BE INSPIRED

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

Course Catalog

Fall 2014


Course Type: core course

Course Content

Doctoral theses supervised by Bernhard Ebbinghaus, Frank Kalter and Irena Kogan, respectively, will be discussed.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
02.09.14
02.12.14
Tuesday
17:15
18:45
Parkring 47, room 317


Course Type: core course

Course Content

Doctoral theses supervised by Thomas Gautschi and Henning Hillmann respectively, will be discussed.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
04.09.14
04.12.14
Thursday
17:15
18:45
Parkring 47, room 217

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

Schedule:

  • 1:45-2:45 PM: Sabine Carey, Political Science, Human Rights and Conflict research
  • 2:45-3:45 PM: Bernhard Ebbinghaus, Macrosociology, Comparative Research on Welfare Capitalism
  • 4:00-5:00 PM: Thomas Bräuninger, Political Economy
  • 5:00-6:00 PM: Nicole Rae Baerg, Political Science, International Organizations


Aug 28

  • 1:45-2:45 PM: Edgar Erdfelder, Cognitive Psychology
  • 2:45-3:45 PM: Arndt Bröder, Experimental Psychology
  • 4:00-5:00 PM Sabine Sonnentag, Organisational Psychology
  • 5:00-5:30 PM: final discussion

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
27.08.14
27.08.14
Wednesday
13:45
18:00
L9,7 room 208
28.08.14
28.08.14
Thursday
13:45
17:30
L9,7 room 308


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

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 Economist. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
26.08.14
26.08.14
Tuesday
10:15
13:15
L9,7 room 308
27.08.14
27.08.14
Wednesday
08:30
13:15
L9,7 room 308
28.08.14
28.08.14
Thursday
08:30
13:45
L9,7 room 308
04.09.14
04.09.14
Thursday
13:45
18:45
L9,7 room 308
05.09.14
05.09.14
Friday
08:30
13:15
L9,7 room 308
Exam
10.10.14
10.10.14
Friday
15:00
16:30
L9,7 room 308

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
12:00
13:30
A5,6 room B 317


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We first introduce the counterfactual theory of causal inference use this framework to investigate (and sometimes re-evaluate) the role of designed and natural experiments and conditioning methods such as regression and matching, in identifying causal effects. This part of the course will emphasize the applied computational aspects of these techniques.

We will briefly discuss alternatives to the counterfactual framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.

The course is a weighted mixture of lecture, discussion and practical exercises. Students should have access to a laptop and some ability with the R statistical language for the practical parts, in the second part of the course.

The course will assume a reasonable level of intuition about and experience with probability reasoning and also with multivariate statistics, up to approximately the level of generalized linear models. However, this is not an applied statistics course. Having a clear idea of what these methods do and what they must assume to do it will be more important than the ability to derive them from first principles. Indeed, one aim of the course is to sensitize you to the assumptions that will mostly be taken for granted in your subsequent formal modeling and applied statistics modules.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.14
05.12.14
Thursday
13:45
15:15
D7,27 room 307
02.10.14
05.12.14
Friday
10:15
11:45
D7,27 room 307

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, Dissertation Proposal Workshop


Course Content

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


Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

As the writing course for the 2013 cohort of PhD students did not take place in spring 2014, they are automatically signed up to this one. The writing course for the 2014 cohort will take place during the spring semester 2015.


Course Content


The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Wednesday
10:15
11:45
L9,7 room 308


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.11.14
03.11.14
Monday
09:30
12:30
D7,27 room 307
03.11.14
03.11.14
Monday
14:00
16:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
09:30
12:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
14:00
16:30
D7,27 room 307
17.11.14
17.11.14
Monday
09:30
12:30
D7,27 room 307
17.11.14
17.11.14
Monday
14:00
16:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
09:30
12:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
14:00
16:30
D7,27 room 307

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden die multivariaten Auswertungsverfahren, die in der Vorlesung (M.Sc. Modul AA1/BA1) formal eingeführt werden, anhand empirischer Beispiele illustriert. Dabei wird die Umsetzung der Verfahren mit Hilfe statistischer Software vorgestellt und eingeübt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
12:00
13:30
Eo 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Die Vorlesung führt grundlegend in die Verfahren und Anwendungsprobleme der multivariaten Datenanalyse ein. Die Veranstaltung behandelt im Wesentlichen die Grundlagen des klassischen linearen Regressionsmodells, führt daneben aber auch neuere Techniken der statistischen Modellierung ein. Die Vorlesung behandelt dabei sowohl die Anwendung von Regressionsverfahren in der sozialwissenschaftlichen Forschung als auch die Grundlagen der Matrixalgebra sowie der statistischen Schätztheorie. Die in der Vorlesung vermittelten Kenntnisse werden durch eine begleitende Übung vertieft.

Literatur:

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

Die Vorlesung schließt mit einer Prüfungsleistung (Klausur) ab.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
13:45
15:15
A5,6 room B 317


Course Type: elective course

Course Number: MET

Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
02.09.14
02.12.14
Tuesday
15:30
17:00
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Course Content

This short workshop is designed to provide individuals with training in the conceptualization and application of multilevel statistical models. We will consider various models that are appropriate when outcomes of interest are known to be correlated or otherwise mutually dependent, such as when many repeated measurements are taken from a single individual across a sample of individuals, or when individuals are sampled across multiple sites (e.g. students nested within multiple classrooms). Linear multilevel models with be covered in detail and nonlinear (e.g. logistic and exponential) extensions will be introduced. The workshop is intended to be hands-on, and a number of working examples will be used throughout using the R software package. Attendees are encouraged to bring their own data for use in a final part of the workshop where we will consider the nuances of individual research questions.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
20.10.14
20.10.14
Monday
09:00
17:00
L7,3-5 room 158
21.10.14
21.10.14
Tuesday
10:00
15:00
L7,3-5 room 158

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

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

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


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


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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.14
04.12.14
Thursday
08:30
10:00
A5,6 room B 143


Course Type: elective course

Course Number: MET

Credits: 2

Course Content

This tutorial accompanies the course "Multivariate Analyses". The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing datasets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

Graded assignments include several problem sets.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
04.09.14
04.12.14
Thursday
10:15
11:45
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden wichtige, in der Psychologie häufig angewendete Modellklassen vertieft behandelt. Dabei stehen verallgemeinerte lineare Modelle (insbesondere logistische Regression), Strukturgleichungsmodelle und Mehrebenenanalysen im Vordergrund. Neben der Behandlung der theoretischen Grundlagen werden empirische Anwendungen dieser Modelle vorgestellt sowie praktische Übungen zu deren Spezifikation in Programmen wie SPSS, LISREL oder Mplus durchgeführt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
01.12.14
Monday
13:45
15:14
EO 162, CIP-Pool

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 in combination with the type setting program LaTeX.
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. A basic introduction to R can be found under: http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.09.14
03.09.14
Wednesday
13:34
17:15
EO 162, CIP-Pool
10.09.14
10.09.14
Wednesday
13:45
17:15
Eo 162, CIP-Pool
24.09.14
24.09.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
08.10.14
08.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
22.10.14
22.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
05.11.14
05.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
19.11.14
19.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
03.12.14
03.12.14
Wednesday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Please refer to Prof. Bosnjak's course webdoc for details.

In a nutshell, meta-analysis can be described as a set of statistical methods for aggregating, summarizing, and drawing inferences from collections of thematically related studies. The key idea is to quantify the size, direction, and/or strength of an effect, and to cancel out sampling errors associated with individual studies.

Meta-analytic techniques have become the standard methods for aggregating the results from thematically related studies in the social and behavioral sciences. They can be used to describe a research field, to test and/or compare theories on a high level of abstraction, and to derive conclusions about the effectiveness of interventions.

The overall goal of this course is to provide a hands-on introduction into the different approaches belonging to the umbrella term ´meta-analysis´, to sketch their conceptual foundations, and to point participants to special issues and problems when evaluating and/or conducting meta-analytic studies. Moreover, each and every of the following topics addressed will be accompanied by exercises:

  • Definitions and processes: Systematic reviews and meta-analysis

  • What kind of scientific and applied research problems can be addressed with the aid of systematic reviews and meta-analysis?

  • Big picture: The systematic review/meta-analysis research cycle

  • Meta-analytic models: Hedges/Olkin (Homogeneity-based), Hunter/Schmidt (Psychometric)

  • Problem statement: Framing meta-analytic research questions

  • Systematically retrieving relevant primary studies: Literature research and selection

  • Extracting information from primary studies: Coding

  • Effect sizes: Basic types, estimation, conversion, and approximation strategies

  • Synthesizing the evidence: Mean effect size computation, moderator analysis techniques

  • Special issues: Dependent effect sizes, publication bias, study quality

  • Interpretation and reporting

  • Case studies and research critiques prepared by participants from various fields (tailored towards the academic backgrounds of the participants)

Office hours:

  • On appointment (can be held in person in L13,9, or via Skype)

Teaching material:

Topics and progress:

  1. Introduction

  • Aims and scope of the course

  • Eligibility and grading criteria

  • Participants´ background and research interests

  • Core concepts and prerequisites (preview)

  • Recommended readings


  1. Basic topics and overview

  • Core concepts and prerequisites

  • Meta-Analysis: Conceptual basics

  • HO-approach illustrated

  • HS-approach illustrated

  • ´Evergreen´ discussion topics


  1. Problem statement and data collection
    (systematic retrieval and selection of studies)

  • Problem statement

  • Systematic retrieval of studies

  • Systematic selection of studies


  1. Data extraction, coding, and unifying effect sizes

  • Principles of data extraction

  • Development of coding form and coding manual

  • Assessing study quality (especially Study DIAD)

  • Study DIAD

  • Determining inter-coder reliability

  • Unifying effect sizes


  1. Analysis and interpretation

  • Step-by-step analysis using MS Excel

    • FE analysis

    • RE analysis

  • Special analysis issues:

    • Homogeneity / heterogeneity indicators

    • Sensitivity analyses

  • Interpreting results:

    • Theory testing/development (SICT)

    • Description of a research field (Web nonresponse)

    • Estimating the effectiveness of interventions (Fluency-effects)


    1. Reporting

    • Meta-Analytic Reporting Standards

    • Reporting meta-analytic findings using ...

      • Tables

      • Graphs


      1. Special topics

      • How to develop a research critique for a published systematic review/meta-analysis?

      • Special issues and problems

        • Criticism of meta-analytic techniques

        • Simpson´s paradox

        • (Stochastically) dependent effect sizes

        • Publication bias (File drawer problem)


        1. Research critiques of selected meta-analyses (presented by participants)


        Recommended Textbooks:


        • Bornstein, M., Hedges, L.V., Higgins, J.P.T, & Rothstein, H.R. (2009). Introduction to Meta-Analysis. Chichester, UK: Wiley.

        • Card, N.A. (2011). Applied Meta-Analysis for the Social Sciences. New York: Guilford Press.

        • Cooper, H. (2010). Research Synthesis and Meta-Analysis: A Step-by-Step Approach. Thousand Oaks, CA: Sage.

        • Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.) (2009). Handbook of Research Synthesis (2nd ed.). New York: Russell Sage Foundation.

        • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Thousand Oaks, CA: Sage.

        • Lipsey, M.W., & Wilson, D.B. (2001). Practical Meta-analysis. Thousand Oaks: Sage.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
10.09.14
10.09.14
Wednesday
17:15
20:30
EO 256
24.09.14
24.09.14
Wednesday
17:15
20:30
Eo 256
01.10.14
01.10.14
Wednesday
17:15
20:30
EO 256
15.10.14
15.10.14
Wednesday
17:15
20:30
EO 256
22.10.14
22.10.14
Wednesday
17:15
20:30
EO 256
05.11.14
05.11.14
Wednesday
17:15
18:45
EO 256
12.11.14
12.11.14
Wednesday
17:15
20:30
EO 256
19.11.14
19.11.14
Wednesday
17:15
20:30
Eo 256
26.11.14
26.11.14
Wednesday
17:15
20:30
EO 256


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free and open-source experiment builder for the social sciences. Simple experiments can be created via the graphical user interface (GUI). Complex experiments can be realized using the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. The workshop consists of the following parts:

  1. Creating simple experiments with the GUI,
  2. Creating complex experiments using Python inline scripts,
  3. Individual projects,
  4. Specific applications.

The first two sessions of the workshop involve both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. In the subsequent break, each participant will work on his/her own experiment and can consult the instructor if needed. In the final session, the individual experiments will be discussed and an overview of selected special applications of OpenSesame will be given (e.g. the analyses of mouse movements, the creation of experiments for tablet devices etc.) - depending on participants' preferences.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.10.14
10.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
11.10.14
11.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool
24.10.14
24.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
25.10.14
25.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Welfare states are designed to protect its citizens from ”new” and ”old” social risks such as poverty, social exclusion, unemployment, disability, old-age and ill health. The instruments used differ greatly not only in transatlantic comparison but also within Europe.
This seminar asks the following questions with respect to origin, development and contemporary challenges of modern social protection systems: How do welfare states differ in international comparison? How can we make sense of these differences analytically? What explains the differences and commonalities in institutional evolution, and what role do socio-cultural ideas, the interests of powerful societal groups, and established action-constraining institutions play? The structure of this seminar is aimed to achieve the following goals: (1) students will be equipped with an analytical tool kit to systematically compare and contrast welfare states; (2) they will be introduced to some of the leading theoretical approaches to explain welfare state origins and evolution in the historical-comparative sciences; and (3) they will gain substantive knowledge about various welfare states and the societies in which they developed. Especially, but not exclusively, will we draw on examples from Britain, Germany, Sweden, and the United States.


Für den benoteten Leistungsnachweis wird die regelmäßige aktive Teilnahme, das Lesen der Pflichtlektüre, und die Übernahme eines Kurzvortrags mit Thesenpapier erwartet. Außerdem ist die Anfertigung einer Hausarbeit erforderlich. Die Hausarbeit kann in deutscher oder englischer Sprache verfasst werden.


Anmeldung:
Aus organisatorischen Gründen bitten wir um Registrierung NUR über das Studierendenportal möglichst bis spätestens 1 Woche vor Beginn des Seminars.


Sprechstunde: Mittwochs, 10.00-11.00 Uhr und nach Vereinbarung


Literature:
Pierson, Christopher and Francis G. Castles, eds. (2000). The Welfare State: A Reader. Malden, Mass., Polity Press.
Francis G. Castles, Stephan Leibfried, Jane Lewis, Herbert Obinger, and Christopher Pierson, eds. (2010). The Oxford Handbook of the Welfare State. Oxford, Oxford University Press.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.09.14
03.12.14
Wednesday
12:00
13:30
A5,6 room B 317


Course Type: elective course

Course Number: SOC/MET

Credits: 6

Course Content

The course introduces students to the fundamental principles, different paradigms and empirical-analytic research strategies in the social sciences. First, as an introduction into the epistemological foundations of social sciences we will read key texts of the philosophy of sciences and analytic philosophy. What is a reasonable explanation of social phenomena and how can it be verified? Furthermore, we will study the different paradigms in the social sciences and sociology in particular. In how far these complement or represent these disparate perspectives? Finally, the advantages and disadvantages of possible empirical research strategies and actual scientific practice will be discussed. In each session, first there will be a lecture on the topic, and thereafter, the texts are discussed. The course therefore requires regular reading of the required literature in preparation of the meeting and active participation in the discussions.


Recommended literature:

  • Della Porta, D., and Keating, M. (eds.) (2008). Approaches and Methodologies in the Social Sicenes. A Pluralist Perspective. Cambridge: Cambridge University Press.
  • Gerring, J. (2012). Social Science Methodology: A Unified Framework (Second Edition). Cambridge: Cambridge University Press.
  • Little, Daniel, Varieties of Social Explanation. An Introduction to the Philosophy of Social Science, Boulder, CO: Westview Press, 1991.

Exam: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
01.09.14
01.12.14
Monday
15:30
17:00
A5,6 room B 143

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

Schedule:

  • 1:45-2:45 PM: Sabine Carey, Political Science, Human Rights and Conflict research
  • 2:45-3:45 PM: Bernhard Ebbinghaus, Macrosociology, Comparative Research on Welfare Capitalism
  • 4:00-5:00 PM: Thomas Bräuninger, Political Economy
  • 5:00-6:00 PM: Nicole Rae Baerg, Political Science, International Organizations


Aug 28

  • 1:45-2:45 PM: Edgar Erdfelder, Cognitive Psychology
  • 2:45-3:45 PM: Arndt Bröder, Experimental Psychology
  • 4:00-5:00 PM Sabine Sonnentag, Organisational Psychology
  • 5:00-5:30 PM: final discussion

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
27.08.14
27.08.14
Wednesday
13:45
18:00
L9,7 room 208
28.08.14
28.08.14
Thursday
13:45
17:30
L9,7 room 308


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

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 Economist. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
26.08.14
26.08.14
Tuesday
10:15
13:15
L9,7 room 308
27.08.14
27.08.14
Wednesday
08:30
13:15
L9,7 room 308
28.08.14
28.08.14
Thursday
08:30
13:45
L9,7 room 308
04.09.14
04.09.14
Thursday
13:45
18:45
L9,7 room 308
05.09.14
05.09.14
Friday
08:30
13:15
L9,7 room 308
Exam
10.10.14
10.10.14
Friday
15:00
16:30
L9,7 room 308

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
12:00
13:30
A5,6 room B 317


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We first introduce the counterfactual theory of causal inference use this framework to investigate (and sometimes re-evaluate) the role of designed and natural experiments and conditioning methods such as regression and matching, in identifying causal effects. This part of the course will emphasize the applied computational aspects of these techniques.

We will briefly discuss alternatives to the counterfactual framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.

The course is a weighted mixture of lecture, discussion and practical exercises. Students should have access to a laptop and some ability with the R statistical language for the practical parts, in the second part of the course.

The course will assume a reasonable level of intuition about and experience with probability reasoning and also with multivariate statistics, up to approximately the level of generalized linear models. However, this is not an applied statistics course. Having a clear idea of what these methods do and what they must assume to do it will be more important than the ability to derive them from first principles. Indeed, one aim of the course is to sensitize you to the assumptions that will mostly be taken for granted in your subsequent formal modeling and applied statistics modules.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.14
05.12.14
Thursday
13:45
15:15
D7,27 room 307
02.10.14
05.12.14
Friday
10:15
11:45
D7,27 room 307

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, Dissertation Proposal Workshop


Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
03.09.14
03.12.14
Wednesday
12:00
13:30
A5,6 room B 143

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

As the writing course for the 2013 cohort of PhD students did not take place in spring 2014, they are automatically signed up to this one. The writing course for the 2014 cohort will take place during the spring semester 2015.


Course Content


The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Wednesday
10:15
11:45
L9,7 room 308


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.11.14
03.11.14
Monday
09:30
12:30
D7,27 room 307
03.11.14
03.11.14
Monday
14:00
16:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
09:30
12:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
14:00
16:30
D7,27 room 307
17.11.14
17.11.14
Monday
09:30
12:30
D7,27 room 307
17.11.14
17.11.14
Monday
14:00
16:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
09:30
12:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
14:00
16:30
D7,27 room 307

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden die multivariaten Auswertungsverfahren, die in der Vorlesung (M.Sc. Modul AA1/BA1) formal eingeführt werden, anhand empirischer Beispiele illustriert. Dabei wird die Umsetzung der Verfahren mit Hilfe statistischer Software vorgestellt und eingeübt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
12:00
13:30
Eo 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Die Vorlesung führt grundlegend in die Verfahren und Anwendungsprobleme der multivariaten Datenanalyse ein. Die Veranstaltung behandelt im Wesentlichen die Grundlagen des klassischen linearen Regressionsmodells, führt daneben aber auch neuere Techniken der statistischen Modellierung ein. Die Vorlesung behandelt dabei sowohl die Anwendung von Regressionsverfahren in der sozialwissenschaftlichen Forschung als auch die Grundlagen der Matrixalgebra sowie der statistischen Schätztheorie. Die in der Vorlesung vermittelten Kenntnisse werden durch eine begleitende Übung vertieft.

Literatur:

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

Die Vorlesung schließt mit einer Prüfungsleistung (Klausur) ab.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
13:45
15:15
A5,6 room B 317


Course Type: elective course

Course Number: MET

Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
02.09.14
02.12.14
Tuesday
15:30
17:00
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Course Content

This short workshop is designed to provide individuals with training in the conceptualization and application of multilevel statistical models. We will consider various models that are appropriate when outcomes of interest are known to be correlated or otherwise mutually dependent, such as when many repeated measurements are taken from a single individual across a sample of individuals, or when individuals are sampled across multiple sites (e.g. students nested within multiple classrooms). Linear multilevel models with be covered in detail and nonlinear (e.g. logistic and exponential) extensions will be introduced. The workshop is intended to be hands-on, and a number of working examples will be used throughout using the R software package. Attendees are encouraged to bring their own data for use in a final part of the workshop where we will consider the nuances of individual research questions.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
20.10.14
20.10.14
Monday
09:00
17:00
L7,3-5 room 158
21.10.14
21.10.14
Tuesday
10:00
15:00
L7,3-5 room 158

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

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

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


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


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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.14
04.12.14
Thursday
08:30
10:00
A5,6 room B 143


Course Type: elective course

Course Number: MET

Credits: 2

Course Content

This tutorial accompanies the course "Multivariate Analyses". The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing datasets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

Graded assignments include several problem sets.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
04.09.14
04.12.14
Thursday
10:15
11:45
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden wichtige, in der Psychologie häufig angewendete Modellklassen vertieft behandelt. Dabei stehen verallgemeinerte lineare Modelle (insbesondere logistische Regression), Strukturgleichungsmodelle und Mehrebenenanalysen im Vordergrund. Neben der Behandlung der theoretischen Grundlagen werden empirische Anwendungen dieser Modelle vorgestellt sowie praktische Übungen zu deren Spezifikation in Programmen wie SPSS, LISREL oder Mplus durchgeführt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
01.12.14
Monday
13:45
15:14
EO 162, CIP-Pool

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 in combination with the type setting program LaTeX.
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. A basic introduction to R can be found under: http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.09.14
03.09.14
Wednesday
13:34
17:15
EO 162, CIP-Pool
10.09.14
10.09.14
Wednesday
13:45
17:15
Eo 162, CIP-Pool
24.09.14
24.09.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
08.10.14
08.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
22.10.14
22.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
05.11.14
05.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
19.11.14
19.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
03.12.14
03.12.14
Wednesday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Please refer to Prof. Bosnjak's course webdoc for details.

In a nutshell, meta-analysis can be described as a set of statistical methods for aggregating, summarizing, and drawing inferences from collections of thematically related studies. The key idea is to quantify the size, direction, and/or strength of an effect, and to cancel out sampling errors associated with individual studies.

Meta-analytic techniques have become the standard methods for aggregating the results from thematically related studies in the social and behavioral sciences. They can be used to describe a research field, to test and/or compare theories on a high level of abstraction, and to derive conclusions about the effectiveness of interventions.

The overall goal of this course is to provide a hands-on introduction into the different approaches belonging to the umbrella term ´meta-analysis´, to sketch their conceptual foundations, and to point participants to special issues and problems when evaluating and/or conducting meta-analytic studies. Moreover, each and every of the following topics addressed will be accompanied by exercises:

  • Definitions and processes: Systematic reviews and meta-analysis

  • What kind of scientific and applied research problems can be addressed with the aid of systematic reviews and meta-analysis?

  • Big picture: The systematic review/meta-analysis research cycle

  • Meta-analytic models: Hedges/Olkin (Homogeneity-based), Hunter/Schmidt (Psychometric)

  • Problem statement: Framing meta-analytic research questions

  • Systematically retrieving relevant primary studies: Literature research and selection

  • Extracting information from primary studies: Coding

  • Effect sizes: Basic types, estimation, conversion, and approximation strategies

  • Synthesizing the evidence: Mean effect size computation, moderator analysis techniques

  • Special issues: Dependent effect sizes, publication bias, study quality

  • Interpretation and reporting

  • Case studies and research critiques prepared by participants from various fields (tailored towards the academic backgrounds of the participants)

Office hours:

  • On appointment (can be held in person in L13,9, or via Skype)

Teaching material:

Topics and progress:

  1. Introduction

  • Aims and scope of the course

  • Eligibility and grading criteria

  • Participants´ background and research interests

  • Core concepts and prerequisites (preview)

  • Recommended readings


  1. Basic topics and overview

  • Core concepts and prerequisites

  • Meta-Analysis: Conceptual basics

  • HO-approach illustrated

  • HS-approach illustrated

  • ´Evergreen´ discussion topics


  1. Problem statement and data collection
    (systematic retrieval and selection of studies)

  • Problem statement

  • Systematic retrieval of studies

  • Systematic selection of studies


  1. Data extraction, coding, and unifying effect sizes

  • Principles of data extraction

  • Development of coding form and coding manual

  • Assessing study quality (especially Study DIAD)

  • Study DIAD

  • Determining inter-coder reliability

  • Unifying effect sizes


  1. Analysis and interpretation

  • Step-by-step analysis using MS Excel

    • FE analysis

    • RE analysis

  • Special analysis issues:

    • Homogeneity / heterogeneity indicators

    • Sensitivity analyses

  • Interpreting results:

    • Theory testing/development (SICT)

    • Description of a research field (Web nonresponse)

    • Estimating the effectiveness of interventions (Fluency-effects)


    1. Reporting

    • Meta-Analytic Reporting Standards

    • Reporting meta-analytic findings using ...

      • Tables

      • Graphs


      1. Special topics

      • How to develop a research critique for a published systematic review/meta-analysis?

      • Special issues and problems

        • Criticism of meta-analytic techniques

        • Simpson´s paradox

        • (Stochastically) dependent effect sizes

        • Publication bias (File drawer problem)


        1. Research critiques of selected meta-analyses (presented by participants)


        Recommended Textbooks:


        • Bornstein, M., Hedges, L.V., Higgins, J.P.T, & Rothstein, H.R. (2009). Introduction to Meta-Analysis. Chichester, UK: Wiley.

        • Card, N.A. (2011). Applied Meta-Analysis for the Social Sciences. New York: Guilford Press.

        • Cooper, H. (2010). Research Synthesis and Meta-Analysis: A Step-by-Step Approach. Thousand Oaks, CA: Sage.

        • Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.) (2009). Handbook of Research Synthesis (2nd ed.). New York: Russell Sage Foundation.

        • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Thousand Oaks, CA: Sage.

        • Lipsey, M.W., & Wilson, D.B. (2001). Practical Meta-analysis. Thousand Oaks: Sage.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
10.09.14
10.09.14
Wednesday
17:15
20:30
EO 256
24.09.14
24.09.14
Wednesday
17:15
20:30
Eo 256
01.10.14
01.10.14
Wednesday
17:15
20:30
EO 256
15.10.14
15.10.14
Wednesday
17:15
20:30
EO 256
22.10.14
22.10.14
Wednesday
17:15
20:30
EO 256
05.11.14
05.11.14
Wednesday
17:15
18:45
EO 256
12.11.14
12.11.14
Wednesday
17:15
20:30
EO 256
19.11.14
19.11.14
Wednesday
17:15
20:30
Eo 256
26.11.14
26.11.14
Wednesday
17:15
20:30
EO 256


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free and open-source experiment builder for the social sciences. Simple experiments can be created via the graphical user interface (GUI). Complex experiments can be realized using the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. The workshop consists of the following parts:

  1. Creating simple experiments with the GUI,
  2. Creating complex experiments using Python inline scripts,
  3. Individual projects,
  4. Specific applications.

The first two sessions of the workshop involve both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. In the subsequent break, each participant will work on his/her own experiment and can consult the instructor if needed. In the final session, the individual experiments will be discussed and an overview of selected special applications of OpenSesame will be given (e.g. the analyses of mouse movements, the creation of experiments for tablet devices etc.) - depending on participants' preferences.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.10.14
10.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
11.10.14
11.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool
24.10.14
24.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
25.10.14
25.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

General elections are the core institution of representative democracy. Accordingly, in parliamentary systems like Germany the question how voters make up their minds about which party to choose is of crucial importance. The seminar offers the opportunity to search answers to this question for the most recent German Federal Election in 2013 by analyzing newly released survey data on voters' beliefs, attitudes and behavior. A wide range of research topics can be explored. Participants of the seminar can, for instance, look into the role of social structural, ideological or partisan predispositions for electoral behavior. Popular folklore has it that these factors are becoming increasingly irrelevant for electoral behavior, but are these claims valid? How important are, on the other hand, short-term factors for electoral choices? Allegedly, voters have become more rational, so we should expect strong effects of issue attitudes or economic circumstances. However, it is also claimed that voting behavior has become strongly personalized, turning elections into beauty contests – yet another question that can be explored. Another possibility is to analyze communication effects on voting – are voters' choices responsive to the information received from social networks, the mass media, or the parties' campaigns? If so, under which conditions? Besides vote choice, student projects can also look into turnout. In the seminar, participants will get the opportunity to define and explore their own research questions.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
08:30
11:45
A5,6 room B 318


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

Legislatures are, at least formally, the key policy-making institutions in modern democracies. They represent and aggregate constituent interests, pass laws and approve government budgets, monitor bureaucracies, and, in European-style, parliamentary democracies choose governments. Yet, any single link in this chain of multiple delegations involves reciprocal dependencies and accountablities that put constraints on what actors can do and how they do it. Institutions certainly matter but how and when and to what extent do they shape the way legislators feel, behave and act?

The objective of this course is to prepare you for professional research into legislative politics. The course has some breadth in coverage in the sense that it provides a graduate-level overview of different areas such as electoral competition, legislative bargaining, coalition formation, information transmission, agenda-setting, legislative organization, voting and cohesion, delegation to bureaucratic authorities, and seminal models used in these areas. It is also narrow in the sense that the emphasis is on approaches that use and apply formal models in these areas. When do legislatures grants discretionary power to bureaucrats and why should they do that at all? What drives legislators' decisions and how does that vary across different types of electoral and parliamentary institutions? The ultimate goal is to identify interesting and important questions in the field, and to think about the ways in which research can be designed to get at those questions. Throughout the semester we will meet to pore over a set of seminal papers and important books. The focus here is on the theoretical argument. What is the substantive argument? What do we have to assume to make the argument? What type of model is used and how do we actually arrive at the conclusions? We will also have a look at one or the other piece that exemplifies empirical strategies and evidence.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
10:15
13:30
102

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

Personality traits and values play an important role in people’s everyday life. But do they also make a difference when it comes to political reasoning and political behavior? This seminar will address this question with its manifold ramifications. After an introduction of central theoretical concepts, we will discuss issues in measuring personality traits and values as well as the impact of traits and values on political attitudes and on political behavior. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available national and cross-national data sets.

Literature:

  • Caprara, G. V., and M. Vecchione. (2013). Personality Approaches to Political Behavior. In: L. Huddy, D. O. Sears, and J. S. Levy (eds.). The Oxford Handbook of Political Psychology. 2nd ed., Oxford: Oxford University Press, 23-58.
  • Mondak, J. J. (2010). Personality and the Foundations of Political Behavior. Cambridge: Cambridge University Press, 1-23.
  • Schwartz, S. H. (1994). Are There Universal Aspects in the Structure and Contents of Human Values? In: Journal of Social Issues 50 (4), 19-45.

Credit points can be obtained for a paper (8,000 words), the oral presentation of this paper, as well as active participation during the sessions.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
10:15
13:30
B6,23-25 room A 305


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

In this seminar theories of mass opinion about international relations will be applied to a series of comparative survey studies collected since the change of the millennium. The focus will be on the determinants of such attitudes and on explaining their development over time and their structure. Replication of existing studies will be encouraged. Participants will prepare a comparative research paper over the course of the semester covering all stages of the empirical research process – except data collection and cleaning. Data from the "Transatlantic Trends (TT)" survey series will be made available to participants. These data sets will contain the surveys from the U.S. and Germany. However, data from other TT-countries can also be analyzed if a research question indicates this. Likewise, if a comparison with elite opinions is desired, one can draw on the "TT: Leaders" data.

Literature

  • Holsti, O.R. 1992: Public Opinion and Foreign Policy: Challenges to the Almond-Lippmann Consensus. In: International Studies Quarterly 36, 439-466.
  • Isernia, P., Juhász, Z., Rattinger, H. 2002: Foreign Policy and the Rational Public in Comparative Perspective. In: Journal of Conflict Resolution 46 (2), 201-224.
  • Wittkopf, E.R. 1990: Faces of Internationalism. Public Opinion and American Foreign Policy. Durham, London Duke University Press.
  • Rattinger, H. 2006: Öffentliche Meinung. In: S. Schmidt et al. (Hrsg.): Handbuch zur deutschen Außenpolitik. Wiesbaden, 313-325.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
01.09.14
01.12.14
Monfay
12:00
15:15
B6,23-25 room A 102

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

In this course, we will engage with contemporary research on the dynamics of civil war and post-war reconstruction. We will thus address questions such as: When does civil war break out? How are rebel groups formed and sustained and when do they use different forms of violence? How can conflict be ended and which political, economic, social, and psychological challenges are post-war societies faced with? How for example can former combatants be disarmed and reintegrated into society and what are the consequences of child soldiering? Finally we will also examine whether peacebuilding, development aid and democratization really contribute to a lasting peace. The focus of this research seminar will be to actively engage with this research and to practice your own research skills. You will therefore be asked to produce work throughout the semester, beyond doing the readings in preparation for class discussion as in regular seminars. You will need to develop your own research design as well as provide feedback on other student’s work.

Course Literature

  • Blattman, Christopher and Edward Miguel. 2009. Civil War. NBER Working Paper, No:14801.
  • Hegre, Havard and Nicholas Sambanis (2006): Sensitivity Analysis of Empirical Results on Civil War Onset. Journal of Conflict Resolution August 2006 vol. 50 no. 4 508-535
  • Gates, Scott. 2002: “Recruitment and Allegiance: The Microfoundations of Rebellion.” Journal of Conflict Resolution 46 : 111–130.
  • Humphreys, Macartan, and Jeremy M. Weinstein (2007): Demobilization and Reintegration. Journal of Conflict Resolution. 51(4), 531-567.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.09.14
03.12.14
Wednesday
10:15
13:30
A5,6 room B 143

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

In this seminar, we look at the two main regime types that characterize the modern world. We ask what distinguished one from the other, we ask what are the implications for the welfare of the individual citizen from living in a dictatorship and a democracy. We discuss the role of the international system in sustaining either regime form. We will read some classic texts such as Moores' Social Origins as well as cutting edge contemporary research on the utility of authoritarian legislature.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
05.09.14
05.12.14
Friday
13:45
17:00
A5,6 room B 143


Course Type: elective course

Course Number: POL/MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
05.12.14
Monday
10:15
11:45
A5,6 room B 317


Course Type: elective course

Course Number: POL/MET

Credits: 2

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
02.09.14
02.12.14
Tuesday
15:30
17:00
A5,6 room B 317


Course Type: elective course

Course Number: SOC/MET

Credits: 6

Course Content

The course introduces students to the fundamental principles, different paradigms and empirical-analytic research strategies in the social sciences. First, as an introduction into the epistemological foundations of social sciences we will read key texts of the philosophy of sciences and analytic philosophy. What is a reasonable explanation of social phenomena and how can it be verified? Furthermore, we will study the different paradigms in the social sciences and sociology in particular. In how far these complement or represent these disparate perspectives? Finally, the advantages and disadvantages of possible empirical research strategies and actual scientific practice will be discussed. In each session, first there will be a lecture on the topic, and thereafter, the texts are discussed. The course therefore requires regular reading of the required literature in preparation of the meeting and active participation in the discussions.


Recommended literature:

  • Della Porta, D., and Keating, M. (eds.) (2008). Approaches and Methodologies in the Social Sicenes. A Pluralist Perspective. Cambridge: Cambridge University Press.
  • Gerring, J. (2012). Social Science Methodology: A Unified Framework (Second Edition). Cambridge: Cambridge University Press.
  • Little, Daniel, Varieties of Social Explanation. An Introduction to the Philosophy of Social Science, Boulder, CO: Westview Press, 1991.

Exam: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
01.09.14
01.12.14
Monday
15:30
17:00
A5,6 room B 143

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

Schedule:

  • 1:45-2:45 PM: Sabine Carey, Political Science, Human Rights and Conflict research
  • 2:45-3:45 PM: Bernhard Ebbinghaus, Macrosociology, Comparative Research on Welfare Capitalism
  • 4:00-5:00 PM: Thomas Bräuninger, Political Economy
  • 5:00-6:00 PM: Nicole Rae Baerg, Political Science, International Organizations


Aug 28

  • 1:45-2:45 PM: Edgar Erdfelder, Cognitive Psychology
  • 2:45-3:45 PM: Arndt Bröder, Experimental Psychology
  • 4:00-5:00 PM Sabine Sonnentag, Organisational Psychology
  • 5:00-5:30 PM: final discussion

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
27.08.14
27.08.14
Wednesday
13:45
18:00
L9,7 room 208
28.08.14
28.08.14
Thursday
13:45
17:30
L9,7 room 308


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

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 Economist. 2nd edition. Manchester: Manchester University Press.
  • Carl P. Simon and Lawrence E. Blume. 1994. Mathematics for Economists. New York: W. W. Norton & Company. McGraw-Hill.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
26.08.14
26.08.14
Tuesday
10:15
13:15
L9,7 room 308
27.08.14
27.08.14
Wednesday
08:30
13:15
L9,7 room 308
28.08.14
28.08.14
Thursday
08:30
13:45
L9,7 room 308
04.09.14
04.09.14
Thursday
13:45
18:45
L9,7 room 308
05.09.14
05.09.14
Friday
08:30
13:15
L9,7 room 308
Exam
10.10.14
10.10.14
Friday
15:00
16:30
L9,7 room 308

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
12:00
13:30
A5,6 room B 317


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

In the second part of the course we focus on a particularly important aspect of social scientific explanation: causal inference. We first introduce the counterfactual theory of causal inference use this framework to investigate (and sometimes re-evaluate) the role of designed and natural experiments and conditioning methods such as regression and matching, in identifying causal effects. This part of the course will emphasize the applied computational aspects of these techniques.

We will briefly discuss alternatives to the counterfactual framework based on logic rather than statistics but spend rather longer examining the role of qualitative techniques in causal inference problems.

The course is a weighted mixture of lecture, discussion and practical exercises. Students should have access to a laptop and some ability with the R statistical language for the practical parts, in the second part of the course.

The course will assume a reasonable level of intuition about and experience with probability reasoning and also with multivariate statistics, up to approximately the level of generalized linear models. However, this is not an applied statistics course. Having a clear idea of what these methods do and what they must assume to do it will be more important than the ability to derive them from first principles. Indeed, one aim of the course is to sensitize you to the assumptions that will mostly be taken for granted in your subsequent formal modeling and applied statistics modules.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.14
05.12.14
Thursday
13:45
15:15
D7,27 room 307
02.10.14
05.12.14
Friday
10:15
11:45
D7,27 room 307


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, Dissertation Proposal Workshop


Course Content

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

Literature: References will be given during the course.

Course material will be provided in ILIAS.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
01.09.14
01.12.14
Monday
15:30
17:00
EO 259


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, Dissertation Proposal Workshop


Course Content

In this seminar we will discuss current issues in Social Cognition. Participants will be required to read current journal articles and to present and discuss them in class. Building either on a literature review or on a linkage to ongoing research projects at the University of Mannheim, participants will be asked to develop own research ideas. These research ideas will be presented in class and will provide a basis for in-class discussions.

Literature: Will be announced in class

Assessment type: By arrangement


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
01.09.14
01.12.14
Monday
13:45
15:15
A5,6 room B 318

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

As the writing course for the 2013 cohort of PhD students did not take place in spring 2014, they are automatically signed up to this one. The writing course for the 2014 cohort will take place during the spring semester 2015.


Course Content


The goal of this course is to provide guidance and constructive feedback on writing academic papers in English. Each session will guide students through techniques for writing and/or revision of a paper or other similar document. Between sessions, students will apply techniques learnt to their own texts, receiving frequent feedback on their papers and tips on how to improve their writing. By the end of the course each participant will have improved at least one paper to a publishable standard and should be able to approach their next paper with greater confidence.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Wednesday
10:15
11:45
L9,7 room 308


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.11.14
03.11.14
Monday
09:30
12:30
D7,27 room 307
03.11.14
03.11.14
Monday
14:00
16:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
09:30
12:30
D7,27 room 307
05.11.14
05.11.14
Wednesday
14:00
16:30
D7,27 room 307
17.11.14
17.11.14
Monday
09:30
12:30
D7,27 room 307
17.11.14
17.11.14
Monday
14:00
16:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
09:30
12:30
D7,27 room 307
19.11.14
19.11.14
Wednesday
14:00
16:30
D7,27 room 307

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden die multivariaten Auswertungsverfahren, die in der Vorlesung (M.Sc. Modul AA1/BA1) formal eingeführt werden, anhand empirischer Beispiele illustriert. Dabei wird die Umsetzung der Verfahren mit Hilfe statistischer Software vorgestellt und eingeübt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
12:00
13:30
Eo 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Die Vorlesung führt grundlegend in die Verfahren und Anwendungsprobleme der multivariaten Datenanalyse ein. Die Veranstaltung behandelt im Wesentlichen die Grundlagen des klassischen linearen Regressionsmodells, führt daneben aber auch neuere Techniken der statistischen Modellierung ein. Die Vorlesung behandelt dabei sowohl die Anwendung von Regressionsverfahren in der sozialwissenschaftlichen Forschung als auch die Grundlagen der Matrixalgebra sowie der statistischen Schätztheorie. Die in der Vorlesung vermittelten Kenntnisse werden durch eine begleitende Übung vertieft.

Literatur:

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

Die Vorlesung schließt mit einer Prüfungsleistung (Klausur) ab.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.09.14
02.12.14
Tuesday
13:45
15:15
A5,6 room B 317


Course Type: elective course

Course Number: MET

Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
02.09.14
02.12.14
Tuesday
15:30
17:00
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Course Content

This short workshop is designed to provide individuals with training in the conceptualization and application of multilevel statistical models. We will consider various models that are appropriate when outcomes of interest are known to be correlated or otherwise mutually dependent, such as when many repeated measurements are taken from a single individual across a sample of individuals, or when individuals are sampled across multiple sites (e.g. students nested within multiple classrooms). Linear multilevel models with be covered in detail and nonlinear (e.g. logistic and exponential) extensions will be introduced. The workshop is intended to be hands-on, and a number of working examples will be used throughout using the R software package. Attendees are encouraged to bring their own data for use in a final part of the workshop where we will consider the nuances of individual research questions.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
20.10.14
20.10.14
Monday
09:00
17:00
L7,3-5 room 158
21.10.14
21.10.14
Tuesday
10:00
15:00
L7,3-5 room 158

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

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

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


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


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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.14
04.12.14
Thursday
08:30
10:00
A5,6 room B 143


Course Type: elective course

Course Number: MET

Credits: 2

Course Content

This tutorial accompanies the course "Multivariate Analyses". The lab sessions will focus on the practical issues associated with quantitative methods, including obtaining and preparing datasets, how to use statistical software, which tests to use for different kinds of problems, how to graph data effectively for presentation and analysis, and how to interpret results. The seminar will also serve as a software tutorial. No prior knowledge of statistical programming is expected.

Graded assignments include several problem sets.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
04.09.14
04.12.14
Thursday
10:15
11:45
A5,6 room C 108

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In diesem Seminar werden wichtige, in der Psychologie häufig angewendete Modellklassen vertieft behandelt. Dabei stehen verallgemeinerte lineare Modelle (insbesondere logistische Regression), Strukturgleichungsmodelle und Mehrebenenanalysen im Vordergrund. Neben der Behandlung der theoretischen Grundlagen werden empirische Anwendungen dieser Modelle vorgestellt sowie praktische Übungen zu deren Spezifikation in Programmen wie SPSS, LISREL oder Mplus durchgeführt.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
01.12.14
Monday
13:45
15:14
EO 162, CIP-Pool

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 in combination with the type setting program LaTeX.
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. A basic introduction to R can be found under: http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
03.09.14
03.09.14
Wednesday
13:34
17:15
EO 162, CIP-Pool
10.09.14
10.09.14
Wednesday
13:45
17:15
Eo 162, CIP-Pool
24.09.14
24.09.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
08.10.14
08.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
22.10.14
22.10.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
05.11.14
05.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
19.11.14
19.11.14
Wednesday
13:45
17:15
EO 162, CIP-Pool
03.12.14
03.12.14
Wednesday
13:45
17:15
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Please refer to Prof. Bosnjak's course webdoc for details.

In a nutshell, meta-analysis can be described as a set of statistical methods for aggregating, summarizing, and drawing inferences from collections of thematically related studies. The key idea is to quantify the size, direction, and/or strength of an effect, and to cancel out sampling errors associated with individual studies.

Meta-analytic techniques have become the standard methods for aggregating the results from thematically related studies in the social and behavioral sciences. They can be used to describe a research field, to test and/or compare theories on a high level of abstraction, and to derive conclusions about the effectiveness of interventions.

The overall goal of this course is to provide a hands-on introduction into the different approaches belonging to the umbrella term ´meta-analysis´, to sketch their conceptual foundations, and to point participants to special issues and problems when evaluating and/or conducting meta-analytic studies. Moreover, each and every of the following topics addressed will be accompanied by exercises:

  • Definitions and processes: Systematic reviews and meta-analysis

  • What kind of scientific and applied research problems can be addressed with the aid of systematic reviews and meta-analysis?

  • Big picture: The systematic review/meta-analysis research cycle

  • Meta-analytic models: Hedges/Olkin (Homogeneity-based), Hunter/Schmidt (Psychometric)

  • Problem statement: Framing meta-analytic research questions

  • Systematically retrieving relevant primary studies: Literature research and selection

  • Extracting information from primary studies: Coding

  • Effect sizes: Basic types, estimation, conversion, and approximation strategies

  • Synthesizing the evidence: Mean effect size computation, moderator analysis techniques

  • Special issues: Dependent effect sizes, publication bias, study quality

  • Interpretation and reporting

  • Case studies and research critiques prepared by participants from various fields (tailored towards the academic backgrounds of the participants)

Office hours:

  • On appointment (can be held in person in L13,9, or via Skype)

Teaching material:

Topics and progress:

  1. Introduction

  • Aims and scope of the course

  • Eligibility and grading criteria

  • Participants´ background and research interests

  • Core concepts and prerequisites (preview)

  • Recommended readings


  1. Basic topics and overview

  • Core concepts and prerequisites

  • Meta-Analysis: Conceptual basics

  • HO-approach illustrated

  • HS-approach illustrated

  • ´Evergreen´ discussion topics


  1. Problem statement and data collection
    (systematic retrieval and selection of studies)

  • Problem statement

  • Systematic retrieval of studies

  • Systematic selection of studies


  1. Data extraction, coding, and unifying effect sizes

  • Principles of data extraction

  • Development of coding form and coding manual

  • Assessing study quality (especially Study DIAD)

  • Study DIAD

  • Determining inter-coder reliability

  • Unifying effect sizes


  1. Analysis and interpretation

  • Step-by-step analysis using MS Excel

    • FE analysis

    • RE analysis

  • Special analysis issues:

    • Homogeneity / heterogeneity indicators

    • Sensitivity analyses

  • Interpreting results:

    • Theory testing/development (SICT)

    • Description of a research field (Web nonresponse)

    • Estimating the effectiveness of interventions (Fluency-effects)


    1. Reporting

    • Meta-Analytic Reporting Standards

    • Reporting meta-analytic findings using ...

      • Tables

      • Graphs


      1. Special topics

      • How to develop a research critique for a published systematic review/meta-analysis?

      • Special issues and problems

        • Criticism of meta-analytic techniques

        • Simpson´s paradox

        • (Stochastically) dependent effect sizes

        • Publication bias (File drawer problem)


        1. Research critiques of selected meta-analyses (presented by participants)


        Recommended Textbooks:


        • Bornstein, M., Hedges, L.V., Higgins, J.P.T, & Rothstein, H.R. (2009). Introduction to Meta-Analysis. Chichester, UK: Wiley.

        • Card, N.A. (2011). Applied Meta-Analysis for the Social Sciences. New York: Guilford Press.

        • Cooper, H. (2010). Research Synthesis and Meta-Analysis: A Step-by-Step Approach. Thousand Oaks, CA: Sage.

        • Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.) (2009). Handbook of Research Synthesis (2nd ed.). New York: Russell Sage Foundation.

        • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Thousand Oaks, CA: Sage.

        • Lipsey, M.W., & Wilson, D.B. (2001). Practical Meta-analysis. Thousand Oaks: Sage.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
10.09.14
10.09.14
Wednesday
17:15
20:30
EO 256
24.09.14
24.09.14
Wednesday
17:15
20:30
Eo 256
01.10.14
01.10.14
Wednesday
17:15
20:30
EO 256
15.10.14
15.10.14
Wednesday
17:15
20:30
EO 256
22.10.14
22.10.14
Wednesday
17:15
20:30
EO 256
05.11.14
05.11.14
Wednesday
17:15
18:45
EO 256
12.11.14
12.11.14
Wednesday
17:15
20:30
EO 256
19.11.14
19.11.14
Wednesday
17:15
20:30
Eo 256
26.11.14
26.11.14
Wednesday
17:15
20:30
EO 256


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

OpenSesame is a free and open-source experiment builder for the social sciences. Simple experiments can be created via the graphical user interface (GUI). Complex experiments can be realized using the underlying programming language Python. The goal of the workshop is to provide an introduction to both approaches. The workshop consists of the following parts:

  1. Creating simple experiments with the GUI,
  2. Creating complex experiments using Python inline scripts,
  3. Individual projects,
  4. Specific applications.

The first two sessions of the workshop involve both structured input from the instructor as well as a number of practical exercises so that participants can directly explore the features of OpenSesame. In the subsequent break, each participant will work on his/her own experiment and can consult the instructor if needed. In the final session, the individual experiments will be discussed and an overview of selected special applications of OpenSesame will be given (e.g. the analyses of mouse movements, the creation of experiments for tablet devices etc.) - depending on participants' preferences.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.10.14
10.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
11.10.14
11.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool
24.10.14
24.10.14
Friday
10:15
15:15
EO 162, CIP-Pool
25.10.14
25.10.14
Saturday
10:15
17:00
EO 162, CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 14

Course Content

Personality traits and values play an important role in people’s everyday life. But do they also make a difference when it comes to political reasoning and political behavior? This seminar will address this question with its manifold ramifications. After an introduction of central theoretical concepts, we will discuss issues in measuring personality traits and values as well as the impact of traits and values on political attitudes and on political behavior. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available national and cross-national data sets.

Literature:

  • Caprara, G. V., and M. Vecchione. (2013). Personality Approaches to Political Behavior. In: L. Huddy, D. O. Sears, and J. S. Levy (eds.). The Oxford Handbook of Political Psychology. 2nd ed., Oxford: Oxford University Press, 23-58.
  • Mondak, J. J. (2010). Personality and the Foundations of Political Behavior. Cambridge: Cambridge University Press, 1-23.
  • Schwartz, S. H. (1994). Are There Universal Aspects in the Structure and Contents of Human Values? In: Journal of Social Issues 50 (4), 19-45.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
10:15
13:30
B6,23-25 room A 305

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

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


Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
02.09.14
02.12.14
Tuesday
09:30
11:00
Parkring 47, room 324


Course Type: elective course

Course Number: SOC/MET

Credits: 6

Course Content

The course introduces students to the fundamental principles, different paradigms and empirical-analytic research strategies in the social sciences. First, as an introduction into the epistemological foundations of social sciences we will read key texts of the philosophy of sciences and analytic philosophy. What is a reasonable explanation of social phenomena and how can it be verified? Furthermore, we will study the different paradigms in the social sciences and sociology in particular. In how far these complement or represent these disparate perspectives? Finally, the advantages and disadvantages of possible empirical research strategies and actual scientific practice will be discussed. In each session, first there will be a lecture on the topic, and thereafter, the texts are discussed. The course therefore requires regular reading of the required literature in preparation of the meeting and active participation in the discussions.


Recommended literature:

  • Della Porta, D., and Keating, M. (eds.) (2008). Approaches and Methodologies in the Social Sicenes. A Pluralist Perspective. Cambridge: Cambridge University Press.
  • Gerring, J. (2012). Social Science Methodology: A Unified Framework (Second Edition). Cambridge: Cambridge University Press.
  • Little, Daniel, Varieties of Social Explanation. An Introduction to the Philosophy of Social Science, Boulder, CO: Westview Press, 1991.

Exam: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
01.09.14
01.12.14
Monday
15:30
17:00
A5,6 room B 143

Register

Social Sciences Fall 2014

Dissertation Tutorial - Sociology
Dissertation Tutorial - Sociology
BAS
Current Research Perspectives
BAS
Mathematics for Social Scientists
MET
Crafting Social Science Research
MET
Theory Building and Casual Inference
RES
CDSS Workshop: Sociology
RES
English Academic Writing
MET
Bayesian Statistics for Social Scientists I
MET
Computergestützte multivariate Analysen
MET
Cross Sectional Data Analysis
MET
Cross Sectional Data Analysis (Tutorial)
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Linear-Mixed Models in R
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Multivariate Analyses
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Multivariate Analyses (Tutorial)
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Spezielle Verfahren der Datenerhebung und Datenanalyse
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Statistics in R and beyond
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Systematic Reviews and Meta-Analysis
MET/PSY
Programming Experiments with OpenSesame
SOC
Comparing Welfare Capitalism in Europe and USA
SOC/MET
Logic of the Social Sciences
RES
CDSS Workshop: Political Science
POL
Selected Topics in Comparative Politics: Analyzing the 2013 Federal Election
POL
Selected Topics in Comparative Politics: Legislative Politics
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Selected Topics in Comparative Politics: Personality, Values and Politics
POL
Selected Topics in Comparative Politics: Public Attention on Foreign Policy and Security in the U.S. and Germany
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Selected Topics in International Politics: Civil War and Post-Conflict Research
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Selected Topics in International Politics: Democracy, Dictatorship and the International System
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Game Theory
POL/MET
Game Theory (Tutorial)
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CDSS Workshop: Research in Cognitive Psychology
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CDSS Workshop: Research in Social Cognition
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Selected Topics in Comparative Politics: Personality, Values and Politics
PSY
Advanced Social and Economic Cognition