you, implement it in an experiment to run in class and/or online.

Students will earn credits for attending the class and for successfully completing module 5. There is no strict prerequisite for the course, but a quantitative background and previous programming knowledge will be valuable assets for the succeeding in the course.

Schedule

Seminar
	04.11.19 – 25.11.19	Monday	08:30 – 11:45	C 108 in A5, 6 entrance C	Link
	07.11.19 – 21.11.19	Thursday	17:15 – 20:30	C 108 in A5, 6 entrance C

MET: Machine Learning (IE675) (CDSS only)

Lecturer(s)

Claudia Schillings

Course Type: elective course

Course Number: MET

Prerequisites

Analysis I&II, Lineare Algebra I

Course Content

Lineare Programmierung: Simplex Verfahren,Dualität, innere Punkte Verfahren
Graphentheorie: minimal spannende Bäume, kürzeste Wege, maximale Flüsse
Ganzzahlige Programmierung: Branch and Bound Verfahren, Schnittebenenverfahren, Heuristiken

Begleitende Literatur

Eigenes Skript (online)
A. Koop, H. Moock: Lineare Optimierung -eine anwendungsorientierte Einführung in Operations Research•K. Neumann, M. Morlock: Operations Research
G. Nemhauser, L. Wolsey: Integer and Combinatorial Optimization
F. Jarre und J. Stoer: Optimierung•S. Krumke, H. Noltemeier: Graphentheoretische Konzepte und Algorithm

Prüfungsvorleistung: Bearbeitung von Übungsblättern und mindestens 50% der Übungsaufgaben bestanden.

Prüfungsleistung: Schriftliche Prüfung (90 Min)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	426154:15 – 11:45	B 144 in A 5, 6 entrance B
	04.09.19 – 04.12.19	Wednesday	10:15 – 11:45	B 144 in A 5, 6 entrance B
Tutorial
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 015 in A5, 6 entrance C
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	C 013 in A5, 6 entrance C
	06.09.19 – 06.12.19	Friday	12:00 – 13:30	C 015 in A5, 6 entrance C

Lecturer(s)

Rainer Gemulla

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Basic knowledge of probability and statistics.

Course Content

Machine learning is concerned with building computer systems that im-prove with experience as well as the study of learning processes, includ-ing the design of algorithms that are able to make predictions or extract knowledge from data. The aim of this module is to provide an introduc-tion into the field of machine learning, and study algorithms, underlying concepts, and theoreticalprinciples.

Basics of machine learning and probability theory
Inference and prediction
Selected classification and regression models
Kernels
Probabilistic graphical models•Introduction to neural networks

Literature

K.P. Murphy. Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
D. Koller, N. Friedman. Probabilistic graphical models. The MIT Press, 2009
Goodfellow, Y. Bengio, A. Courville. Deep Learning, The MIT Press, 2017
Additional material and articles provided in lecture notes

The course consists of a lecture accompanied by theoretical and practical exercises as well as case studies with real data. In the exercises, students will deepen the material discussed in the lecture, apply the methods in practice, and present the result.

Admission requirements for assessment: Homework assignments (pass at least 3 assignments)

From of assessment: Oral or written examination(Notification will be given at the start of the lecture period for this module)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	A 101 in B6, 23–25 entrance A
Tutorial
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	A 101 in B 6, 23–25 entrance A	Link

Lecturer(s)

Thomas Gschwend

Denis Cohen

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

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

Lecture
	04.09.19 – 04.12.19	Wednesday	08:30 – 10:00	B244 in A5,6 entrance B
Tutorial
	02.09.19 – 02.12.19	Monday	12:00 – 13:30	C -108 in A5,6, entrance C
Denis Cohen	03.09.19 – 03.12.19	Tuesday	17:15 – 18:45	C 108 in A 5, 6 entrance C
Marcel Neunhoeffer	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 108 in A 5, 6 entrance C

MET: Programming Course

MET: Programming in R and beyond

Lecturer(s)

Ursula Rost

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The course deals with the basic concepts of (object-oriented) pro-gramming using Java. In addition, some advanced topics are cov-ered such as writing GUI applications and dealing with external data (XML, databases):

primitive data types, variables, operators, expressions
control flow statements
complex data types (arrays), references•classes
OO concepts (information hiding, inheritance, polymorphism, abstract classes, interfaces)
Java API
Exceptions
IO using streams
Java Collections
GUI programming
processingof XML documents
database access (SQL, JDOM)

Literature

Learning Java (P. Niemeyer, P.Leuck),O'Reilly UK Ltd
Core Java Volume I—Fundamentals (C.S. Horstmann), Prentice Hall

Workload

6 SWS
28h: pre and post lecture studying and revision
56h: preparation and presentation of small software projects

Admission requirements for assessment: Successful completion and presentation of at least 75% of the weekly assessments

Form of assessment: Written examination (Programmiertestat, 180 mins)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C -109 PC Pool in A 5, 6 entrance C
Tutorial
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C
	09.09.19 – 02.12.19	Monday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C

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

Lecturer(s)

Martin Brandt

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:

Literature will be given during the course

Course achievement – regular participation in the course; non-graded test

Academic assessment – graded homework

Schedule

Seminar
biweekly	06.09.19 – 29.11.19	Friday	13:45 – 17:00	EO 162, CIP-Pool	Link

MET: Research Design (Lecture + Tutorial)

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Ruben Bach

Christoph Kern

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

Lecture
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	A 102 in B6, 23–25
Tutorial
	05.09.19 – 05.12.19	Thursday	15:30 – 17:00	A 102 in B6, 23–25 entrance A

Further details and registration.

Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

A Modern Axiomatic Perspective: Theory, Statistical Methodology, and Experimental Design
Multinomial processing tree models with reaction times
Introduction to R: Basics
Introduction to R: Advanced
NHST reloaded: Recent developments
An Introduction to Machine Learning
Longitudinal models for panel data and intensive longitudinal data
Introduction to OpenSesame: Creating Experiments with an Open-Source Software
The Role of Theorizing for strong Psychological Science
Introduction to Stan: A Probabilistic Programming Language for Bayesian Inference

MET: SMiP Foundations of Statistical Modeling (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

This course gives an advanced overview of standard multivariate methods and current developments in multivariate modeling. The topics include general and generalized linear models, structural equation models, multilevel modeling and specific models for the analysis of time-related effects. In the morning sessions, we will provide the formal foundations and theoretical background of the model classes for continuous and discrete observations. The afternoon sessions focus on empirical applications and hands-on exercises in data analysis.
The goals are to bring the PhD candidates to a common level of statistical knowledge and data analytic skills and to set the stage for the more specialized topics in the Foundations 2 course and workshops in the following semesters.

Dates

10.10.2019:   09:00 – 16:00 in B6, 30–32, Room 212
11.10.2019:   09:00 – 16:00 in B6, 30–32, Room 212

07.11.2019:    09:00 – 16:00 in B6, 30–32, Room 212
08.11.2019:    09:00 – 16:00 in Schloss EO 256

05.12.2019:    10:00 – 17:00 in B6, 30–32, Room 212
06.12.2019:    09:00 – 16:00 in B6, 30–32, Room 212

Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

MET/PSY: Programming experiments in OpenSesame

Lecturer(s)

Thomas Bräuninger

Samuel Müller

Course Type: elective course

Course Number: MET/POL

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

The objective of this course is to provide students with the basics of formal modeling in political science. The course has some breadth in coverage in the sense that it provides a graduate-level introduction and overview to di erent areas in game theory. It is also narrow in the sense that the emphasis is not on application and model testing but getting trained in reading and writing down formal models. At the conceptual level the course will cover the following topics: normal form games, Nash equilibria, extensive form games, subgame perfect equilibria, repeated games, bargaining, games with incomplete and imperfect information, Bayesian perfect equilibria, signaling games, preferences and individual choices, basics of decision theory and social choice. At the substantial level, we will use these concepts to study, as examples, candidate competition, political lobbying, and war and deterrence.

Literature
tba

Assessment: mid-term and final exam

Tutorial

This 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

Lecture
	02.09.19 – 02.12.19	Monday	10:15 – 11:45	B 317 in A5,6 entrance B
Tutorial
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	A 203 in B6, 23–25
	04.09.19 – 04.12.19	Wednesday	17:15 – 18:45	B 317 in Ab, 6 entrance B

MET/PSY: Theories and Methods in Social Psychology

Lecturer(s)

Pascal Kieslich

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

Schedule

Workshop
	11.10.19	Friday	10:15 – 15:15	EO 162 CIP Pool	Link
	12.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool
	25.10.19	Friday	10:15 – 15:15	EO 162 CIP-Pool
	26.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool

MET/SOC: Multilevel Analyses with the European Social Survey

Lecturer(s)

Herbert Bless

Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

This seminar has a particular focus on research activities in social psychology. Unlike seminars that concentrate on one core thematic topic, this seminar will address a selected variety of different research topics in current social psychology. In each seminar session we will have a presentation either by participating students or by members of the social psychology group. Each presentation will address a current research topic in social psychology. The seminar provides the opportunity to actively discuss methodological, theoretical, and applied implications of the presented research. . A particular focus will rest on the discussion of general methodological aspects. Participants need to have passed at least one other social psychology seminar in the master program or, alternatively, they have to be enrolled in one other social psychology seminar during the same semester.

Literature: Will be announced in the seminar

Examination:
Graded examination. Necessary conditions are: (a) active participation in the seminar discussions, (b) own presentation, and (c) homework. Grades are based on the homework (essay).

Schedule

Seminar
	02.09.19 – 02.12.19	Thursday	13:45 – 15:15	C 112 in A5, 6 entrance C

Dissertation Tutorial: Political Science

Lecturer(s)

Katja Möhring

Course Type: elective course

Course Number: MET/SOC

Credits: 6

Course Content

The seminar is designed as hands-on course to learn and apply multilevel regression analyses with the European Social Survey (ESS). Focus is on relationships between individuals’ employment, family life, wellbeing, and attitudes on the one hand, and welfare state policies, structural and cultural characteristics on the other hand. We will first get to know current research articles that use ESS data to analyse these relationships. In the second part of the seminar students will realize their own research projects with the ESS data oriented upon this previous research.

Schedule

Seminar
	13.01.20 – 17.01.20		10:15 – 17:00	C-108 in A5, 6 entrance C

0 ECTS

Lecturer(s)

Lilli Banholzer

Annelies Blom

Thomas Bräuninger

Sabine C. Carey

Marc Debus

Thomas Gschwend

Thomas König

Irene Menéndez Gonzaléz

Rüdiger Schmitt-Beck

Harald Schoen

Nikoleta Yordanova

Course Type: core course

Course Content

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

Please check with individual chairs for dates and times.

BAS: Current Research Perspectives

BAS: Mathematics for Social Scientists

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

Talk schedule

Schedule

Lecture
	06.09.19 – 13.09.19	Friday	08:30 – 11:45	211 in B 6, 30–32
	12.09.19	Thursday	08:30 – 11:45	211 in B 6, 30–32

MET: Crafting Social Science Research

Lecturer(s)

Anna-Sophie Kurella

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The exam is scheduled for 16 December from 10.30am to 12.30pm 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

Lecture
	04.10.19	Friday	10:15 – 13:30	211 in B 6, 30–32
	18.10.19	Friday	10:15 – 13:30	406 in B6, 30–32
	25.10.19	Friday	10:15 – 13:30	211 in B6, 30–32
	15.11.19 – 29.11.19	Friday	10:15 – 13:30	211 in B6, 30–32

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Schedule

Workshop
	03.09.19 – 03.12.19	Tuesday	12:00 – 13:30	A 103 in B6, 23–25

MET: Theory Building and Causal Inference

Lecturer(s)

Thomas Gschwend

Denis Cohen

Course Type: core course

Course Number: MET

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

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

Lecture
	04.09.19 – 04.12.19	Wednesday	08:30 – 10:00	B244 in A5,6 entrance B
Tutorial
	02.09.19 – 02.12.19	Monday	12:00 – 13:30	C -108 in A5,6, entrance C
Denis Cohen	03.09.19 – 03.12.19	Tuesday	17:15 – 18:45	C 108 in A 5, 6 entrance C
Marcel Neunhoeffer	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 108 in A 5, 6 entrance C

Lecturer(s)

Richard Traunmüller

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Schedule

Workshop
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	211 in B6, 30–32 on 17.10 in room 310 in B6, 30–32

RES: CDSS Workshop: Political Science

Lecturer(s)

Thomas Bräuninger

Samuel Müller

Course Type: core course

Course Number: MET/POL

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

Assessment: mid-term and final exam

Tutorial

Schedule

Lecture
	02.09.19 – 02.12.19	Monday	10:15 – 11:45	B 317 in A5,6 entrance B
Tutorial
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	A 203 in B6, 23–25
	04.09.19 – 04.12.19	Wednesday	17:15 – 18:45	B 317 in Ab, 6 entrance B

2 or 3 depending on applicable study regulations ECTS

Lecturer(s)

Thomas König

Course Type: core course

Course Number: RES

Credits: 2 or 3 depending on applicable study regulations

Course Content

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

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

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

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

Schedule

Workshop
	04.09.19 – 04.12.19	Wednesday	12:00 – 13:30	tbd	Link

RES: MZES B Colloquium “European Political Systems and their Integration”

RES: SFB 884 Seminar Series

Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

CSSR, TBCI, Dissertation Proposal

Course Content

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

MET: Data and Measurement (Lecture + Tutorial)

Lecturer(s)

Thomas Gautschi

Katrin Paula

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

Course Content

Assessment type (Prüfungsleistung): written exam (90 min)

Credits (9 ECTS) are awarded on a passed written exam. Participation in the final exam is subject to having passed all requirements (Studienleistungen).

Requirements (Studienleistungen):

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	13:45 – 15:15	008.2 in B6, 30–32
Tutorial
	03.09.19 – 03.12.19	Tuesday	15:30 – 17:00	C-108 in A5,6 entrance C
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	C-108 in A5,6 entrance C

MET: Design and Implementation of Online Behavioral Experiments

Lecturer(s)

Annelies Blom

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

Summary

Examination (lecture): Term paper

Course work (tutorial): 3 mock exams (Übungsarbeit)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	B318 in A5, 6 entrance B	Link
	31.10.19	Thursday	13:45 – 15:15	B244 in A5, 6 entrance B
Tutorial
	03.09.19 – 03.12.19	Tuesday	10:15 – 11:45	C-108 in A 5, 6 entrance C

MET: Lineare Optimierung (MAC 404 – in German)

Lecturer(s)

Stefano Balietti

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Schedule

Seminar
	04.11.19 – 25.11.19	Monday	08:30 – 11:45	C 108 in A5, 6 entrance C	Link
	07.11.19 – 21.11.19	Thursday	17:15 – 20:30	C 108 in A5, 6 entrance C

MET: Machine Learning (IE675) (CDSS only)

Lecturer(s)

Claudia Schillings

Course Type: elective course

Course Number: MET

Prerequisites

Analysis I&II, Lineare Algebra I

Course Content

Lineare Programmierung: Simplex Verfahren,Dualität, innere Punkte Verfahren
Graphentheorie: minimal spannende Bäume, kürzeste Wege, maximale Flüsse
Ganzzahlige Programmierung: Branch and Bound Verfahren, Schnittebenenverfahren, Heuristiken

Begleitende Literatur

Eigenes Skript (online)
A. Koop, H. Moock: Lineare Optimierung -eine anwendungsorientierte Einführung in Operations Research•K. Neumann, M. Morlock: Operations Research
G. Nemhauser, L. Wolsey: Integer and Combinatorial Optimization
F. Jarre und J. Stoer: Optimierung•S. Krumke, H. Noltemeier: Graphentheoretische Konzepte und Algorithm

Prüfungsvorleistung: Bearbeitung von Übungsblättern und mindestens 50% der Übungsaufgaben bestanden.

Prüfungsleistung: Schriftliche Prüfung (90 Min)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	426154:15 – 11:45	B 144 in A 5, 6 entrance B
	04.09.19 – 04.12.19	Wednesday	10:15 – 11:45	B 144 in A 5, 6 entrance B
Tutorial
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 015 in A5, 6 entrance C
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	C 013 in A5, 6 entrance C
	06.09.19 – 06.12.19	Friday	12:00 – 13:30	C 015 in A5, 6 entrance C

Lecturer(s)

Rainer Gemulla

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Basic knowledge of probability and statistics.

Course Content

Basics of machine learning and probability theory
Inference and prediction
Selected classification and regression models
Kernels
Probabilistic graphical models•Introduction to neural networks

Literature

K.P. Murphy. Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
D. Koller, N. Friedman. Probabilistic graphical models. The MIT Press, 2009
Goodfellow, Y. Bengio, A. Courville. Deep Learning, The MIT Press, 2017
Additional material and articles provided in lecture notes

Admission requirements for assessment: Homework assignments (pass at least 3 assignments)

From of assessment: Oral or written examination(Notification will be given at the start of the lecture period for this module)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	A 101 in B6, 23–25 entrance A
Tutorial
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	A 101 in B 6, 23–25 entrance A	Link

Lecturer(s)

Thomas Gschwend

Denis Cohen

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

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

Lecture
	04.09.19 – 04.12.19	Wednesday	08:30 – 10:00	B244 in A5,6 entrance B
Tutorial
	02.09.19 – 02.12.19	Monday	12:00 – 13:30	C -108 in A5,6, entrance C
Denis Cohen	03.09.19 – 03.12.19	Tuesday	17:15 – 18:45	C 108 in A 5, 6 entrance C
Marcel Neunhoeffer	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 108 in A 5, 6 entrance C

MET: Programming Course

MET: Programming in R and beyond

Lecturer(s)

Ursula Rost

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

primitive data types, variables, operators, expressions
control flow statements
complex data types (arrays), references•classes
OO concepts (information hiding, inheritance, polymorphism, abstract classes, interfaces)
Java API
Exceptions
IO using streams
Java Collections
GUI programming
processingof XML documents
database access (SQL, JDOM)

Literature

Learning Java (P. Niemeyer, P.Leuck),O'Reilly UK Ltd
Core Java Volume I—Fundamentals (C.S. Horstmann), Prentice Hall

Workload

6 SWS
28h: pre and post lecture studying and revision
56h: preparation and presentation of small software projects

Admission requirements for assessment: Successful completion and presentation of at least 75% of the weekly assessments

Form of assessment: Written examination (Programmiertestat, 180 mins)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C -109 PC Pool in A 5, 6 entrance C
Tutorial
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C
	09.09.19 – 02.12.19	Monday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C

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

Lecturer(s)

Martin Brandt

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Literature will be given during the course

Course achievement – regular participation in the course; non-graded test

Academic assessment – graded homework

Schedule

Seminar
biweekly	06.09.19 – 29.11.19	Friday	13:45 – 17:00	EO 162, CIP-Pool	Link

MET: Research Design (Lecture + Tutorial)

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Ruben Bach

Christoph Kern

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

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

There will be several assignments/Exam

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	A 102 in B6, 23–25
Tutorial
	05.09.19 – 05.12.19	Thursday	15:30 – 17:00	A 102 in B6, 23–25 entrance A

Further details and registration.

Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

A Modern Axiomatic Perspective: Theory, Statistical Methodology, and Experimental Design
Multinomial processing tree models with reaction times
Introduction to R: Basics
Introduction to R: Advanced
NHST reloaded: Recent developments
An Introduction to Machine Learning
Longitudinal models for panel data and intensive longitudinal data
Introduction to OpenSesame: Creating Experiments with an Open-Source Software
The Role of Theorizing for strong Psychological Science
Introduction to Stan: A Probabilistic Programming Language for Bayesian Inference

MET: SMiP Foundations of Statistical Modeling (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Dates

Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

MET/PSY: Programming experiments in OpenSesame

Lecturer(s)

Thomas Bräuninger

Samuel Müller

Course Type: elective course

Course Number: MET/POL

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

Assessment: mid-term and final exam

Tutorial

Schedule

Lecture
	02.09.19 – 02.12.19	Monday	10:15 – 11:45	B 317 in A5,6 entrance B
Tutorial
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	A 203 in B6, 23–25
	04.09.19 – 04.12.19	Wednesday	17:15 – 18:45	B 317 in Ab, 6 entrance B

MET/PSY: Theories and Methods in Social Psychology

Lecturer(s)

Pascal Kieslich

Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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.

Schedule

Workshop
	11.10.19	Friday	10:15 – 15:15	EO 162 CIP Pool	Link
	12.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool
	25.10.19	Friday	10:15 – 15:15	EO 162 CIP-Pool
	26.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool

MET/SOC: Multilevel Analyses with the European Social Survey

Lecturer(s)

Herbert Bless

Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

Literature: Will be announced in the seminar

Examination:
Graded examination. Necessary conditions are: (a) active participation in the seminar discussions, (b) own presentation, and (c) homework. Grades are based on the homework (essay).

Schedule

Seminar
	02.09.19 – 02.12.19	Thursday	13:45 – 15:15	C 112 in A5, 6 entrance C

POL: Advanced Topics in Comparative Politics: Elections in Comparative Perspective

Lecturer(s)

Katja Möhring

Course Type: elective course

Course Number: MET/SOC

Credits: 6

Course Content

Schedule

Seminar
	13.01.20 – 17.01.20		10:15 – 17:00	C-108 in A5, 6 entrance C

POL: Advanced Topics in Comparative Politics: Political Behavior in Context

Lecturer(s)

Sean Carey

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

Schedule

Seminar
	02.09.19 – 02.12.19	Monday	13:45 – 15:15	A 102 in B6, 23–25

POL: Advanced Topics in International Politics: After Intergovernmentalism. New Directions in Integration Research

Lecturer(s)

Harald Schoen

Course Type: elective course

Course Number: POL

Credits: 10

Course Content

Political behavior takes place in context. This statement is a truism and implies several challenges at the same time. Context is a multidimensional concept comprising – inter alia – social, political, and institutional features. At the theoretical level, the diversity of dimensions requires careful consideration of how to integrate contextual features into individual-level models of political behavior. Moreover, combining data from different levels of aggregation to examine the role of contexts in individual-level behavior raises several methodological issues. In this seminar, we will address the conceptual, theoretical, and methodological issues in the analysis of contextual effects on individual-level political behavior. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.

Course requirements: Oral presentation of a literature review and a data analysis and active participation during the sessions.

Academic assessment: Term Paper (ca. 8,000 words)

Schedule

Seminar
	03.09.19 – 03.12.19	Tuesday	12:00 – 13:30	B 317 in A 5, 6 entrance B	Link

POL: Advanced Topics in International Politics: Agents of Political Violence

Lecturer(s)

Thomas König

Course Type: elective course

Course Number: POL

Credits: 10

Course Content

Populism, Euroskepticism and Polarization are attracting a new generation of European integration scholars from different disciplines and subfields of Political Science. In addition to demand-side approaches, which study public attitudes and the reasons for their change, scholars investigate supply-side changes of the EU’s political system and the transformation of party competition at the national and EU level. After presenting “classical” demand- and supply-side approach we will discuss the most recent developments in European integration research.

Participants are expected to prepare the readings for each session and to deliver a paper on one European integration topic of their choice.

Assessment: Term paper

Schedule

Seminar
	05.09.19 – 05.12.19	Thursday	12:00 – 13:00	B 143 in A 5, 6 entrance B

BAS: Current Research Perspectives

Lecturer(s)

Sabine C. Carey

Course Type: elective course

Course Number: POL

Credits: 10

Course Content

This seminar discusses seminal and current work on agents of political violence. We analyze the role and characteristics of the military, including drivers and counterstrategies to coup d’états. A large part of the course will focus on irregular armed agents that are aligned with the stage, engaging with the growing research on militias, death squads, civil defense forces and paramilitary groups. We assess national and transnational drivers of their formation, their termination, how they affect political violence during and outside of civil wars. The seminars will be student-led. Over the course of the seminar you will develop your own research question on one of the topics discussed in the seminar and carry out your own research. Additionally, you are expected to write one book review, read all required materials, engage in the discussions and provide feedback on other student’s work.

One book review (1,000 words, potentially for submission to International Studies Review)
Feedback on a student’s research proposal
Leading one seminar session.

Required readings are indicated in the course schedule, which are based on seminal and current research on agents of political violence. Each session requires a significant amount of reading. Focus on the key arguments. You are not expected to know the details of all readings, or specific empirical strategies, results or facts. The specific topics and readings may change based on the interests of the class.

Schedule

Seminar
	03.09.19 – 03.12.19	Tuesday	10:15 – 11:45	B 143 in A 5, 6 entrance B	Link

BAS: Mathematics for Social Scientists

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

Talk schedule

Schedule

Lecture
	06.09.19 – 13.09.19	Friday	08:30 – 11:45	211 in B 6, 30–32
	12.09.19	Thursday	08:30 – 11:45	211 in B 6, 30–32

MET: Crafting Social Science Research

Lecturer(s)

Anna-Sophie Kurella

Course Type: core course

Course Number: BAS

Credits: 2

Course Content

The exam is scheduled for 16 December from 10.30am to 12.30pm 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

Lecture
	04.10.19	Friday	10:15 – 13:30	211 in B 6, 30–32
	18.10.19	Friday	10:15 – 13:30	406 in B6, 30–32
	25.10.19	Friday	10:15 – 13:30	211 in B6, 30–32
	15.11.19 – 29.11.19	Friday	10:15 – 13:30	211 in B6, 30–32

MET: Theory Building and Causal Inference

Lecturer(s)

Thomas Gschwend

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Schedule

Workshop
	03.09.19 – 03.12.19	Tuesday	12:00 – 13:30	A 103 in B6, 23–25

Lecturer(s)

Richard Traunmüller

Course Type: core course

Course Number: MET

Credits: 6

Course Content

Schedule

Workshop
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	211 in B6, 30–32 on 17.10 in room 310 in B6, 30–32

RES: AC2/BC3 Colloquia I

RES: CDSS Workshop: Research in Psychology

Lecturer(s)

Georg W. Alpers

Herbert Bless

Arndt Bröder

Oliver Dickhäuser

Edgar Erdfelder

Jutta Mata

Thorsten Meiser

Stefan Münzer

Sabine Sonnentag

Michaela Wänke

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.

2 or 3 depending on applicable study regulations ECTS

Lecturer(s)

Edgar Erdfelder

Arndt Bröder

Course Type: core course

Course Number: RES

Credits: 2 or 3 depending on applicable study regulations

Course Content

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

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

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

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

Literature: References will be given during the course.

Competences acquired

Improvement in research skills and communication of research results.

Schedule

Workshop
	02.09.19 – 02.12.19	Monday	15:30 – 17:00	EO 256	Link

RES: English Academic Writing for Psychologists 1st part (for CDSS students only)

1 ECTS

Lecturer(s)

Benjamin Hilbig

Course Type: core course

Course Number: RES

Credits: 1

Course Content

Writing Research Proposals – The course will cover recommendations on organization, content, and style of proposals for future research. The focus will be on thesis proposals (as requiredfor first-year SMiP-students) and grant applications (e.g. as would be submittedto the German Research Foundation).

Schedule

Workshop
	26.10.19	Saturday	10:00 – 18:00	EO 256

MET: Cross Sectional Data Analysis (Lecture + Tutorial)

MET: Data and Measurement (Lecture + Tutorial)

Lecturer(s)

Thomas Gautschi

Katrin Paula

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

Sound understanding of linear regression models (OLS), knowledge in linear algebra and calculus, and being familiar with the statistical package Stata.

Course Content

Assessment type (Prüfungsleistung): written exam (90 min)

Credits (9 ECTS) are awarded on a passed written exam. Participation in the final exam is subject to having passed all requirements (Studienleistungen).

Requirements (Studienleistungen):

Regular and active participation in the lab sessions.
Presentation of a weekly exercise; you must hand in the slides of the presentation, the Stata syntax file and output of the respective exercise, and a short output interpretation.

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	13:45 – 15:15	008.2 in B6, 30–32
Tutorial
	03.09.19 – 03.12.19	Tuesday	15:30 – 17:00	C-108 in A5,6 entrance C
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	C-108 in A5,6 entrance C

MET: Design and Implementation of Online Behavioral Experiments

Lecturer(s)

Annelies Blom

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

Summary

Examination (lecture): Term paper

Course work (tutorial): 3 mock exams (Übungsarbeit)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	B318 in A5, 6 entrance B	Link
	31.10.19	Thursday	13:45 – 15:15	B244 in A5, 6 entrance B
Tutorial
	03.09.19 – 03.12.19	Tuesday	10:15 – 11:45	C-108 in A 5, 6 entrance C

MET: Lineare Optimierung (MAC 404 – in German)

Lecturer(s)

Stefano Balietti

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Schedule

Seminar
	04.11.19 – 25.11.19	Monday	08:30 – 11:45	C 108 in A5, 6 entrance C	Link
	07.11.19 – 21.11.19	Thursday	17:15 – 20:30	C 108 in A5, 6 entrance C

MET: Machine Learning (IE675) (CDSS only)

Lecturer(s)

Claudia Schillings

Course Type: elective course

Course Number: MET

Prerequisites

Analysis I&II, Lineare Algebra I

Course Content

Lineare Programmierung: Simplex Verfahren,Dualität, innere Punkte Verfahren
Graphentheorie: minimal spannende Bäume, kürzeste Wege, maximale Flüsse
Ganzzahlige Programmierung: Branch and Bound Verfahren, Schnittebenenverfahren, Heuristiken

Begleitende Literatur

Eigenes Skript (online)
A. Koop, H. Moock: Lineare Optimierung -eine anwendungsorientierte Einführung in Operations Research•K. Neumann, M. Morlock: Operations Research
G. Nemhauser, L. Wolsey: Integer and Combinatorial Optimization
F. Jarre und J. Stoer: Optimierung•S. Krumke, H. Noltemeier: Graphentheoretische Konzepte und Algorithm

Prüfungsvorleistung: Bearbeitung von Übungsblättern und mindestens 50% der Übungsaufgaben bestanden.

Prüfungsleistung: Schriftliche Prüfung (90 Min)

Schedule

Lecture
	03.09.19 – 03.12.19	Tuesday	426154:15 – 11:45	B 144 in A 5, 6 entrance B
	04.09.19 – 04.12.19	Wednesday	10:15 – 11:45	B 144 in A 5, 6 entrance B
Tutorial
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 015 in A5, 6 entrance C
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	C 013 in A5, 6 entrance C
	06.09.19 – 06.12.19	Friday	12:00 – 13:30	C 015 in A5, 6 entrance C

Lecturer(s)

Rainer Gemulla

Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Basic knowledge of probability and statistics.

Course Content

Basics of machine learning and probability theory
Inference and prediction
Selected classification and regression models
Kernels
Probabilistic graphical models•Introduction to neural networks

Literature

K.P. Murphy. Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
D. Koller, N. Friedman. Probabilistic graphical models. The MIT Press, 2009
Goodfellow, Y. Bengio, A. Courville. Deep Learning, The MIT Press, 2017
Additional material and articles provided in lecture notes

Admission requirements for assessment: Homework assignments (pass at least 3 assignments)

From of assessment: Oral or written examination(Notification will be given at the start of the lecture period for this module)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	A 101 in B6, 23–25 entrance A
Tutorial
	03.09.19 – 03.12.19	Tuesday	08:30 – 10:00	A 101 in B 6, 23–25 entrance A	Link

Lecturer(s)

Thomas Gschwend

Denis Cohen

Course Type: elective course

Course Number: MET

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

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

Lecture
	04.09.19 – 04.12.19	Wednesday	08:30 – 10:00	B244 in A5,6 entrance B
Tutorial
	02.09.19 – 02.12.19	Monday	12:00 – 13:30	C -108 in A5,6, entrance C
Denis Cohen	03.09.19 – 03.12.19	Tuesday	17:15 – 18:45	C 108 in A 5, 6 entrance C
Marcel Neunhoeffer	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C 108 in A 5, 6 entrance C

MET: Programming Course

MET: Programming in R and beyond

Lecturer(s)

Ursula Rost

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

primitive data types, variables, operators, expressions
control flow statements
complex data types (arrays), references•classes
OO concepts (information hiding, inheritance, polymorphism, abstract classes, interfaces)
Java API
Exceptions
IO using streams
Java Collections
GUI programming
processingof XML documents
database access (SQL, JDOM)

Literature

Learning Java (P. Niemeyer, P.Leuck),O'Reilly UK Ltd
Core Java Volume I—Fundamentals (C.S. Horstmann), Prentice Hall

Workload

6 SWS
28h: pre and post lecture studying and revision
56h: preparation and presentation of small software projects

Admission requirements for assessment: Successful completion and presentation of at least 75% of the weekly assessments

Form of assessment: Written examination (Programmiertestat, 180 mins)

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	10:15 – 11:45	C -109 PC Pool in A 5, 6 entrance C
Tutorial
	05.09.19 – 05.12.19	Thursday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C
	09.09.19 – 02.12.19	Monday	12:00 – 13:30	C -109 PC Pool in A 5, 6 entrance C

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

Lecturer(s)

Martin Brandt

Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Literature will be given during the course

Course achievement – regular participation in the course; non-graded test

Academic assessment – graded homework

Schedule

Seminar
biweekly	06.09.19 – 29.11.19	Friday	13:45 – 17:00	EO 162, CIP-Pool	Link

MET: Research Design (Lecture + Tutorial)

MET: SMiP – Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)

Lecturer(s)

Ruben Bach

Christoph Kern

Course Type: elective course

Course Number: MET

Credits: 6 + 3

Prerequisites

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

There will be several assignments/Exam

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	13:45 – 15:15	A 102 in B6, 23–25
Tutorial
	05.09.19 – 05.12.19	Thursday	15:30 – 17:00	A 102 in B6, 23–25 entrance A

Further details and registration.

Course Type: elective course

Course Number: MET

Course Content

Further SMiP courses open to CDSS doctoral students are:

A Modern Axiomatic Perspective: Theory, Statistical Methodology, and Experimental Design
Multinomial processing tree models with reaction times
Introduction to R: Basics
Introduction to R: Advanced
NHST reloaded: Recent developments
An Introduction to Machine Learning
Longitudinal models for panel data and intensive longitudinal data
Introduction to OpenSesame: Creating Experiments with an Open-Source Software
The Role of Theorizing for strong Psychological Science
Introduction to Stan: A Probabilistic Programming Language for Bayesian Inference

MET: SMiP Foundations of Statistical Modeling (CDSS only)

Lecturer(s)

Thorsten Meiser

Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Dates

Further details can be found on the web page of the RTG 'Statistical Modeling in Psychology'

MET/PSY: Programming experiments in OpenSesame

Lecturer(s)

Thomas Bräuninger

Samuel Müller

Course Type: elective course

Course Number: MET/POL

Credits: 6 + 2

Course Content

This is a mandatory course for doctoral students in Political Science.

Assessment: mid-term and final exam

Tutorial

Schedule

Lecture
	02.09.19 – 02.12.19	Monday	10:15 – 11:45	B 317 in A5,6 entrance B
Tutorial
	04.09.19 – 04.12.19	Wednesday	13:45 – 15:15	A 203 in B6, 23–25
	04.09.19 – 04.12.19	Wednesday	17:15 – 18:45	B 317 in Ab, 6 entrance B

MET/PSY: Theories and Methods in Social Psychology

Lecturer(s)

Pascal Kieslich

Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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.

Schedule

Workshop
	11.10.19	Friday	10:15 – 15:15	EO 162 CIP Pool	Link
	12.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool
	25.10.19	Friday	10:15 – 15:15	EO 162 CIP-Pool
	26.10.19	Saturday	10:15 – 17:00	EO 162 CIP-Pool

MET/SOC: Multilevel Analyses with the European Social Survey

Lecturer(s)

Herbert Bless

Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

Literature: Will be announced in the seminar

Examination:
Graded examination. Necessary conditions are: (a) active participation in the seminar discussions, (b) own presentation, and (c) homework. Grades are based on the homework (essay).

Schedule

Seminar
	02.09.19 – 02.12.19	Thursday	13:45 – 15:15	C 112 in A5, 6 entrance C

PSY: Advanced topics in Cognitive Psychology

Lecturer(s)

Katja Möhring

Course Type: elective course

Course Number: MET/SOC

Credits: 6

Course Content

Schedule

Seminar
	13.01.20 – 17.01.20		10:15 – 17:00	C-108 in A5, 6 entrance C

PSY: Advanced Topics in Work and Organizational Psychology – Overview Work & Organizational Psychology

Lecturer(s)

Arndt Bröder

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, Dr. Michael Gräf, Dr. Meike Kroneisen, Dr. Lena Naderevic, Prof. Rüdiger Pohl und Dr. Monika Undorf.

Final test: written exam.

Schedule

Lecture
	05.09.19 – 05.12.19	Thursday	15:30 – 17:00	EO 145	Link