Fall 2019

  • Sociology

    Dissertation Tutorial: Sociology
    0 ECTS
    Lecturer(s)

    Course Type: core course
    Course Content

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

    Schedule
    Colloquium
    Keusch 02.09.19 - 02.12.19 Monday 15:30 - 17:00 tbc
    Möhring 03.09.19 - 03.12.19 Tuesday 10:15 - 11:45 tbd
    Leszczensky 03.09.19 - 03.12.19 Tuesday 12:00 - 13:30 tbd
    Kogan 03.09.19 - 03.12.19 Tuesday 15:30 - 17:00 tbd
    Hillmann 05.09.19 - 05.12.19 Thursday 10:15 - 11:45 tbc
    BAS: Current Research Perspectives
    2 ECTS
    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 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
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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: Crafting Social Science Research
    6 ECTS
    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
    Workshop
    03.09.19 - 03.12.19 Tuesday 12:00 - 13:30 A 103 in B6, 23-25
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    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: Sociology
    2 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

    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"
    2 ECTS
    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)
    6 + 3 ECTS
    Lecturer(s)
    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: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Lecturer(s)

    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: Design and Implementation of Online Behavioral Experiments
    6 ECTS
    Lecturer(s)

    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: Lineare Optimierung (MAC 404 - in German)
    ECTS
    Lecturer(s)

    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
    MET: Machine Learning (IE675) (CDSS only)
    6 ECTS
    Lecturer(s)

    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
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)

    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
    6 ECTS
    Lecturer(s)

    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
    MET: Programming in R and beyond
    4 ECTS
    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 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)
    6 + 3 ECTS
    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
    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
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    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

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS only)
    6 ECTS
    Lecturer(s)

    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/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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: Programming experiments in OpenSesame
    4 ECTS
    Lecturer(s)

    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/PSY: Theories and Methods in Social Psychology
    4 ECTS
    Lecturer(s)

    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
    MET/SOC: Multilevel Analyses with the European Social Survey
    6 ECTS
    Lecturer(s)

    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
  • Political Science

    Dissertation Tutorial: Political Science
    0 ECTS
    Lecturer(s)

    Course Type: core course
    Course Content

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

    Please check with individual chairs for dates and times.

    BAS: Current Research Perspectives
    2 ECTS
    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 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
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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: Crafting Social Science Research
    6 ECTS
    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
    Workshop
    03.09.19 - 03.12.19 Tuesday 12:00 - 13:30 A 103 in B6, 23-25
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)

    Course Type: core 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: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    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
    MET/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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.

    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
    RES: CDSS Workshop: Political Science
    2 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

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

    Please refer to the MZES webpages for dates and times.

    RES: SFB 884 Seminar Series
    2 ECTS
    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: Cross Sectional Data Analysis (Lecture + Tutorial)
    6 + 3 ECTS
    Lecturer(s)
    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: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Lecturer(s)

    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: Design and Implementation of Online Behavioral Experiments
    6 ECTS
    Lecturer(s)

    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: Lineare Optimierung (MAC 404 - in German)
    ECTS
    Lecturer(s)

    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
    MET: Machine Learning (IE675) (CDSS only)
    6 ECTS
    Lecturer(s)

    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
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)

    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
    6 ECTS
    Lecturer(s)

    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
    MET: Programming in R and beyond
    4 ECTS
    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 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)
    6 + 3 ECTS
    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
    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
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    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

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS only)
    6 ECTS
    Lecturer(s)

    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/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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: Programming experiments in OpenSesame
    4 ECTS
    Lecturer(s)

    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/PSY: Theories and Methods in Social Psychology
    4 ECTS
    Lecturer(s)

    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
    MET/SOC: Multilevel Analyses with the European Social Survey
    6 ECTS
    Lecturer(s)

    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
    POL: Advanced Topics in Comparative Politics: Elections in Comparative Perspective
    10 ECTS
    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. 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 Comparative Politics: Political Behavior in Context
    10 ECTS
    Lecturer(s)

    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: After Intergovernmentalism. New Directions in Integration Research
    10 ECTS
    Lecturer(s)

    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
    POL: Advanced Topics in International Politics: Agents of Political Violence
    10 ECTS
    Lecturer(s)

    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
  • Psychology

    BAS: Current Research Perspectives
    2 ECTS
    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 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
    BAS: Mathematics for Social Scientists
    2 ECTS
    Lecturer(s)

    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: Crafting Social Science Research
    6 ECTS
    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
    Workshop
    03.09.19 - 03.12.19 Tuesday 12:00 - 13:30 A 103 in B6, 23-25
    MET: Theory Building and Causal Inference
    6 ECTS
    Lecturer(s)

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

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

    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
    2 ECTS
    Lecturer(s)

    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.

    RES: CDSS Workshop: Research in Psychology
    2 or 3 depending on applicable study regulations ECTS
    Lecturer(s)

    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)

    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)
    6 + 3 ECTS
    Lecturer(s)
    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: Data and Measurement (Lecture + Tutorial)
    6 + 2 ECTS
    Lecturer(s)

    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: Design and Implementation of Online Behavioral Experiments
    6 ECTS
    Lecturer(s)

    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: Lineare Optimierung (MAC 404 - in German)
    ECTS
    Lecturer(s)

    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
    MET: Machine Learning (IE675) (CDSS only)
    6 ECTS
    Lecturer(s)

    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
    MET: Multivariate Analyses (Theory + Lab Course)
    6 + 2 ECTS
    Lecturer(s)

    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
    6 ECTS
    Lecturer(s)

    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
    MET: Programming in R and beyond
    4 ECTS
    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 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)
    6 + 3 ECTS
    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
    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
    MET: SMiP - Research Training Group 'Statistical Modeling in Psychology' additional courses (CDSS only)
    ECTS
    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

    Further details and registration.

    MET: SMiP Foundations of Statistical Modeling (CDSS only)
    6 ECTS
    Lecturer(s)

    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/POL: Game Theory (Theory + Tutorial)
    6 + 2 ECTS
    Lecturer(s)
    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: Programming experiments in OpenSesame
    4 ECTS
    Lecturer(s)

    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/PSY: Theories and Methods in Social Psychology
    4 ECTS
    Lecturer(s)

    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
    MET/SOC: Multilevel Analyses with the European Social Survey
    6 ECTS
    Lecturer(s)

    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
    PSY: Advanced topics in Cognitive Psychology
    4 ECTS
    Lecturer(s)

    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
    PSY: Advanced Topics in Work and Organizational Psychology - Overview Work & Organizational Psychology
    4 ECTS
    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

    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.

    Requirements for course credit:
    Research proposal and peer feedback

    Literature

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

    Schedule
    Lecture
    05.09.19 - 05.12.19 Thursday 17:15 - 18:45 B 244 in A 5, 6 entrance B