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 2016

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.

The course will take place on 09 and 23 September 2016 in L9, 7, room 308 starting at 1.30pm

The exam is set for 29 September 2016 starting at 1.45pmin room O135, 'Saal der starken Marken', Schloss Ostflügel

Course schedule

 


Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

The exam is scheduled for 15 December 2016 from 9am to 11am in room A203 in B6, 23-25, entrance A, 2nd floor

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
07.09.16
06.12.16
Wednesday
08:30
10:00
308 in L9, 7

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
06.09.16
06.12.16
Tuesday
12:00
13:30
B143 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.10.16
07.10.16
Wednesday to Friday
10:30
17:30
O 126, Schloss Ostflügel


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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.16
07.12.16
Wednesday
12:00
13:30
B 243 in A 5, 6 entrance B


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.


Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The amount of digital data generated as a by-product in society is growing fast, e.g., data from satellites, sensors, transactions, administrative processes, social media and smartphones. This type of data is characterized by high volume, high velocity, high variety and is often called big data. The hope is to gain insights from this data for different areas such as e.g., health and crime prevention, planning of infrastructures, and business decisions. In particular on the economic statistics side, this interest in growing rapidly. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for agencies that in the past relied primarily on survey research. However, data quality frameworks well established in statistics production still hold. Thus the goal of this lecture is to equip the next generation of social and economic scientists as well as survey researchers with the right tools to face these data. The lecture uses specially curated data sets and a working example that runs through the entire course. The lecture is paired with a hands-on lab session in which students apply all learned techniques through a worked example relevant to core work of the Federal Statistical Agencies.


Competences acquired

Learn how to think about data analysis to solve social problems using and combining large quantities of heterogeneous data from a variety of different sources. Learn how to evaluate which data are appropriate to a given research question and statistical need. Learn the different data quality frameworks and learn how to apply them. Learn the basic computational skills required for data analytics (for text-mining, large-scale data integration and visualization), typically not taught in social science, economics, statistics or survey courses. Learn how to apply statistical and data quality frameworks to big data problems. Identify new approaches to creating and displaying information for Federal Agencies.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
10:15
11:45
B 6, 23-25 entrance A, room A301
27.10.16
Thursday
12:00
18:45
tbd
28.10.16
Friday
10:15
18:45
tbd
07.11.16
Monday
10:15
18:45
tbd


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

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

Literature:

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

 Assessment type: written exam


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.16
06.12.16
Tuesday
13:45
15:15
A5,6 room B 317
Tutorial
06.09.16
06.12.16
Tuesday
15:30
17:00
A5,6 room C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - irregular times & dates please check Portal 2 web page for all further details
09.09.16
17.09.15
Wednesday, Friday and Saturday
08:30
18:45
Rooms A 102 and A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Description
In addition to a thorough understanding of the substantive field you are studying you need firm methodological and statistical knowledge in order to successfully conduct quantitative social research. This seminar will give you the opportunity to apply and expand your knowledge of social research by replicating published research findings.

The research that we are going to replicate was conducted with data from publicly available survey data like the European Social Survey (ESS), the International Social Survey Programme (ISSP) or the European Values Study. Data from surveys like these have several advantages: the surveys follow a repeated cross-section design, a research design particularly well suited to study social change; they are comparative surveys allowing you to compare data cross-nationally on a broad range of topics; the surveys follow rigorous methodological standards and, finally, data are available at no cost and can be downloaded from the web.

Replicating published research has the advantage that you are able to check your results against existing results. By trying to replicate previous research you learn where the original researcher has made tacit decisions not documented in the paper (e.g. coding of variables, treatment of missing values or exclusion of cases). Replicating social research also trains you to judge the validity of research results. In addition to these primarily pedagogical aspects replicating research is important from an epistemological point of view as well. Through replication of research by independent research groups biases in previous work can be discovered and findings can be validated (see Hendrick 1991, King 1995).

Assignment
Participants should choose a published paper and try to replicate the findings reported in it using the same data. The results to be replicated often will be given in a table containing the outcome of a multivariate model. Please document each step in your attempt to replicate the findings, report and explain the decisions you had to make during data preparation and data analysis. If you fail to replicate the results please indicate possible explanations. Your paper should not exceed 10,000 words; please add your documented syntax in the appendix.

Papers should be delivered in electronic form no later than January 20, 2017.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Dates: September 9 & 23; October 7, 21 & 28; November 11 & 25; December 9
09.09.16
Friday
10:15
13:30
Room B 318 from 10:15 and room C-108 (PC lab) from 12:00 in A5, 6

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Course Readings:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Everitt, B. S. (2004). Handbook of Statistical Analyses Using Stata (Third Edition). Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Rabe-Hesketh, S. & Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Snijders, T. A. B. & Bosker, R. J. (1999). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2013). Stata Multilevel Mixed-Effects. Reference Manual. Release 13. College Station, TX: Stata Press.

Assessment type:  Home assignments/presentation


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
14.09.16
07.12.16
Wednesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.16
08.12.16
Thursday
08:30
10:00
Room B 143 in A5,6 entrance B
Tutorial
08.09.16
08.12.16
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Recommended:

·         Contents of an introductory course on systematic reviews and meta-analysis (e.g., the ones offered by the course instructor at the University of Mannheim in the following semesters: FSS 2015, or HWS 2014, or FSS 2014).

·         M.Sc. or PhD thesis topic has been (at least roughly) defined already

·         Basic understanding of R ( http://www.r-project.org)


Course Content

This course will assist students to prepare, conduct, and to write-up a systematic review and/or meta-analysis for a M.Sc. or PhD thesis, encompassing the entire research synthesis process, namely:

·         Developing a problem statement and specifying research questions / hypotheses for a systematic review / meta-analysis;

·         Data collection (systematic retrieval and selection of studies);

·         Data extraction, coding, and unifying effect sizes;

·         Analysis and interpretation;

·         Reporting / writing a thesis encompassing a systematic review / meta-analysis.

Special emphasis will be on the analysis procedure (4) using R packages (esp. metafor: www.metafor-project.org ).

 

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.

Competences acquired

Advanced skills in conducting and writing-up meta analyses.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
07.09.16
30.11.16
Wednesday
15:15
18:30
Room B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The workshop covers (a) data analyses and (b) statistical power analyses for experimental and quasi-experimental designs from an applied perspective. We focus on theory-driven tests for differences in means and mean vectors in the context of the General Linear Model (GLM). Both uni- and multivariate designs will be addressed. Implementation in the statistics platform R will be discussed.

Among the topics are:
* One- and multi-factorial analysis of variance with fixed effects (ANOVA)
* Post-hoc comparisons
* Planned comparisons and "tailor-made hypothesis tests"
* Analysis of covariance (ANCOVA) and alternatives
* Random and mixed-effects ANOVAs
* Repeated-measures ANOVAs and MANOVAs
* Multivariate analysis of variance (MANOVA)
* Statistical power analyses for (M)ANOVAs, ANCOVAs, and planned comparisons using
   G*Power 3.1

Literature:
 
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation
analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
 
Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. (2009). Statistical power analysis using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
 
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007). G*Power 3: A flexible statistical power
analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39,
175-191.

 

Participants are required to prepare a short (i.e., less than 6 pages) research report describing (1) a
specific GLM research problem in the context of their Ph.D. project, (2) the appropriate statistical
analysis for this problem (using real or simulated data), and (3) the appropriate statistical power
analysis. Reports should be send to erdfelder@uni-mannheim.de within six weeks following the
workshop. Feedback will be provided in written form.

 


Competences acquired

After the workshop students should be able to select statistical procedures and set up design matrices in the context of the GLM that match the underlying substantive research questions as closely as possible. They should also be able to implement these techniques in R, using either specific procedures (i.e., non-default options) or appropriately defined coding variables for categorical independent variables in the framework of the multiple linear regression procedure.

The course includes a basic introduction to the R programming environment i.e. data handling, basic statistical analyses and the creation of graphics. Applications of R in the area of multiple regression and analysis of variance will be discussed. We will show how to test assumptions of the general linear model, dealing with multivariate outliers and influential data points, and introduce linear modeling techniques in R. We also present a short introduction to multivariate analysis, logistic regression and multilevel models. The software package R is free and available on all major platforms (www.r-project.org). We also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under: cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Moreover, participants will achieve competence in performing different types of statistical power analyses with G*Power 3.1. Goal attainment will be checked based on a brief research report (see Record of Achievement)


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Irregular dates please check web page in Portal 2
16.09.16
25.11.16
Friday
08:30
17:00
EO 259 and EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Transient populations, who's members are hard to reach due to the fact that they are highly mobile and can only be accessed at specific locations for a limited time period (e.g., refugees, tourists and travelers, nomads, people moving in and out of the labor market), become increasingly interesting for social scientist. This seminar explores the challenges of locating, recruiting, and surveying these populations. We will review different methods and innovative technologies to overcome barriers in sampling and studying transient populations, including respondent driven sampling, mobile web surveys, smartphone tracking, and making use of administrative records. We will also discuss issues of writing survey questions on sensitive topics and ethical considerations that arise from studying these populations.


Competences acquired

The Seminar in Research Methods deepens and extends knowledge of advanced methods of empirical social research in preparation for the subsequent seminar Research Project.

Learning Outcomes:
(1) Deeper knowledge of advanced econometric techniques and the latest
methodological developments in empirical social research.
(2) Knowledge of formal and stylistic requirements for academic texts
(3) Deeper understanding of causal explanations and the ability to evaluate them
(4) The ability to carry out theory-based empirical investigations into social
processes in one or more of the sociological research fields.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.16
07.12.16
Wednesday
15:30
17:00
tbd


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.10.16
Friday
10:15
15:15
EO 162 CIP Pool
08.10.16
Saturday
10:15
17:00
EO 162 CIP-Pool
21.10.16
Friday
10:15
15:15
EO 162 CIP-Pool
22.10.16
Friday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

Intensive longitudinal studies (e.g., quantitative diary methods, experience-sampling methods) receive increasing attention within the social sciences. Although increasingly popular in psychology, but they offer also many options for researchers in sociology and the political sciencies. In essence, Intensive longitudinal methods allow for „capturing life as it is lived” (Bolger, Davis, Rafaeli, 2003, p. 579) and thereby they overcome retrospective bias and other limitations of other survey methods. Importantly, multiple assessments allow for modeling changes in affect, attitude, and behavior over time courses.   In this course I will give an overview of the nature of intensive longitudinal methods, the research options they offer, as well as potential problems and challenges. I will discuss how to design empirical studies that use intensive longitudinal methods and will provide conceptual information about how to analyze the data (however, this course will not give an in-depth introduction in multi-level modeling.

Course Readings (a more comprehensive list will be available in the first meeting)

  • Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579-616.
  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York: Guilford Press.
  • Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
15:30
17:00
Room 308 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
05.09.16
05.12.16
Monday
12:00
13:30
B 143 in B6,23-25 entrance B

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
06.09.16
06.12.16
Tuesday
10:15
11:45
B 317 in A5,6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
10:15
11:45
B 317 in A5,6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Course Content

This is a graduate seminar course on the relationship between political institutions and the economy. This course assumes familiarity with the contents of international relations, comparative politics, and some economics. The twin goals of the course are to (a) introduce students to contemporary scholarly research on political economy broadly defined and (b) help students form original ideas and promising research projects in the substantive area of political economy.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
05.09.16
28.11.16
Monday
15:30
18:45
L13, 16, room 512

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 10

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
12:00
13:30
A 103 in B6, 23-25 entrance A


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

Tutorial

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

 


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.16
05.12.16
Monday
10:15
11:45
B 317 in A5,6 entrance B
Tutorial
07.09.16
07.12.16
Wednesdays
17:15
18:45
B 317 in A5,6 entrance B

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.

The course will take place on 09 and 23 September 2016 in L9, 7, room 308 starting at 1.30pm

The exam is set for 29 September 2016 starting at 1.45pmin room O135, 'Saal der starken Marken', Schloss Ostflügel

Course schedule

 


Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

The exam is scheduled for 15 December 2016 from 9am to 11am in room A203 in B6, 23-25, entrance A, 2nd floor

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
07.09.16
06.12.16
Wednesday
08:30
10:00
308 in L9, 7

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
06.09.16
06.12.16
Tuesday
12:00
13:30
B143 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.10.16
07.10.16
Wednesday to Friday
10:30
17:30
O 126, Schloss Ostflügel


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

 

Recent and ongoing psychological and neuropsychological research projects are discussed, including possible research plans, frameworks for data analysis, and interpretation of results.

Literature: References will be given during the course.

Course material will be provided in ILIAS.

 


Competences acquired

Improvement in research skills and communication of research results.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.09.16
05.12.16
Monday
15:30
17:00
EO 259

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The amount of digital data generated as a by-product in society is growing fast, e.g., data from satellites, sensors, transactions, administrative processes, social media and smartphones. This type of data is characterized by high volume, high velocity, high variety and is often called big data. The hope is to gain insights from this data for different areas such as e.g., health and crime prevention, planning of infrastructures, and business decisions. In particular on the economic statistics side, this interest in growing rapidly. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for agencies that in the past relied primarily on survey research. However, data quality frameworks well established in statistics production still hold. Thus the goal of this lecture is to equip the next generation of social and economic scientists as well as survey researchers with the right tools to face these data. The lecture uses specially curated data sets and a working example that runs through the entire course. The lecture is paired with a hands-on lab session in which students apply all learned techniques through a worked example relevant to core work of the Federal Statistical Agencies.


Competences acquired

Learn how to think about data analysis to solve social problems using and combining large quantities of heterogeneous data from a variety of different sources. Learn how to evaluate which data are appropriate to a given research question and statistical need. Learn the different data quality frameworks and learn how to apply them. Learn the basic computational skills required for data analytics (for text-mining, large-scale data integration and visualization), typically not taught in social science, economics, statistics or survey courses. Learn how to apply statistical and data quality frameworks to big data problems. Identify new approaches to creating and displaying information for Federal Agencies.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
10:15
11:45
B 6, 23-25 entrance A, room A301
27.10.16
Thursday
12:00
18:45
tbd
28.10.16
Friday
10:15
18:45
tbd
07.11.16
Monday
10:15
18:45
tbd


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

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

Literature:

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

 Assessment type: written exam


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.16
06.12.16
Tuesday
13:45
15:15
A5,6 room B 317
Tutorial
06.09.16
06.12.16
Tuesday
15:30
17:00
A5,6 room C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - irregular times & dates please check Portal 2 web page for all further details
09.09.16
17.09.15
Wednesday, Friday and Saturday
08:30
18:45
Rooms A 102 and A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Description
In addition to a thorough understanding of the substantive field you are studying you need firm methodological and statistical knowledge in order to successfully conduct quantitative social research. This seminar will give you the opportunity to apply and expand your knowledge of social research by replicating published research findings.

The research that we are going to replicate was conducted with data from publicly available survey data like the European Social Survey (ESS), the International Social Survey Programme (ISSP) or the European Values Study. Data from surveys like these have several advantages: the surveys follow a repeated cross-section design, a research design particularly well suited to study social change; they are comparative surveys allowing you to compare data cross-nationally on a broad range of topics; the surveys follow rigorous methodological standards and, finally, data are available at no cost and can be downloaded from the web.

Replicating published research has the advantage that you are able to check your results against existing results. By trying to replicate previous research you learn where the original researcher has made tacit decisions not documented in the paper (e.g. coding of variables, treatment of missing values or exclusion of cases). Replicating social research also trains you to judge the validity of research results. In addition to these primarily pedagogical aspects replicating research is important from an epistemological point of view as well. Through replication of research by independent research groups biases in previous work can be discovered and findings can be validated (see Hendrick 1991, King 1995).

Assignment
Participants should choose a published paper and try to replicate the findings reported in it using the same data. The results to be replicated often will be given in a table containing the outcome of a multivariate model. Please document each step in your attempt to replicate the findings, report and explain the decisions you had to make during data preparation and data analysis. If you fail to replicate the results please indicate possible explanations. Your paper should not exceed 10,000 words; please add your documented syntax in the appendix.

Papers should be delivered in electronic form no later than January 20, 2017.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Dates: September 9 & 23; October 7, 21 & 28; November 11 & 25; December 9
09.09.16
Friday
10:15
13:30
Room B 318 from 10:15 and room C-108 (PC lab) from 12:00 in A5, 6

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Course Readings:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Everitt, B. S. (2004). Handbook of Statistical Analyses Using Stata (Third Edition). Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Rabe-Hesketh, S. & Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Snijders, T. A. B. & Bosker, R. J. (1999). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2013). Stata Multilevel Mixed-Effects. Reference Manual. Release 13. College Station, TX: Stata Press.

Assessment type:  Home assignments/presentation


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
14.09.16
07.12.16
Wednesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.16
08.12.16
Thursday
08:30
10:00
Room B 143 in A5,6 entrance B
Tutorial
08.09.16
08.12.16
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Recommended:

·         Contents of an introductory course on systematic reviews and meta-analysis (e.g., the ones offered by the course instructor at the University of Mannheim in the following semesters: FSS 2015, or HWS 2014, or FSS 2014).

·         M.Sc. or PhD thesis topic has been (at least roughly) defined already

·         Basic understanding of R ( http://www.r-project.org)


Course Content

This course will assist students to prepare, conduct, and to write-up a systematic review and/or meta-analysis for a M.Sc. or PhD thesis, encompassing the entire research synthesis process, namely:

·         Developing a problem statement and specifying research questions / hypotheses for a systematic review / meta-analysis;

·         Data collection (systematic retrieval and selection of studies);

·         Data extraction, coding, and unifying effect sizes;

·         Analysis and interpretation;

·         Reporting / writing a thesis encompassing a systematic review / meta-analysis.

Special emphasis will be on the analysis procedure (4) using R packages (esp. metafor: www.metafor-project.org ).

 

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.

Competences acquired

Advanced skills in conducting and writing-up meta analyses.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
07.09.16
30.11.16
Wednesday
15:15
18:30
Room B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The workshop covers (a) data analyses and (b) statistical power analyses for experimental and quasi-experimental designs from an applied perspective. We focus on theory-driven tests for differences in means and mean vectors in the context of the General Linear Model (GLM). Both uni- and multivariate designs will be addressed. Implementation in the statistics platform R will be discussed.

Among the topics are:
* One- and multi-factorial analysis of variance with fixed effects (ANOVA)
* Post-hoc comparisons
* Planned comparisons and "tailor-made hypothesis tests"
* Analysis of covariance (ANCOVA) and alternatives
* Random and mixed-effects ANOVAs
* Repeated-measures ANOVAs and MANOVAs
* Multivariate analysis of variance (MANOVA)
* Statistical power analyses for (M)ANOVAs, ANCOVAs, and planned comparisons using
   G*Power 3.1

Literature:
 
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation
analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
 
Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. (2009). Statistical power analysis using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
 
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007). G*Power 3: A flexible statistical power
analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39,
175-191.

 

Participants are required to prepare a short (i.e., less than 6 pages) research report describing (1) a
specific GLM research problem in the context of their Ph.D. project, (2) the appropriate statistical
analysis for this problem (using real or simulated data), and (3) the appropriate statistical power
analysis. Reports should be send to erdfelder@uni-mannheim.de within six weeks following the
workshop. Feedback will be provided in written form.

 


Competences acquired

After the workshop students should be able to select statistical procedures and set up design matrices in the context of the GLM that match the underlying substantive research questions as closely as possible. They should also be able to implement these techniques in R, using either specific procedures (i.e., non-default options) or appropriately defined coding variables for categorical independent variables in the framework of the multiple linear regression procedure.

The course includes a basic introduction to the R programming environment i.e. data handling, basic statistical analyses and the creation of graphics. Applications of R in the area of multiple regression and analysis of variance will be discussed. We will show how to test assumptions of the general linear model, dealing with multivariate outliers and influential data points, and introduce linear modeling techniques in R. We also present a short introduction to multivariate analysis, logistic regression and multilevel models. The software package R is free and available on all major platforms (www.r-project.org). We also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under: cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Moreover, participants will achieve competence in performing different types of statistical power analyses with G*Power 3.1. Goal attainment will be checked based on a brief research report (see Record of Achievement)


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Irregular dates please check web page in Portal 2
16.09.16
25.11.16
Friday
08:30
17:00
EO 259 and EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Transient populations, who's members are hard to reach due to the fact that they are highly mobile and can only be accessed at specific locations for a limited time period (e.g., refugees, tourists and travelers, nomads, people moving in and out of the labor market), become increasingly interesting for social scientist. This seminar explores the challenges of locating, recruiting, and surveying these populations. We will review different methods and innovative technologies to overcome barriers in sampling and studying transient populations, including respondent driven sampling, mobile web surveys, smartphone tracking, and making use of administrative records. We will also discuss issues of writing survey questions on sensitive topics and ethical considerations that arise from studying these populations.


Competences acquired

The Seminar in Research Methods deepens and extends knowledge of advanced methods of empirical social research in preparation for the subsequent seminar Research Project.

Learning Outcomes:
(1) Deeper knowledge of advanced econometric techniques and the latest
methodological developments in empirical social research.
(2) Knowledge of formal and stylistic requirements for academic texts
(3) Deeper understanding of causal explanations and the ability to evaluate them
(4) The ability to carry out theory-based empirical investigations into social
processes in one or more of the sociological research fields.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.16
07.12.16
Wednesday
15:30
17:00
tbd


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.10.16
Friday
10:15
15:15
EO 162 CIP Pool
08.10.16
Saturday
10:15
17:00
EO 162 CIP-Pool
21.10.16
Friday
10:15
15:15
EO 162 CIP-Pool
22.10.16
Friday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

Intensive longitudinal studies (e.g., quantitative diary methods, experience-sampling methods) receive increasing attention within the social sciences. Although increasingly popular in psychology, but they offer also many options for researchers in sociology and the political sciencies. In essence, Intensive longitudinal methods allow for „capturing life as it is lived” (Bolger, Davis, Rafaeli, 2003, p. 579) and thereby they overcome retrospective bias and other limitations of other survey methods. Importantly, multiple assessments allow for modeling changes in affect, attitude, and behavior over time courses.   In this course I will give an overview of the nature of intensive longitudinal methods, the research options they offer, as well as potential problems and challenges. I will discuss how to design empirical studies that use intensive longitudinal methods and will provide conceptual information about how to analyze the data (however, this course will not give an in-depth introduction in multi-level modeling.

Course Readings (a more comprehensive list will be available in the first meeting)

  • Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579-616.
  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York: Guilford Press.
  • Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
15:30
17:00
Room 308 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.16
09.12.16
Fridays
12:00
13:30
L 13, 17, room 014

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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
06.09.16
06.12.16
Tuesdays
10:15
11:45
324, Parkring 47

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Course Content

The lecture will present cutting edge research conducted in Cognitive Psychology at the University of Mannheim.
After an introductory overview of Cognitive Psychology and its advanced methods by A. Bröder, various researchers will present their current work. The following reseacrher are planned as lecturers (changes possible): Dr. Nina Arnold, Dr. Martin Brandt, Prof. A. Bröder, Prof. E. Erdfelder, Dr. Michael Gräf, Dr. Meike Kroneisen, Dr. Lena Naderevic, Prof. Rüdiger Pohl und Dr. Monika Undorf.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.16
08.12.16
Thursday
15:30
17:00
EO 145

Lecturer(s)


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

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


Course Content

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

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

Literature

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.16
08.12.16
Thursday
17:15
18:45
EO 242

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).




Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.09.16
09.12.16
Fridays
13:45
15:15
L 13, 17 in room 014 except 21 October in L13, 17 in room 009


Course Type: elective course

Course Number: PSY

Credits: 4

Prerequisites

Admittance to the CDSS Psychology Program or any of the other GESS Doctoral Programs


Course Content

The “Perspectives on Psychology” lecture series covers a coherent topic of psychological research addressed from the perspectives of different sub-disciplines within psychology. Possible topics include phenomena and mechanisms of relevance in different parts of psychology, for example affect, motivation, decision making, health-relevant behavior, personality and individual differences, etc. In the current semester, all lectures address research on motivation and affect. Moreover, the lectures may cover methodological advances instrumental for any sub-field of psychology, e.g. recent research synthesis methods and systematic replications. The lectures will be presented from different members of the CDSS Psychology Faculty, primarily based on their own research. Each lecture will include a discussion block and the opportunity to discuss cross-connections between lectures.

The lecture series ends with an essay (max. 5 pages) for which the submission deadline is 31 December 2016. Full details will be e-mailed to course participants.

Program schedule

 

 


Competences acquired

Improvement of the ability to elaborate a psychologically relevant topic from different sub-disciplinary and theoretical perspectives. Increase in knowledge of psychological theories and methods.



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
Colloquium
07.09.16
07.12.16
Wednesday
19:00
20:30
tbd


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
Colloquium
Colloquium
06.09.16
06.12.16
Tuesday
19:00
20:30
tbd

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.

The course will take place on 09 and 23 September 2016 in L9, 7, room 308 starting at 1.30pm

The exam is set for 29 September 2016 starting at 1.45pmin room O135, 'Saal der starken Marken', Schloss Ostflügel

Course schedule

 


Lecturer(s)


Course Type: core course

Course Number: BAS

Credits: 2

Course Content

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

The exam is scheduled for 15 December 2016 from 9am to 11am in room A203 in B6, 23-25, entrance A, 2nd floor

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
07.09.16
06.12.16
Wednesday
08:30
10:00
308 in L9, 7

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
06.09.16
06.12.16
Tuesday
12:00
13:30
B143 in A 5, 6 Bauteil B


Course Type: core course

Course Number: MET

Credits: 6

Course Content

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

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


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
05.10.16
07.10.16
Wednesday to Friday
10:30
17:30
O 126, Schloss Ostflügel


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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.16
07.12.16
Wednesday
12:00
13:30
B 243 in A 5, 6 entrance B


Course Type: core course

Course Number: RES

Credits: 2

Course Content

Please refer to the MZES webpages for dates and times.


Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

The amount of digital data generated as a by-product in society is growing fast, e.g., data from satellites, sensors, transactions, administrative processes, social media and smartphones. This type of data is characterized by high volume, high velocity, high variety and is often called big data. The hope is to gain insights from this data for different areas such as e.g., health and crime prevention, planning of infrastructures, and business decisions. In particular on the economic statistics side, this interest in growing rapidly. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for agencies that in the past relied primarily on survey research. However, data quality frameworks well established in statistics production still hold. Thus the goal of this lecture is to equip the next generation of social and economic scientists as well as survey researchers with the right tools to face these data. The lecture uses specially curated data sets and a working example that runs through the entire course. The lecture is paired with a hands-on lab session in which students apply all learned techniques through a worked example relevant to core work of the Federal Statistical Agencies.


Competences acquired

Learn how to think about data analysis to solve social problems using and combining large quantities of heterogeneous data from a variety of different sources. Learn how to evaluate which data are appropriate to a given research question and statistical need. Learn the different data quality frameworks and learn how to apply them. Learn the basic computational skills required for data analytics (for text-mining, large-scale data integration and visualization), typically not taught in social science, economics, statistics or survey courses. Learn how to apply statistical and data quality frameworks to big data problems. Identify new approaches to creating and displaying information for Federal Agencies.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
10:15
11:45
B 6, 23-25 entrance A, room A301
27.10.16
Thursday
12:00
18:45
tbd
28.10.16
Friday
10:15
18:45
tbd
07.11.16
Monday
10:15
18:45
tbd


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

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

Literature:

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

 Assessment type: written exam


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.16
06.12.16
Tuesday
13:45
15:15
A5,6 room B 317
Tutorial
06.09.16
06.12.16
Tuesday
15:30
17:00
A5,6 room C-108 (PC Lab)

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Block course - irregular times & dates please check Portal 2 web page for all further details
09.09.16
17.09.15
Wednesday, Friday and Saturday
08:30
18:45
Rooms A 102 and A 103 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Description
In addition to a thorough understanding of the substantive field you are studying you need firm methodological and statistical knowledge in order to successfully conduct quantitative social research. This seminar will give you the opportunity to apply and expand your knowledge of social research by replicating published research findings.

The research that we are going to replicate was conducted with data from publicly available survey data like the European Social Survey (ESS), the International Social Survey Programme (ISSP) or the European Values Study. Data from surveys like these have several advantages: the surveys follow a repeated cross-section design, a research design particularly well suited to study social change; they are comparative surveys allowing you to compare data cross-nationally on a broad range of topics; the surveys follow rigorous methodological standards and, finally, data are available at no cost and can be downloaded from the web.

Replicating published research has the advantage that you are able to check your results against existing results. By trying to replicate previous research you learn where the original researcher has made tacit decisions not documented in the paper (e.g. coding of variables, treatment of missing values or exclusion of cases). Replicating social research also trains you to judge the validity of research results. In addition to these primarily pedagogical aspects replicating research is important from an epistemological point of view as well. Through replication of research by independent research groups biases in previous work can be discovered and findings can be validated (see Hendrick 1991, King 1995).

Assignment
Participants should choose a published paper and try to replicate the findings reported in it using the same data. The results to be replicated often will be given in a table containing the outcome of a multivariate model. Please document each step in your attempt to replicate the findings, report and explain the decisions you had to make during data preparation and data analysis. If you fail to replicate the results please indicate possible explanations. Your paper should not exceed 10,000 words; please add your documented syntax in the appendix.

Papers should be delivered in electronic form no later than January 20, 2017.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
Dates: September 9 & 23; October 7, 21 & 28; November 11 & 25; December 9
09.09.16
Friday
10:15
13:30
Room B 318 from 10:15 and room C-108 (PC lab) from 12:00 in A5, 6

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Knowledge of regression analysis


Course Content

Multilevel modeling is used when observations on the individual level are nested in units of one or more higher levels (e.g. students in classes in schools). The course will cover the logic of multilevel modeling, its statistical background, and implementation with Stata. Applications will come from international comparative research treating countries as the higher level units. Data from the International Social Survey Program and the PIONEUR project (on intra-European migration) serve as examples. However, students are also encouraged to bring their own data.

Course Readings:

  • Goldstein, H. (2010). Multilevel Statistical Models (Fourth Edition). London: Arnold.
  • Hox, J. (2010). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Erlbaum.
  • Rabe-Hesketh, S. & Everitt, B. S. (2004). Handbook of Statistical Analyses Using Stata (Third Edition). Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Rabe-Hesketh, S. & Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd Edition. College Station, TX: Stata Press.
  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks: Sage.
  • Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, FL: Chapman & Hall/ CRC Press.
  • Snijders, T. A. B. & Bosker, R. J. (1999). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
  • StataCorp. (2013). Stata Multilevel Mixed-Effects. Reference Manual. Release 13. College Station, TX: Stata Press.

Assessment type:  Home assignments/presentation


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
14.09.16
07.12.16
Wednesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 6+2

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.16
08.12.16
Thursday
08:30
10:00
Room B 143 in A5,6 entrance B
Tutorial
08.09.16
08.12.16
Thursday
10:15
11:45
Room C -108 (PC Lab) in A5,6, entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Recommended:

·         Contents of an introductory course on systematic reviews and meta-analysis (e.g., the ones offered by the course instructor at the University of Mannheim in the following semesters: FSS 2015, or HWS 2014, or FSS 2014).

·         M.Sc. or PhD thesis topic has been (at least roughly) defined already

·         Basic understanding of R ( http://www.r-project.org)


Course Content

This course will assist students to prepare, conduct, and to write-up a systematic review and/or meta-analysis for a M.Sc. or PhD thesis, encompassing the entire research synthesis process, namely:

·         Developing a problem statement and specifying research questions / hypotheses for a systematic review / meta-analysis;

·         Data collection (systematic retrieval and selection of studies);

·         Data extraction, coding, and unifying effect sizes;

·         Analysis and interpretation;

·         Reporting / writing a thesis encompassing a systematic review / meta-analysis.

Special emphasis will be on the analysis procedure (4) using R packages (esp. metafor: www.metafor-project.org ).

 

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.

Competences acquired

Advanced skills in conducting and writing-up meta analyses.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
07.09.16
30.11.16
Wednesday
15:15
18:30
Room B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET

Credits: 4

Course Content

The workshop covers (a) data analyses and (b) statistical power analyses for experimental and quasi-experimental designs from an applied perspective. We focus on theory-driven tests for differences in means and mean vectors in the context of the General Linear Model (GLM). Both uni- and multivariate designs will be addressed. Implementation in the statistics platform R will be discussed.

Among the topics are:
* One- and multi-factorial analysis of variance with fixed effects (ANOVA)
* Post-hoc comparisons
* Planned comparisons and "tailor-made hypothesis tests"
* Analysis of covariance (ANCOVA) and alternatives
* Random and mixed-effects ANOVAs
* Repeated-measures ANOVAs and MANOVAs
* Multivariate analysis of variance (MANOVA)
* Statistical power analyses for (M)ANOVAs, ANCOVAs, and planned comparisons using
   G*Power 3.1

Literature:
 
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation
analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
 
Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. (2009). Statistical power analysis using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
 
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007). G*Power 3: A flexible statistical power
analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39,
175-191.

 

Participants are required to prepare a short (i.e., less than 6 pages) research report describing (1) a
specific GLM research problem in the context of their Ph.D. project, (2) the appropriate statistical
analysis for this problem (using real or simulated data), and (3) the appropriate statistical power
analysis. Reports should be send to erdfelder@uni-mannheim.de within six weeks following the
workshop. Feedback will be provided in written form.

 


Competences acquired

After the workshop students should be able to select statistical procedures and set up design matrices in the context of the GLM that match the underlying substantive research questions as closely as possible. They should also be able to implement these techniques in R, using either specific procedures (i.e., non-default options) or appropriately defined coding variables for categorical independent variables in the framework of the multiple linear regression procedure.

The course includes a basic introduction to the R programming environment i.e. data handling, basic statistical analyses and the creation of graphics. Applications of R in the area of multiple regression and analysis of variance will be discussed. We will show how to test assumptions of the general linear model, dealing with multivariate outliers and influential data points, and introduce linear modeling techniques in R. We also present a short introduction to multivariate analysis, logistic regression and multilevel models. The software package R is free and available on all major platforms (www.r-project.org). We also recommend the free and platform independent Software RStudio as a comfortable IDE for R (www.rstudio.com). A basic introduction to R can be found under: cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Moreover, participants will achieve competence in performing different types of statistical power analyses with G*Power 3.1. Goal attainment will be checked based on a brief research report (see Record of Achievement)


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Irregular dates please check web page in Portal 2
16.09.16
25.11.16
Friday
08:30
17:00
EO 259 and EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Course Content

Transient populations, who's members are hard to reach due to the fact that they are highly mobile and can only be accessed at specific locations for a limited time period (e.g., refugees, tourists and travelers, nomads, people moving in and out of the labor market), become increasingly interesting for social scientist. This seminar explores the challenges of locating, recruiting, and surveying these populations. We will review different methods and innovative technologies to overcome barriers in sampling and studying transient populations, including respondent driven sampling, mobile web surveys, smartphone tracking, and making use of administrative records. We will also discuss issues of writing survey questions on sensitive topics and ethical considerations that arise from studying these populations.


Competences acquired

The Seminar in Research Methods deepens and extends knowledge of advanced methods of empirical social research in preparation for the subsequent seminar Research Project.

Learning Outcomes:
(1) Deeper knowledge of advanced econometric techniques and the latest
methodological developments in empirical social research.
(2) Knowledge of formal and stylistic requirements for academic texts
(3) Deeper understanding of causal explanations and the ability to evaluate them
(4) The ability to carry out theory-based empirical investigations into social
processes in one or more of the sociological research fields.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.16
07.12.16
Wednesday
15:30
17:00
tbd


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

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

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

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

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.10.16
Friday
10:15
15:15
EO 162 CIP Pool
08.10.16
Saturday
10:15
17:00
EO 162 CIP-Pool
21.10.16
Friday
10:15
15:15
EO 162 CIP-Pool
22.10.16
Friday
10:15
17:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

Intensive longitudinal studies (e.g., quantitative diary methods, experience-sampling methods) receive increasing attention within the social sciences. Although increasingly popular in psychology, but they offer also many options for researchers in sociology and the political sciencies. In essence, Intensive longitudinal methods allow for „capturing life as it is lived” (Bolger, Davis, Rafaeli, 2003, p. 579) and thereby they overcome retrospective bias and other limitations of other survey methods. Importantly, multiple assessments allow for modeling changes in affect, attitude, and behavior over time courses.   In this course I will give an overview of the nature of intensive longitudinal methods, the research options they offer, as well as potential problems and challenges. I will discuss how to design empirical studies that use intensive longitudinal methods and will provide conceptual information about how to analyze the data (however, this course will not give an in-depth introduction in multi-level modeling.

Course Readings (a more comprehensive list will be available in the first meeting)

  • Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579-616.
  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York: Guilford Press.
  • Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guildford Press.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
08.09.16
08.12.16
Thursday
15:30
17:00
Room 308 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
07.09.16
07.12.16
Wednesday
12:00
13:30
217, Parkring 47

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
09.09.16
09.12.16
Friday
10:15
11:45
B 317 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Prerequisites

Knowledge of Stata is helpful but not required.


Course Content

Since World War II, the countries of Western Europe have held relatively stable borders and grew to be some of the richest in the world. Despite sustained geographic areas and economic growth, the populations of these societies have been changing rapidly for many decades. The populations are aging. Rates of child birth have declined and the number of retirees has boomed. Since the 1950s, more and more immigrants have arrived in Western European countries to fill the labor shortage generated by an aging population and to provide competitive wage labor in a globalized labor market. Moreover, immigrants arrive seeking better fortunes than they left behind in their home countries. Some also flee from violence, oppression and war.
 
Today Western Europe has more foreign-born immigrants than at any point since the world wars. Many parts of Western Europe have immigrants that makeup over 20% of their populations – counting those who immigrated or who have immigrant parents. These immigrants are often, but not always, disadvantaged compared to natives in terms of social capital. This means that they lack the same level of integration into society and culture, have less access to education, and often get jobs that are lower status than natives. In addition, they sometimes face hostile response to their presence, especially for those who come from Muslim countries. The past two decades saw waves of success for anti-immigrant populist movements and political parties. Even in Germany where populism has been off the map since WWII, the Alternativ für Deutschland party made considerable gains in the most recent election. The once bright future for immigrants through guest worker programs, family reunification policies and ‘open arms’ for asylum (wir schaffen das!) has become cloudier.
 
This course will look at the situation of immigrants using survey data from the Programme for the International Assessment of Adult Competencies (PIAAC) conducted around 2011. We will investigate the how immigrants fare in the labor market, what their family lives are like, and how much trust and engagement they have in society; and how this compares with natives. As Western Europe is the site of the most intensive immigration, we will focus on these countries. The course is simultaneously a literature review of recent research on immigration in Western Europe and a practical research course. We will develop hypotheses based on the literature and investigate them through PIAAC data analysis using Stata software. Students are expected to individually or in groups design a research plan using PIAAC data and to write a paper describing the statistical results they find.
 
Presentations, discussion and the final paper in this course will be in English.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
06.09.16
06.12.16
Tuesday
08:30
10:00
B 318 in A 5, 6 entrance B

Register

Social Sciences Fall 2016

Dissertation Tutorial: Sociology (Gautschi/Hillmann/Kreuter)
Dissertation Tutorial: Sociology (Kogan/Kalter/Ebbinghaus)
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
Big Data in the Social Sciences
MET
Cross Sectional Data Analysis (Lecture + Tutorial)
MET
Data and Measurement: Questionnaire Design and Implementation (Theory + Lab Course)
MET
Learning social research through replication
MET
Multilevel Modeling
MET
Multivariate Analyses (Theory + Lab Course)
MET
Research Seminar on Systematic Reviews and Meta-Analysis
MET
Study design, statistical power, and linear modeling (using R)
MET
Using modern data collection methods to study transient populations
MET/PSY
Creating experiments with OpenSesame
MET/PSY
Intensive Longitudinal Methods - deferred to spring 2017
SOC
Economy & the Welfare State: Expert Control and Public Problems
SOC
Migration & Integration: Education and Migration
SOC
Migration & Integration: Immigrants and Social Capital in Europe: Lessons from Survey Data
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
POL
Advanced Topics in Comparative Politics: Elections in Comparative Perspective
POL
Advanced Topics in Comparative Politics: Political Behavior in Context
POL
Advanced Topics in International Politics
POL
Advanced Topics in International Politics
POL
Advanced Topics in International Politics: Civil War and Post-Conflict Research
POL/MET
Game Theory (Theory + Tutorial)
RES
AC3/BC3 Colloquia I
RES
CDSS Workshop: Research in Psychology
MET/PSY
Introduction to Functional Magnetic Resonance Imaging
PSY
Advanced Social and Economic Cognition
PSY
Advanced topics in Cognitive Psychology
PSY
Advanced Topics in Work and Organizational Psychology
PSY
Introduction to Social Cognitive Neuroscience I
PSY
Perspectives on Psychology - Affect and Motivation