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 2015

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

Schedule:

02. October 2015

09:30-10:15 Edgar Erdfelder, Cognitive Psychology & Individual Differences
10:15-11:00 Arndt Bröder, Experimental Psychology
Break
11:15-12:00 Thorsten Meiser, Research Methods & Psychological Assessment
12:00-12:45 Thomas König, Political Science

 

01. September 2015

13:45 - 14:45: Thomas Gschwend, Quantitative Methods in the Social Sciences
14:45 - 15:45: Sabine Carey, Conflict Research
Break
16:00 - 17:00: Michaela Wänke, Consumer & Economic Psychology
17:00 - 18:00: Sabine Sonnentag, Work & Organizational Psychology


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.15
02.10.15
Friday
09:30
12:45
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 fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

Basic readings:

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


Additional readings:

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
02.09.14
Tuesday & Wednesday
10:15
13:30
L9,7 room 308
03.09.14
03.09.14
Thursday
12:00
13:30
L9,7 room 308
03.09.15
03.09.15
Thursday
17:15
18:45
L9,7 room 308
04.09.15
04.09.15
Friday
12:00
18:45
L9,7 room 308
11.09.15
11.09.15
Friday
10:30
17:30
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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
Mondays
13:45
15:30
D7,27, room 307


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.09.15
10.12.15
Thursdays
13:45
15:15
L9,7, room 308

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Participation is mandatory for second and third year CDSS students of Sociology and Political Science.
Participation is recommended for first year CDSS and visiting PhD students, as well as for later CDSS PhD candidates, but to no credit.

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


Course Content

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

In order to receive useful feedback, participants will circulate their paper and two related published pieces of research one week before their talk. If you plan to attend the talk and wish to receive the papers please contact Sonja Collet.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
09.09.15
09.12.15
Wednesdays
12:00
13:30
Parkring 47, room 217


Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.



Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

CSSR, TBCI, Dissertation Proposal


Course Content

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



Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
06.11.15
27.11.15
Fridays
09:00
16:00
L9,7, room 308


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus of this course lies on the introduction to statistical models and estimators beyond linear regression useful to social scientists. We first repeat and deepen the basics of the classical linear regression model (OLS). A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will then discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to 1st) the maximum likelihood estimator and 2nd) to limited dependent variable models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial).

Literature:

  • Cameron, C.A. and P.K. Trivedi. (1998). Regression Analysis of Count Data . Cambridge: Cambridge University Press.
  • Greene, W.H. (2003). Econometric Analysis. 5th ed. Upper Saddle River: Prentice Hall.
  • Gujarati, D.N. (2003). Basic Econometrics. 4th ed. Boston: McGraw-Hill.
  • Maddala, G.S. (2001). Introduction to Econometrics. 3rd ed. Chichester: Wiley.
  • Morgan, S.L. and C. Winship. 2007. Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge: Cambridge University Press.
  • Long, J.S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage.
  • Verbeek, M. (2008). A Guide to Modern Econometrics. 3rd ed. Chichester: Wiley.
  • Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.
  • Wooldridge, J.M. (2008). Introductory Econometrics. A Modern Approach. 4th ed. Mason, OH: Thompson

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.15
08.12.15
Tuesdays
13:45
15:15
A5,6 room B 317
Tutorial
08.09.15
08.12.15
Tuesdays
15:30
17:00
A5,6, C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
17.09.15
17.09.15
Thursday
13:45
17:00
A5,6, room B244
25.09.15
25.09.15
Friday
08:30
17:00
B6,23-25, room A103
26.09.15
26.09.15
Saturday
08:30
17:00
B6,23-25, rooms A102 & A103
09.10.15
09.10.15
Friday
12:00
13:30
A5,6, room C -108 (PC Lab)
09.10.15
09.10.15
Friday
13:30
17:00
B6,23-25, room A102
10.10.15
10.10.15
Saturday
10:00
15:15
A5,6, rooms B317 & B318
Tutorial
09.10.15
09.10.15
Friday
08:30
11:45
A5,6, room C -108 (PC Lab)
10.10.15
10.10.15
Saturday
15:45
19:00
A5,6, room C -108 (PC Lab)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
10.09.15
12.12.15
Thursdays
08:30
10:00
A5,6 room B 143
Tutorial
10.09.15
10.12.15
Thursdays
10:15
11:45
A5,6, C -108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

This seminar explores the causes and consequences of missing data in applied survey research, and the methods frequently employed to compensate for it.  Specifically, students will take away from the course a much deeper understanding of weight adjustment methods and imputation techniques.  We will also review and critique the numerous competing metrics that have been proposed in the literature for measuring the magnitude of nonresponse bias and nonresponse error, as well as discuss other practical tools for assessing the impact of missing data on sample-based estimates and inferences.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.15
09.12.15
Wednesdays
15:30
17:00
A5,6, room B318

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Course Content


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
16.09.15
16.12.15
Wednesdays (bi-weekly)
17:30
20:30
tba.


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.

Literature:
OpenSesame can be downloaded under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. doi:10.3758/s13428-011-0168-7


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
09.10.15
09.10.15
Friday
10:15
15:15
EO 162 CIP Pool
10.10.15
10.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool
23.10.15
23.10.15
Friday
10:15
11:45
EO 259
23.10.15
23.10.15
Friday
12:00
15:15
EO 162 CIP-Pool
24.10.15
24.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

The basic design of comparative research is simple: one examines either the same phenomena in different contexts or different phenomena in similar contexts. But what is ‘the same’? Is a French socialist party ‘the same’ as a left-wing political party in Norway? Is a Swedish community agency for social caring ‘the same’ as a religious social association in Italy because they perform similar tasks? Is collecting financial support for a German club ‘the same’ as ‘fundraising’ in Scotland although the last phrase cannot even be translated into German? When, then, is a phenomenon ‘the same’ in different contexts or is it allowed to speak of ‘similar’ contexts? The main topics of the seminar are (1) the logic of comparative research, (2) assessing comparability, and (3) establishing equivalence in cross-national and cross-cultural research. Participants are invited to develop equivalent measures for various political orientations (political participation, voluntary activities, norms of citizenship, etc.) by using available cross-national and longitudinal data sets (especially ESS and WVS).

A total of ten credit points (10 ECTS) can be obtained for a paper (6,500 words), the oral presentation of this paper, as well as active participation during the sessions.

Core Literature:

  • Sartori, Giovanni. 1970. "Concept Misformation in Comparative Politics." American Political Science Review 64 (4): 1033-1053.
  • Rathke, Julia. 2007. "Achieving comparability of secondary data." In: Thomas Gschwend/Frank Schimmelfennig (Hg.). Research Design in Political Science. How to Practice What They Preach. Houndmills: Palgrave: 103-126.
  • van de Vijver, Fons J.R., und Kwok Leung. 2011. "Equivalence and bias: A review of concepts, models, and data analytic procedures." In David Matsumoto/Fons J.R. Van de Vijver (Hg.). Cross-Cultural Research Methods in Psychology. Cambridge: Cambridge University Press: 17-45.
  • van Deth, Jan W. 2009. "Establishing Equivalence." In: Todd Landman/Neil Robinson (Hg.). The Sage Handbook of Comparative Politics. London: Sage: 84-100.
  • van Deth, Jan W. (Hg.). 2013. Comparative Politics. The Problem of Equivalence. Colchester: ECPR Press.

Additional literature on specific topics will be offered during the seminar.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.15
08.12.15
Tuesdays
10:15
11:45
A5,6, room B317

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.15
07.12.15
Mondays
12:00
13:30
B6,23-25, room A102


Course Type: elective course

Course Number: POL

Credits: 10

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.15
08.12.15
Tuesdays
12:00
13:30
A5,6, room B318

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

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

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.

Introductory Literature:

  • Jones, Bradford S. 2008. Multilevel Models, in: Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier (eds.). Oxford Handbook of Political Methodology. Oxford: Oxford University Press: 605- 623.
  • McGraw, Kathleen, M. 2006. Why and How Psychology Matters, in: Robert E. Goodin and Charles Tilly (eds.). Oxford Handbook of Contextual Political Analysis. Oxford: Oxford University Press: 131-156.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.15
08.12.15
Tuesdays
12:00
13:30
A5,6, room B143

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.15
07.12.15
Mondays
15:30
17:00
A5,6, room B318

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
10.09.15
10.12.15
Thirsdays
12:00
13:30
A5,6, room B317


Course Type: elective course

Course Number: POL/MET

Credits: 6+2

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
07.12.15
Mondays
10:15
11:45
A5,6 room B 243
Tutorial
09.09.15
09.12.15
Wednesdays
17:15
18:45
A5,6, room C 012
25.09.15
25.09.15
Friday
15:30
17:00
A5,6, room C 012


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 do these complement or represent 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
Lecture
07.09.15
07.12.15
Mondays
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 doctoral students to the theoretically informed research approaches and substantive research fields that build the strongholds of social science research in Mannheim. A series of talks provides first year doctoral students with an overview of current debates and ongoing research in the fields of psychology, political science and sociology. CDSS faculty members will present an overview of their research fields, report on prime examples of their current research, and provide an outlook on potential topics for future research. Doctoral students will have the opportunity to discuss the short talks and the required readings with the respective lecturer during the remaining discussion time.

Schedule:

02. October 2015

09:30-10:15 Edgar Erdfelder, Cognitive Psychology & Individual Differences
10:15-11:00 Arndt Bröder, Experimental Psychology
Break
11:15-12:00 Thorsten Meiser, Research Methods & Psychological Assessment
12:00-12:45 Thomas König, Political Science

 

01. September 2015

13:45 - 14:45: Thomas Gschwend, Quantitative Methods in the Social Sciences
14:45 - 15:45: Sabine Carey, Conflict Research
Break
16:00 - 17:00: Michaela Wänke, Consumer & Economic Psychology
17:00 - 18:00: Sabine Sonnentag, Work & Organizational Psychology


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.15
02.10.15
Friday
09:30
12:45
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 fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

Basic readings:

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


Additional readings:

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
02.09.14
Tuesday & Wednesday
10:15
13:30
L9,7 room 308
03.09.14
03.09.14
Thursday
12:00
13:30
L9,7 room 308
03.09.15
03.09.15
Thursday
17:15
18:45
L9,7 room 308
04.09.15
04.09.15
Friday
12:00
18:45
L9,7 room 308
11.09.15
11.09.15
Friday
10:30
17:30
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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
Mondays
13:45
15:30
D7,27, room 307


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.09.15
10.12.15
Thursdays
13:45
15:15
L9,7, room 308


Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

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



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.

Course schedule


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
Monday
13:45
15:15
A5,6 room B 318

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

In dieser Vorlesung werden die formalen Grundlagen und zentralen Verfahren der multivariaten Statistik eingeführt und empirisch illustriert. Dabei werden zunächst die multiple Regression, das Allgemeine Lineare Modell und die Mehrebenenanalyse behandelt. Darauf aufbauend werden multivariate Auswertungsverfahren für Mittelwertsvergleiche und zur Diskrimination sowie Verfahren der exploratorischen Faktorenanalyse vorgestellt. Abschließend erfolgt eine Einführung in lineare Strukturgleichungsmodelle.

Zu den einzelnen Verfahren werden die mathematischen Grundlagen dargelegt und Anwendungsmöglichkeiten und Einsatzgebiete in der psychologischen Forschung diskutiert. Als optionale Vertiefung zu der Vorlesung wird ein Kurs AC2/BC2 angeboten, in dem die konkrete Anwendung der Verfahren und die Interpretation der Ergebnisse anhand empirischer Datensätze eingeübt werden können.

Readings

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Erlbaum.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford.
  • Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling. Mahwah, NJ: Erlbaum.
  • Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York: Routledge.
  • Stevens, J. P. (2009). Applied multivariate statistics for the social sciences. New York: Routledge.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston: Pearson.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.15
08.12.15
Tuesdays
08:30
10:00
Schloss, EO 145


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

Bei diesem Seminar handelt es sich um ein optionales Angebot, das begleitend zur Vorlesung "AA1/BA1: Multivariate Auswertungsverfahren" angeboten wird.

Der Fokus des Seminars liegt auf der praktischen Anwendung der theoretischen Inhalte der Vorlesung (insbesondere Regression und Allgemeines Lineares Modell, Mehrebenenanalyse, sowie exploratorische und konfirmatorische Faktorenanalyse). Dazu werden Beispieldatensätze mit gängiger statistischer Software (z.B. SPSS) ausgewertet und die Ergebnisse interpretiert. Dies erfolgt durch Demonstrationen seitens des Dozenten und in angeleiteter Einzel- und Gruppenarbeit. Vorkenntnisse in SPSS sind nicht nötig.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Tutorial
08.09.15
08.12.15
Tuesdays
12:00
13:30
Schloss, EO 162 CIP-Pool


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
Workshop
06.11.15
27.11.15
Fridays
09:00
16:00
L9,7, room 308


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus of this course lies on the introduction to statistical models and estimators beyond linear regression useful to social scientists. We first repeat and deepen the basics of the classical linear regression model (OLS). A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will then discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to 1st) the maximum likelihood estimator and 2nd) to limited dependent variable models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial).

Literature:

  • Cameron, C.A. and P.K. Trivedi. (1998). Regression Analysis of Count Data . Cambridge: Cambridge University Press.
  • Greene, W.H. (2003). Econometric Analysis. 5th ed. Upper Saddle River: Prentice Hall.
  • Gujarati, D.N. (2003). Basic Econometrics. 4th ed. Boston: McGraw-Hill.
  • Maddala, G.S. (2001). Introduction to Econometrics. 3rd ed. Chichester: Wiley.
  • Morgan, S.L. and C. Winship. 2007. Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge: Cambridge University Press.
  • Long, J.S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage.
  • Verbeek, M. (2008). A Guide to Modern Econometrics. 3rd ed. Chichester: Wiley.
  • Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.
  • Wooldridge, J.M. (2008). Introductory Econometrics. A Modern Approach. 4th ed. Mason, OH: Thompson

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.15
08.12.15
Tuesdays
13:45
15:15
A5,6 room B 317
Tutorial
08.09.15
08.12.15
Tuesdays
15:30
17:00
A5,6, C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
17.09.15
17.09.15
Thursday
13:45
17:00
A5,6, room B244
25.09.15
25.09.15
Friday
08:30
17:00
B6,23-25, room A103
26.09.15
26.09.15
Saturday
08:30
17:00
B6,23-25, rooms A102 & A103
09.10.15
09.10.15
Friday
12:00
13:30
A5,6, room C -108 (PC Lab)
09.10.15
09.10.15
Friday
13:30
17:00
B6,23-25, room A102
10.10.15
10.10.15
Saturday
10:00
15:15
A5,6, rooms B317 & B318
Tutorial
09.10.15
09.10.15
Friday
08:30
11:45
A5,6, room C -108 (PC Lab)
10.10.15
10.10.15
Saturday
15:45
19:00
A5,6, room C -108 (PC Lab)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
10.09.15
12.12.15
Thursdays
08:30
10:00
A5,6 room B 143
Tutorial
10.09.15
10.12.15
Thursdays
10:15
11:45
A5,6, C -108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

This seminar explores the causes and consequences of missing data in applied survey research, and the methods frequently employed to compensate for it.  Specifically, students will take away from the course a much deeper understanding of weight adjustment methods and imputation techniques.  We will also review and critique the numerous competing metrics that have been proposed in the literature for measuring the magnitude of nonresponse bias and nonresponse error, as well as discuss other practical tools for assessing the impact of missing data on sample-based estimates and inferences.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.15
09.12.15
Wednesdays
15:30
17:00
A5,6, room B318

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Course Content


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
16.09.15
16.12.15
Wednesdays (bi-weekly)
17:30
20:30
tba.

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
18.09.15
11.12.15
Friday, every 2nd Friday from 18 September
13:45
17:00
EO 162, CIP-Pool


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.

Literature:
OpenSesame can be downloaded under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. doi:10.3758/s13428-011-0168-7


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
09.10.15
09.10.15
Friday
10:15
15:15
EO 162 CIP Pool
10.10.15
10.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool
23.10.15
23.10.15
Friday
10:15
11:45
EO 259
23.10.15
23.10.15
Friday
12:00
15:15
EO 162 CIP-Pool
24.10.15
24.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

Social cognitive neuroscience is an interdisciplinary field devoted to understanding how biological systems implement social processes and behavior and to using biological concepts and methods to inform and refine theories of social processes and behavior. The objective of this lecture (part I & II) is to introduce the concepts and methods of social cognitive neuroscience. Part I of the lectures addresses the following topics: methods of social cognitive neuroscience (e.g. electrophysiological methods; functional imaging; lesion methods, disruption of functions using TMS), evolutionary origins of social intelligence and culture (e.g., social intelligence hypothesis; evolutionary origins of culture; cultural skills), emotion and motivation (e.g., different categories of emotion in the brain; neural signatures of reward and punishment), reading faces and bodies (e.g., perceiving faces and bodies; joint attention from perception to intention; trait inferences from faces and bodies), and understanding others (e.g., empathy and simulation theory; theory of mind and social mentalizing; social brain disorders).


Part II of the lectures is to follow in spring 2016.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
11.12.15
Fridays
12:00
13:30
A5,6, room B 243

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

We begin our lives without beliefs, yet our brain comes equipped with a natural propensity to believe in almost everything. Beliefs can be seen as forms of mental representations; they help us to organize the world in meaningful ways, give us sense of ourselves, and guide us in our moral pursuits. This seminar investigates whether the everyday understanding of beliefs is valid. If a neuroscientific explanation of this phenomenon exists, then functional neuroimaging, lesion method, and examining neurophysiological correlates of belief states provide valid approaches for studying the neural basis of human belief systems. The seminar provides on overview on how different belief systems are implemented in the human brain and explains how the neural underpinnings of beliefs mediate a wide range of explicit and implicit behaviors ranging from everyday decision-making to the practice of religion, drawing on inferences from philosophy, psychology, psychiatry, and neurosciences.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
10.09.15
10.12.15
Thursdays
08:30
10:00
A5,6, room B 244


Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

Please check with individual chairs in the Psychology Department for dates and times of research/project seminar offers  as well as registration.


Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

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


Course Content

This course provides an overview of core topics in work and organizational psychology. We will focus on recent theoretical approaches and empirical research findings (meta-analyses). In addition, we will discuss practical implications of core research findings.

Topics include: Work motivation, stress and health, leadership, teams, personnel selection.
Methods comprise: Lecture, reading (as homework), teamwork assignments during class.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
10.09.15
10.12.15
Thursdays
17:15
18:45
Schloss, room EO 242
29.09.15
29.09.15
Tuesday (replacement for Nov 5)
19:00
20:30
A5,6, room B 244

Lecturer(s)


Course Type: elective course

Course Number: PSY/MET

Credits: 4

Course Content

Functional magnetic resonance imaging (fMRI) is a non-invasive technique for studying brain activity that has become an important addition to the toolbox of experimental psychologists, but substantial knowledge is required to implement a successful fMRI experiment. The purpose of this seminars is to provide a solid foundation in neuroimaging data acquisition and analysis. The seminar covers all aspects of fMRI-based data acquisition, including motivation for using fMRI, physics behind MRI scanners, MRI safety, basic principles of MR signal generation, spatial and temporal properties of fMRI, signal and noise in fMRI, MR contrast mechanism and pulse sequences, basic principles of experimental designs, and fMRI data analysis methods. The seminar is intended for potential users of the technique, who wish to incorporate fMRI into their work.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
11.09.15
11.12.15
Fridays
08:30
10:00
B6,23-25, room A102


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 do these complement or represent 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
Lecture
07.09.15
07.12.15
Mondays
15:30
17:00
A5,6 room B 143


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
08.09.15
08.12.15
Tuesday
19:00
20:30


Course Type: core course

Course Content

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Colloquium
09.09.15
09.12.15
Wednesday
18:00
20:30

Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

Schedule:

02. October 2015

09:30-10:15 Edgar Erdfelder, Cognitive Psychology & Individual Differences
10:15-11:00 Arndt Bröder, Experimental Psychology
Break
11:15-12:00 Thorsten Meiser, Research Methods & Psychological Assessment
12:00-12:45 Thomas König, Political Science

 

01. September 2015

13:45 - 14:45: Thomas Gschwend, Quantitative Methods in the Social Sciences
14:45 - 15:45: Sabine Carey, Conflict Research
Break
16:00 - 17:00: Michaela Wänke, Consumer & Economic Psychology
17:00 - 18:00: Sabine Sonnentag, Work & Organizational Psychology


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.15
02.10.15
Friday
09:30
12:45
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 fields cannot be overestimated. However, using these methods may be cumbersome without knowledge of the fundamental math behind. This course is to provide you with some of these fundamentals, which are beneficiary to your understanding of formal methods (like game theory) and statistics during your PhD studies here in Mannheim. It is therefore highly recommended to take the course at the beginning of your PhD.

Basic readings:

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


Additional readings:

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.09.14
02.09.14
Tuesday & Wednesday
10:15
13:30
L9,7 room 308
03.09.14
03.09.14
Thursday
12:00
13:30
L9,7 room 308
03.09.15
03.09.15
Thursday
17:15
18:45
L9,7 room 308
04.09.15
04.09.15
Friday
12:00
18:45
L9,7 room 308
11.09.15
11.09.15
Friday
10:30
17:30
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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
Mondays
13:45
15:30
D7,27, room 307


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
10.09.15
10.12.15
Thursdays
13:45
15:15
L9,7, room 308

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Participation is mandatory for second and third year CDSS students of Sociology and Political Science.
Participation is recommended for first year CDSS and visiting PhD students, as well as for later CDSS PhD candidates, but to no credit.

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


Course Content

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

In order to receive useful feedback, participants will circulate their paper and two related published pieces of research one week before their talk. If you plan to attend the talk and wish to receive the papers please contact Sonja Collet.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
09.09.15
09.12.15
Wednesdays
12:00
13:30
Parkring 47, room 217


Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.



Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

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

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
06.11.15
27.11.15
Fridays
09:00
16:00
L9,7, room 308


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

The main focus of this course lies on the introduction to statistical models and estimators beyond linear regression useful to social scientists. We first repeat and deepen the basics of the classical linear regression model (OLS). A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will then discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to 1st) the maximum likelihood estimator and 2nd) to limited dependent variable models for binary choice decisions (Logit, Probit), ordinal dependent variables, and count data (Poisson, Negative Binomial).

Literature:

  • Cameron, C.A. and P.K. Trivedi. (1998). Regression Analysis of Count Data . Cambridge: Cambridge University Press.
  • Greene, W.H. (2003). Econometric Analysis. 5th ed. Upper Saddle River: Prentice Hall.
  • Gujarati, D.N. (2003). Basic Econometrics. 4th ed. Boston: McGraw-Hill.
  • Maddala, G.S. (2001). Introduction to Econometrics. 3rd ed. Chichester: Wiley.
  • Morgan, S.L. and C. Winship. 2007. Counterfactuals and Causal Inference. Methods and Principles for Social Research. Cambridge: Cambridge University Press.
  • Long, J.S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage.
  • Verbeek, M. (2008). A Guide to Modern Econometrics. 3rd ed. Chichester: Wiley.
  • Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.
  • Wooldridge, J.M. (2008). Introductory Econometrics. A Modern Approach. 4th ed. Mason, OH: Thompson

 Assessment type: written exam


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.15
08.12.15
Tuesdays
13:45
15:15
A5,6 room B 317
Tutorial
08.09.15
08.12.15
Tuesdays
15:30
17:00
A5,6, C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
17.09.15
17.09.15
Thursday
13:45
17:00
A5,6, room B244
25.09.15
25.09.15
Friday
08:30
17:00
B6,23-25, room A103
26.09.15
26.09.15
Saturday
08:30
17:00
B6,23-25, rooms A102 & A103
09.10.15
09.10.15
Friday
12:00
13:30
A5,6, room C -108 (PC Lab)
09.10.15
09.10.15
Friday
13:30
17:00
B6,23-25, room A102
10.10.15
10.10.15
Saturday
10:00
15:15
A5,6, rooms B317 & B318
Tutorial
09.10.15
09.10.15
Friday
08:30
11:45
A5,6, room C -108 (PC Lab)
10.10.15
10.10.15
Saturday
15:45
19:00
A5,6, room C -108 (PC Lab)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
10.09.15
12.12.15
Thursdays
08:30
10:00
A5,6 room B 143
Tutorial
10.09.15
10.12.15
Thursdays
10:15
11:45
A5,6, C -108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

This seminar explores the causes and consequences of missing data in applied survey research, and the methods frequently employed to compensate for it.  Specifically, students will take away from the course a much deeper understanding of weight adjustment methods and imputation techniques.  We will also review and critique the numerous competing metrics that have been proposed in the literature for measuring the magnitude of nonresponse bias and nonresponse error, as well as discuss other practical tools for assessing the impact of missing data on sample-based estimates and inferences.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.15
09.12.15
Wednesdays
15:30
17:00
A5,6, room B318

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Course Content


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
16.09.15
16.12.15
Wednesdays (bi-weekly)
17:30
20:30
tba.


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.

Literature:
OpenSesame can be downloaded under http://osdoc.cogsci.nl/index.html, where you can also find an extensive documentation.
Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. doi:10.3758/s13428-011-0168-7


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
09.10.15
09.10.15
Friday
10:15
15:15
EO 162 CIP Pool
10.10.15
10.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool
23.10.15
23.10.15
Friday
10:15
11:45
EO 259
23.10.15
23.10.15
Friday
12:00
15:15
EO 162 CIP-Pool
24.10.15
24.10.15
Saturday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.15
09.12.15
Wednesdays
08:30
10:00
A5,6, room B 318

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Mit dem Begriff „private governance“ werden diverse Formen nicht-staatlicher, oft transnationaler Regulierung wirtschaftlicher Aktivitäten bezeichnet, wie z.B. Zertifikate, Standards, Rankings oder Labels. Sie sind meist nicht obligatorisch und bedienen sich der Marktlogik, um den Markt zu regulieren. Der Ansatz unterscheidet sich auf der einen Seite von der Idee der staatlichen Zwangsregulierung, auf der anderen Seite aber von der liberalen Idee der Nichtregulierung („der Markt reguliert sich selbst“).

In der Literatur herrscht Einigkeit darüber, dass „private governance“ durch die Globalisierung und die neoliberalen Deregulierungspolitik stark befördert wurde. Offen bleibt allerdings, ob die „weichen“ privaten oder zivilgesellschaftlichen Regulierungsformen die „harten“ staatlichen Regulierungsmaßnahmen ergänzen oder vielmehr verdrängen („crowding out“), ob damit insgesamt mehr oder weniger Regulierung herauskommt und welchen Stellenwert „private governance“ für die Wirtschaft insgesamt hat.


In der Veranstaltung sollen die unterschiedlichen Formen der nicht-staatlichen Regulierung dargestellt, analysiert und unter gesamtregulatorischen Gesichtspunkten bewertet werden.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.15
09.12.15
Wednesdays
12:00
13:30
B6,23-25, room A301


Course Type: elective course

Course Number: SOC

Credits: 8

Course Content

The course "Selected Topics in Educational and Migrational Studies" is intended to cover recent debates, controversies, and latest research on immigration and educational topics. The syllabus and readings will be available at the beginning of the course.

Requirements:
- Paper (max. 6500 words)
- 2-3 presentations during the course


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.15
08.12.15
Tuesdays
12:00
13:30
B6, 23-25, room A 303


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

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

Available Subfields:

  • A: Families, Education & Labour Markets
  • B: Migration & Integration
  • C: Economy & the Welfare State


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 do these complement or represent 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
Lecture
07.09.15
07.12.15
Mondays
15:30
17:00
A5,6 room B 143

Register

Social Sciences Fall 2015

Dissertation Tutorial - Sociology
Dissertation Tutorial - Sociology
BAS
Current Research Perspectives
BAS
Mathematics for Social Scientists
MET
Crafting Social Science Research
MET
Theory Building and Causal Inference
RES
CDSS Workshop: Political Science & Sociology
RES
MZES A Colloquium "European Societies and their Integration"
MET
Bayesian Statistics for Social Scientists I
MET
Cross Sectional Data Analysis (Theory + Lab Course)
MET
Data and Measurement (Theory + Lab Course)
MET
Multivariate Analyses (Theory + Lab Course)
MET
Nonresponse/Imputation
MET
Research Seminar on Systematic Reviews and Meta-analysis
MET/PSY
Programming Experiments with OpenSesame
SOC
Gender and Work
SOC
Private Governance
SOC
Selected Topics in Educational and Migrational Studies
SOC
Selected Topics in Sociology
SOC/MET
Logic of the Social Sciences
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
POL
Advanced Topics in Comparative Politics: Comparability and Equivalence in Cross-National Research on Political Culture
POL
Advanced Topics in Comparative Politics: Elections in Comparative Perspective
POL
Advanced Topics in Comparative Politics: Legislative Politics
POL
Advanced Topics in Comparative Politics: Political Behavior in Context
POL
Advanced Topics in International Politics: Coalition Politics in International Relations
POL
Advanced Topics in International Politics: Exodus - Conflict, Migration and Refugees
POL/MET
Game Theory (Theory + Tutorial)
RES
AC3/BC3 Colloquia I
RES
CDSS Workshop: Research in Cognitive Psychology
RES
CDSS Workshop: Research in Social Cognition
MET
AA1/BA1: Multivariate Auswertungsverfahren
MET
AC2/BC2: Computergestützte multivariate Analysen
MET
Statistics in R and beyond
PSY
Introduction to Social Cognitive Neuroscience I
PSY
Neural Basis of Human Belief Systems
PSY
Project Seminars/Selected Topics in Psychology
PSY
Work and Organizational Psychology
PSY/MET
Introduction to Functional Magnetic Resonance Imaging