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

Spring 2017


Course Type: core course

Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Colloquium
Gautschi/Hillmann
08.02.17
31.05.17
Wednesday
17:15
18:45
tbc
Keusch/Kreuter
13.02.17
29.05.17
Monday
12:00
13:30
tbc

Lecturer(s)


Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

CSSR, Literature Review

You should be prepared to address the following questions: What makes a particular research question an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?


Course Content

The goal of this course is to provide support and crucial feedback on writing students' dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions (building on the Literature Review), the development of a theoretical framework, the specification of the methodology and planned empirical analysis.

Nota bene: Further meeting dates and locations will be determined during the first session.

Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.03.16
Monday
09:00
13:30
Room 307 in D 7, 27
11.03.16
Friday
09:00
13:30
Room 307 in D 7, 27
25.04.16
Monday
09:00
13:30
Room 307 in D 7, 27
06.05.16
Friday
09:00
13:30
Room 307 in D 7, 27
1st meeting
15.02.17
Wednesday
10:15
11:45
307 in D7, 27

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, TBCI, EAW, Literature Review, Dissertation Proposal


Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
15.02.17
31.05.17
Wednesday
12:00
13:30
307 in D7, 27

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review


Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
16.02.17
01.06.17
Thursday
12:00
13:30
B 143 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: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
03.08.17
25.08.17
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Will require attendance of the Bayesian Statistics for Social Scientist I course taught by Prof. Traunmüller or knowledge of the topics covered.


Course Content

Bayesian statistics has long been overlooked in the quantitative methods training for social scientists.  Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class.  This is not surprising.  First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software.  Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful (and free) statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.

The orientation of this workshop is to introduce social scientists to advanced elements of Bayesian statistics and to show through discussion and practice, why the Bayesian perspective provides a powerful alternative to the frequentist perspective.  It is assumed that students of the workshop will have a background in basic Bayesian statistics, though the workshop will contain some review.  Some exposure to multilevel modeling and factor analysis is desirable.

Readings

  • Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14, 382–417. pdf
  • Kaplan, D. & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling. (pp 650-673), New York: Guilford Publications, Inc. pdf
  • Kaplan, D. & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (ed.), Oxford Handbook of Quantitative Methods. (pp 407-437) Oxford: Oxford University Press. pdf
  • Kaplan, D. & Park, S. (2013). Analyzing international large-scale assessment data within a Bayesian framework. In L. Rutkowski, M. Von Davier, and D. Rutkowski (eds.), A Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis. (pp 547-581). London: Chapman Hall/CRC Press. pdf
  • Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press. 
  • Kaplan, D. (2015). The future of quantitative inquiry in education: Challenges and opportunities. In M. J. Feuer, A. I. Berman, and R. C. Atkinson (eds.), The past as prologue: The National Academy ofEducation at 50. Members reflect. (pp. 109{115). Washington, DC. National Academy of Education. pdf
  • Kaplan, D. & Lee, C. (2015). Bayesian model averaging over directed acyclic graphs with implications for the predictive performance of structural equation models. Structural Equation Modeling.doi:10.1080/10705511.2015.1092088 pdf
  • van de Schoot, R., Kaplan, D., Denissen, J., Asndorpf, J. B., Neyer, F. J. & van Aken, M. A. G. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. DOI: 10.1111/cdev.12169 pdf

 

Workshop Outline

Day 1

Morning:

1.  Major differences between the Bayesian and frequentist paradigms of statistics.
2.  Bayes’ theorem; The likelihood, The nature of priors; The posterior distribution.
3.  Bayesian hypothesis testing; Contrasts with frequentist hypothesis testing.

Afternoon:

1.  Bayesian computation; MCMC; diagnostics
2.  Introduction to "rjags”.
3.  Exploration of distributions under different priors

Day 2

Morning:

1.   Bayesian model building.
1.   Bayesian model evaluation.
2.   Bayesian model averaging.
3.   Bayesian linear regression.

Afternoon:

1.  Student analyses – Bayesian regression analysis

Day 3

Morning:

1.  Advanced topics; HLM; factor analysis (time permitting)
2.   Final philosophical issues

 Afternoon:

1.  Final student analyses

 

End of course assignment to be completed within 4 weeks of the workshop.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.05.17
24.05.17
09:00
16:00
A 301 in B6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
31.03.17
07.04.17
Friday
09:00
16:00
EO 154
28.04.17
05.05.17
Friday
09:00
16:00
509 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

Lecture "Longitudinal Data Analysis"

 

The lecture gives a broad overview over methods of longitudinal data analysis. The focus of the course lies on methods for panel and event history data analysis and their application in the social sciences. Attendance of the complementary course "Data Sources in Social Sciences" is highly recommended as the course illustrates the practical application of the methods in Stata and deepens understanding of the theoretical content of the lecture.

Lab Course "Data Sources in Social Sciences"

Using Stata we practice methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) with examples mainly from the German SOEP. Attendance of the complementary lecture " Longitudinal Data Analysis " is highly recommended as firm knowledge of the lecture content is presumed. In addition, a further prerequisite for participation is firm knowledge of data preparation and estimation of simple linear regressions in Stata.

Suggested Readings:

  • Blossfeld, H.-P., K. Golsch, and G. Rohwer (2009): Event History Analysis with Stata. New York/ London: Psychology Press. [But avoid the philosophical part of the book on causality in chapter 1]
  • Andreß, H.J.,K. Golsch, and A. Schmidt (2013) Applied Panel Data Analysis for Economic and Social Surveys. Springer.

6 ECTS will be awarded for successful completion of an exam and an additional 2 ECTS can be awarded for participation in the lab course and submission of two practical assignments.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
15.02.17
31.05.17
Wednesday
10:15
11:45
B 244 in A 5, 6 entrance B
Tutorial
Lab Course
16.02.17
01.06.17
Thursday
15:30
17:00
C-108 (PC/Methods Lab) in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Prerequisites: Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

cran.r-project.org/manuals.html
www.rstudio.com/online-learning/
www.statmethods.net


Course Content

This course provides an introduction to supervised statistical learning techniques such as regression trees, random forests and boosting and discusses their potential applications in the social sciences. These methods focus on predicting an outcome Y based on some data-driven function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. In connection with the empirical applications, this course will also discuss some aspects of data gathering, curation and data quality within the big data framework. The discussed methods will be implemented using the statistical programming language R.


Competences acquired

At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods. Furthermore, students will gain insights into new types of data, their advantages and their potential drawbacks.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesday
10:15
11:45
A305 in B 6, 23-25 entrance A
28.02.17
Tuesday
13:45
15:15
A303 in B 6, 23-25 entrance A

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
14.02.17
23.05.17
Tuesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

The course includes theoretical sessions on formal model foundations as well as empirical demonstrations and practical exercises with Mplus. Students have to be familiar with the basics of structural equation modeling and multilevel analysis from introductory Master courses on multivariate statistics.


Course Content

The seminar introduces fundamentals and advanced topics in linear structural equation modeling and multilevel modeling, including

  • Principles of Structural Equation Modeling
  1. ­       Model equations
  2. ­       Parameter estimation and model testing
  3. ­       Model specification with Mplus
  4. ­       Model applications
  • Advanced Topics in Structural Equation Modeling
  1. ­       Modeling continuous and discrete variables
  2. ­       Multi-group designs and measurement invariance
  3. ­       Mixed structural equation models
  4. ­       Structural equation models for longitudinal data
  • Principles of Multilevel Modeling
  1. ­       Regression models for hierarchical data structures
  2. ­       Equivalence of multilevel and structural equation models
  3. ­       Model specification with Mplus
  4. ­       Model applications
  •  Advanced Topics in Multilevel Modeling
  1. ­       Generalized multilevel models
  2. ­       Latent growth curve analysis and dynamic predictors
  3. ­       Crossed multilevel models
  4. ­       Multilevel structural equation models

 

Sessions:

Friday, 24th March, 9:00-13:00

Friday, 7th April, 9:00-13:00

Friday, 5th May, 9:00-13:00

Friday, 19th May, 9:00-13:00

Friday, 2nd June, 9:00-13:00, 14:00-16:00

Friday, 9th June, 9:00-13:00, 14:00-16:00

 All in room EO 162 CIP-Pool


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - first session 24 March
24.03.17
Fridays
09:00
13:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Mathematics for Social Scientists


Course Content

The lectures aim at introducing students to stochastic models such as Markov chains and their applications. In a first part a minimum level of needed mathematical tools such as basic linear algebra will be introduced, followed by an introduction to Markov chains. Mathematical content will be addressed on a level accessible to students without specialized Math background. The final part of the lectures gives examples Markov chains and some applications. Overall, we intend to raise awareness of interdisciplinary research between Mathematics and Social Science without using an overkill of methodology.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.17
29.05.17
Monday
10:15
11:45
C 116 in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Prerequisites

  • 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 ( www.r-project.org )
  • A Beaker Lab notebook account (free):
    https://lab.beakernotebook.com


Course Content

This syllabus is updated occasionally, so please check back at least weekly to see if new information has been posted

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: http://www.metafor-project.org ).

Course topics:

  • The R package ´metafor´
    http://www.metafor-project.org
  • Interim report presentations by participants:  Data structure and sample data

  • Meta-Analysis with R Exercise

  • Final presentations by participants: Results

Literature:

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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
17.02.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
every three weeks
02.03.16
13.04.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
biweekly
27.04.16
11.05.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
01.06.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
irregular, please check for dates in Portal 2
15.02.17
24.05.17
Wednesday
15:30
18:45
B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

Course Readings:

  • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
  • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
  • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is necessary for the assignments which complement the lecture (6 ECTS).


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.17
31.05.17
Wednesday
08:30
10:00
B 143 in A 5, 6 entrance B
Tutorial
16.02.17
01.06.17
Thursday
08:30
10:00
B 317, A 5, 6 entrance B

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
16.02.17
01.06.17
Thursday
10:15
11:45
EO 256, Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:
* Generalized linear mixed models with lme4 (also known as hierarchical models)
* Simple structural equation models
* Basic set-up of Monte-Carlo simulations
* Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
16.02.17
30.03.17
Thursday
13:45
17:25
EO 162 CIP-Pool, Schloss Ehrenhof Ost
04.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
18.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
01.06.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: POL/SOC

Credits: 8

Prerequisites

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.


Course Content

Comparison is essential to research in political sociology. At the same time, it raises multiple issues of comparability and equivalence. What is ‘similar’, what is ‘different’? And how can we figure out whether measures are equivalent or not? In this seminar, we will address the conceptual and methodological issues in measurement in comparative political research. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesdays
12:00
13:30
B 318 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

  • Foundations of linear algebra and probability theory (high school level)
  • Computer skills that allow you to get familiar with complex applications fast

Course Content

The course presents methods for the computer assisted automatic analysis of digital documents as a basis for further quantitative content analyses used in social and cultural sciences.

In the beginning we will present some possible analyses computational linguistics can offer to social and cultural sciences using the software GATE. This is followed by a short programming course in the Python programming language introducing a more flexible way of pre-processing texts and also access to text data through web crawling and conversion of different file formats. More advanced methods on text classification and clustering are presented later on along with more tools that can be used. In the final part of the course participants will present their own project work to each other.

Passing the course is based on:

  • Implementation of a project
  • Final presentation
  • Report (~ 15 pages)

Topics by class (always from 2-5pm):

24.02.2017 – Introduction to the course; Presentation of methods and tools that can be used to enhance quantitative content analysis; Examples of works in computational social science; Discussion about initial ideas for the course projects.

28.02.2017 – Introduction to Python; Lecture and hands-on exercises.

07.03.2017 – Introduction to NLTK (natural language processing tools in Python); Lecture and hands-on exercises (guest teacher: Federico Nanni).

21.03.2017 – Crawling and downloading relevant data with Python; Lecture and hands-on exercises (guest teacher: Federico Nanni); preparation of data for machine learning experiments (Python).

05.05.2017 – Introduction to machine learning; classification and clustering; Lecture and step-by-step demonstration in Weka; discussions about the projects.

12.05.2017 – Machine learning hands-on exercises using Weka; final discussions about the projects.

09.06.2017 – Project presentations (course participants).

Final project report submissions deadline: 30.06.2017


Competences acquired

Basic programming skills in Python, familiarity with natural language processing tools, ability to use machine learning (classification and clustering algorithms)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
29.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
Intro session, further dates in the list above.
24.02.17
Friday
14:00
17:00
C-109 in A5, 6 entrance C
Presentations
09.06.17
Friday
14:00
16:00
C-109 in A5, 6 entrance C


Course Type: elective course

Course Number: RES

Credits: 2

Prerequisites

Advanced PhD-Students (3rd year) with first experience in writing journal articles (at least one submitted manuscript). PostDocs are also welcome. Max. 20 participants


Course Content

Reviews provided by experienced researchers in the field are a key instrument in order to assure high quality in research. Writing reviews, however, does not only require expertise in the field of research. It requires also knowledge on the review process itself and skills in writing clear reviews in order to make a convincing point to the editor and to the author. This course will provide you with the necessary knowledge and skills in order to write better reviews for academic journals. Such skills and knowledge will hopefully not only be helpful for writing better reviews. I am convinced that a profound knowledge about reviews and the review process will also be helpful for one’s own writing of research papers. 

Topics of the workshop

-       Functions of peer – review

-       Forms of peer – review

-       Process of reviewing

-       General rules for writing reviews

-       Structure of reviews

-       Do’s and don’ts in reviewing

-       Specific problems

In the working part, you will work on your own review (which you are asked to prepare in advance) and revise it according to suggestions given in the workshop.

Recommended reading (for your interest – no need to read in advance)

Hames, I. (2007). Peer review and manuscript management in scientific journals: Guidelines for good practice. Oxford: Blackwell.

In order to register, please send an eMail: registra@mail.uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
Kick-off Meeting
17.05.17
Wednesday
14:00
15:30
B 318 in A 5, entrance B
01.06.17
Thursday
09:00
18:00
B 316 in A 5, entrance B


Course Type: elective course

Course Number: SOC

Credits: 6

Prerequisites

- Active and regular participation
- Short oral presentation
- Two essays during the semester (each ca. 2-3 pages/1000 words, written in English)
- Research proposal (ca. 5-6 pages/3000 words, written in English or German)


Course Content

In life course sociology, welfare state policies and intuitions are perceived as main structuring forces of individuals’ life course patterns, transitions and sequences, as well as their biographical decisions and expectations. In social policy research, longitudinal micro-level data is increasingly used to evaluate policy outcomes and consequences from a life-course perspective. The seminar will bring together both strands of research with respect to theoretical and empirical perspectives. We will first get to know relevant theoretical approaches and scientific debates: How is the welfare state perceived and conceptualized in life course sociology? Can we identify ‘life course regimes’ in Europe? How do social policy researchers explain effects of redistribution and social investment in different life stages? In what way do welfare state change and ‘de-standardization’ of life courses interact? Second, the seminar will focus on recent empirical studies from both strands of research covering topics as income trajectories, poverty risks over the life cycle, employment histories, family life, and the transition to retirement. Finally, the students will have the possibility to develop their own research proposal addressing one topic covered in the seminar.

Literature:

  • Dewilde, C. (2003). A life-course perspective on social exclusion and poverty, in: The British Journal of Sociology 54(1): 109–128.
  • Heinz, W. R./Huinink, J./Weymann, A. (Ed.) (2009). The life course reader. Individuals and societies across time, Frankfurt/Main: Campus-Verlag.
  • Leisering, L. (2004). Government and the life course, in: J. T. Mortimer/M. J. Shanahan (Ed.) Handbook of the Life Course, New York: Kluwer Academic/Plenum Publishers: 205-228.

Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
15.02.17
31.05.17
Wednesday
12:00
13:30
A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

In this advanced seminar, we will examine how multiple types of networks give rise to political action. Such networks may include direct political allegiances, as in party membership, as well as alternative economic, religious, ethnic or regional affiliations that channel political action. Substantive topics include, among others: coalition formation and dissolution in such fragmented party systems as in Italy; the role of clientele networks in shaping political institutions in developing countries; the mechanisms whereby social networks facilitate cooperative behavior in settings with little social capital to begin with; and institutional mechanisms whereby social networks reinforce or undermine political polarization. The emphasis throughout the seminar is on understanding empirical puzzles. To this end, we will primarily consider recent empirical studies on political networks in the social sciences.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesday
12:00
13:30
A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

This course scrutinizes social actors in economic settings and asks how their relational arrangements and decision strategies shape modern life. We take three analytical perspectives: (1) Foundations, including markets, money, work and consumption; (2) conceptual frameworks of economic action, status, relational and network arrangements, as well as institutional contexts on the macro-level; and (3) detailed and comprehensive analyses of industries, regions, and other cases. Substantively, we analyze problems that take us from the Bazaar economy to Wall St., from Broadway musical productions to Mexican factory shop floors as well as from Baden-Württemberg’s craft economy to global trading networks. We focus on two sets of broader questions: On the one hand, we will ask how these arrangements work, when they fail, and why. On the other hand, we scrutinize which aspects of modern social life are considered economic, which are not, and to what effect. This course aims to provide an understanding of markets, organizations, innovation, and the division of labor, as well as of strategies for studying these topics empirically.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.17
29.05.17
Monday
15:30
17:00
B 317 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Prerequisites

Knowledge of basic concepts and theoretical models in sociology, in particular in the field of social stratification and social inequality, is beneficial. In addition sound methodological competencies are advantageous.


Course Content

Social stratification and inequality are universal characteristics of human societies. But the extent of inequality, the relevant dimensions of inequality and their interconnectedness vary across societies and through time. The course covers the core aspects of social stratification and inequality in modern societies.

During each session topics will be introduced by the instructor and then specific aspects will be highlighted in presentations by students (see dates below). Topics for presentations can be arranged with the instructor either before the start of the course via e-mail or at the first session.

It is assumed that all participants read the basic literature, actively follow the course, engage in discussions and do a presentation. The term paper should not exceed 5,000 words and should be delivered electronically via e-mail preferably in PDF-format to the instructor.

Deadline for delivery of the term paper is midnight 30 June 2017.

 

Dates and topics

17/02 Introduction
Social stratification and social inequality

24/02  Education
Education 1: Empirical findings, theoretical reflection

10/03
Education 2: Transitions in the educational system and to work

Transitions 1: From elementary to secondary school

Transitions 2a: From school to vocational training

Transitions 2b: From school to academic training

Transitions 3: From school to work

Income and assets

24/03 
Income and assets – poverty and wealth
Development of income distribution
Distribution of material resources in Germany
“Working Poor”: the poverty of working population

07/04  Conception of social inequality
Classes, conditions, circumstances, lifestyles and milieus

28/04 
Lifestyles and social environment

12/05 Social mobility and status attainment
Social mobility: Evidence for Germany
Social mobility: International perspectives

26/05 
The process of status attainment

Final discussion


Competences acquired

In this course you will learn how social stratification, social inequality and social mobility shape Western societies. You will be introduced to the main dimensions of social inequality and processes by which social inequality is (re-)produced but also transformed. Through your course work, your presentation and your term paper you will gain experience in writing scientific texts and presenting scientific results.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular
17.02.17
Fridays
10:15
13:30
318 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

In this course, we focus on one specific dimension of social inequality, which until very recently has been largely neglected in social stratification research: wealth. We will start by learning about the importance of wealth in the process of social stratification and the reasons for its longtime disregard. We will then go on to study recent research on the distribution of wealth; the composition of wealth; determinants of wealth and wealth inequality; sources of differences in wealth and consequences of wealth inequality. In each session, we will present and critically discuss a number of empirical studies, mostly from sociology but also from related disciplines, like economics. Moreover, we will learn about available data to analyze the distribution of wealth as well as about difficulties in the assessment of wealth and the measurement of wealth inequality. The seminar has an international comparative focus.

 

Assessment
 
Students are required to attend all four days of the seminar, to actively participate in the seminar and to give an oral presentation. Credits (100%) will be granted for a seminar paper.
 
Active participation is demonstrated by regular attendance, by reading required basic articles, by preparing questions to initiate the discussion and by contributing to the discussion.
 
Oral presentation
In oral presentations participants present key aspects of texts and themes of one session, based on the basic as well as the additional literature; presentations (PowerPoint or similar) should be submitted to the instructor at least one week prior to the presentation date. Participants may apply for a session to present in by writing to the instructor: nora.skopek@gesis.org. Please give your first, second and third priority. Themes will be allocated to participants in order of application (first come‐first served).
 
Active participation
Moreover, each student has to choose two sessions to prepare questions and/or arguments derived from the session literature (basic and additional literature) to discuss with the audience. Questions/arguments should be submitted to the instructor at least one week prior to the seminar date. Questions/arguments will be made available to all participants of the seminar. Participants may apply for a session to prepare questions/arguments writing to the instructor: nora.skopek@gesis.org. Please give your first, second and third priority. Themes will be allocated to participants in order of application (first come‐first served).
 
Finally, each student has to choose one session to prepare answers/arguments for the discussion points/questions/arguments raised by their peers. Participants may apply for a session to prepare answers/arguments for the discussion points writing to the instructor: nora.skopek@gesis.org. Please give your first, second and third priority. Themes will be allocated to participants in order of application (first come‐first served). No materials have to be handed in here beforehand.
 
Seminar paper
The seminar paper will usually focus on the same or similar aspect as the presentation and should be 5,000 words long (excluding tables, figures, references). Papers have to be delivered to the instructor in electronic form (preferably as pdf‐document) to nora.skopek@gesis.org.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
24.02.17
Friday
15:30
18:45
317 in A 5, 6 entrance B
11.03.17
Saturday
09:00
18:00
317 in A 5, 6 entrance B
18.03.17
Saturday
09:00
18:00
317 in A 5, 6 entrance B
19.03.17
Sunday
09:00
18:00
317 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: SOC

Credits: 6

Course Content

Immigration has many diverse effects on contemporary modern societies. It affects demographic processes, social inequality, policy and many other realms of the social life. These changes are experienced not only by the immigrants but by all members of society. In this course we focus our attention on the negative attitudes of the (native) public towards migrants and foreigners. The course will propose in introduction into the main theories explaining anti-immigrant sentiment and its reduction and empirical findings supporting these theories. Specifically, we shall focus on the role of perceptions of material and cultural ethnic competition, national sentiments, personality traits, values, and interethnic contact. Reflecting on contemporary immigration into Europe we shall also discuss anti-Islam and antisemitism as special cases of anti-foreigner sentiment.


Course requirements:
Regular participation in all classes
Reading of items in the syllabus before each lecture
One paper presentation including a discussion
Submission of final assignment (TBA)


Class 1 17.2.: Introduction: What is Prejudice, and how can we explain it
Jackson, L. M. (2011). Defining prejudice. In The Psychology of Prejudice: From Attitudes to Social Action (pp.7-28). Washington, DC, US: American Psychological Association.
Zamora-Kapoor, A., Kvincic, P., & Causey, C. (2013). Anti-Foreigner Sentiment: State of the Art. Sociology Compass, 7(4), 303-314.
 
Class 2 3.3.: The group conflict/threat theory
Quillian, L. (1995). Prejudice as a Response to Perceived Group Threat: Population Composition and Anti-Immigrant and Racial Prejudice in Europe. American Sociological Review, 60(4), 586-611.
Semyonov, M., Raijman, R., & Gorodzeisky, A. (2006). The Rise of Anti-foreigner Sentiment in European Societies, 1988-2000. American Sociological Review, 71(3), 426-449.
Lancee, B., & Pardos‐Prado, S. (2013). Group Conflict Theory in a Longitudinal Perspective: Analyzing the Dynamic Side of Ethnic Competition. International Migration Review, 47(1), 106-131.
 
Class 3 17.3.: National sentiments (national identity)
Huddy, L. (2016). Unifying national identity research. In: J., Grimm, L., Huddy, P., Schmidt, & J., Seethaler (Eds.), Dynamics of National Identity. Media and societal factors of what we are (9-21). London: Routledge.
Lewin-Epstein, N., & Levanon, A. (2005). National identity and xenophobia in an ethnically divided society. International Journal on Multicultural Societies, 7(2), 90-118.
Sarrasin, O., Green, E. G. T., & Fasel, N. (2016). Critical views of the Nation, national attachment, and attitudes toward immigrants in Switzerland. In: J., Grimm, L., Huddy, P., Schmidt, & J., Seethaler (Eds.), Dynamics of National Identity. Media and societal factors of what we are (192-205). London: Routledge.
 
Class 4 31.3.: Personality traits
Altemeyer, B. (1988). How Do People Become Authoritarians?. In Enemies of Freedom: Understanding Right-Wing Authoritarianism (pp. 51-104). San Francisco, CA, US: Jossey-Bass.
Esses, V. M., Jackson, L. M., & Armstrong, T. L. (1998). Intergroup Competition and Attitudes Toward Immigrants and Immigration: An Instrumental Model of Group Conflict.Journal of Social Issues,54(4), 699-724.
Craig, M. A., & Richeson, J. A. (2014). Not in My Backyard! Authoritarianism, Social Dominance Orientation, and Support for Strict Immigration Policies at Home and Abroad.Political Psychology,35(3), 417-429.
 
 
Class 5 14.4.: Values
Davidov, E., & Meuleman, B. (2012). Explaining Attitudes Towards Immigration Policies in European Countries: The Role of Human Values.Journal of Ethnic and Migration Studies,38(5), 757-775.
Kalir, B. (2015). The Jewish State of Anxiety: Between Moral Obligation and Fearism in the Treatment of African Asylum Seekers in Israel. Journal of Ethnic and Migration Studies, 41(4), 580-598.
Schwartz, S. H. (2007). Universalism Values and the Inclusiveness of Our Moral Universe. Journal of Cross-Cultural Psychology, 38(6), 711-728.
 
Class 6 28.4.: Intergroup contact
Hewstone, M., & Swart, H. (2011). Fifty‐odd years of inter‐group contact: From hypothesis to integrated theory. British Journal of Social Psychology, 50(3), 374-386.
Pettigrew, T. F., Tropp, L. R., Wagner, U., & Christ, O. (2011). Recent advances in intergroup contact theory. International Journal of Intercultural Relations, 35(3), 271-280.
Martinović, B. (2013). The Inter-Ethnic Contacts of Immigrants and Natives in the Netherlands: A Two-Sided Perspective. Journal of Ethnic and Migration Studies, 39(1), 69-85.

Class 7 12.5.: Anti-Islam / Antisemitism
Carol, S., Helbling, M., & Michalowski, I. (2015). A Struggle over Religious Rights? How Muslim Immigrants and Christian Natives View the Accommodation of Religion in Six European Countries. Social Forces, 94(2), 647-671.
Bergmann, W. (2008). Anti‐Semitic Attitudes in Europe: A Comparative Perspective. Journal of Social Issues, 64(2), 343-362.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
17.02.17
26.05.17
Friday
08:30
11:45
B 6, 23-25 entrance A

Lecturer(s)


Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

CSSR, Literature Review

You should be prepared to address the following questions: What makes a particular research question an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?


Course Content

The goal of this course is to provide support and crucial feedback on writing students' dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions (building on the Literature Review), the development of a theoretical framework, the specification of the methodology and planned empirical analysis.

Nota bene: Further meeting dates and locations will be determined during the first session.

Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.03.16
Monday
09:00
13:30
Room 307 in D 7, 27
11.03.16
Friday
09:00
13:30
Room 307 in D 7, 27
25.04.16
Monday
09:00
13:30
Room 307 in D 7, 27
06.05.16
Friday
09:00
13:30
Room 307 in D 7, 27
1st meeting
15.02.17
Wednesday
10:15
11:45
307 in D7, 27

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

Participation is mandatory for second and third year CDSS students of 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 the 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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
15.02.17
31.05.17
Wednesday
12:00
13:30
B 317 in A 5, 6 entrance B

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review


Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
16.02.17
01.06.17
Thursday
12:00
13:30
B 143 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.



Course Type: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
03.08.17
25.08.17
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Will require attendance of the Bayesian Statistics for Social Scientist I course taught by Prof. Traunmüller or knowledge of the topics covered.


Course Content

Bayesian statistics has long been overlooked in the quantitative methods training for social scientists.  Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class.  This is not surprising.  First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software.  Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful (and free) statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.

The orientation of this workshop is to introduce social scientists to advanced elements of Bayesian statistics and to show through discussion and practice, why the Bayesian perspective provides a powerful alternative to the frequentist perspective.  It is assumed that students of the workshop will have a background in basic Bayesian statistics, though the workshop will contain some review.  Some exposure to multilevel modeling and factor analysis is desirable.

Readings

  • Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14, 382–417. pdf
  • Kaplan, D. & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling. (pp 650-673), New York: Guilford Publications, Inc. pdf
  • Kaplan, D. & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (ed.), Oxford Handbook of Quantitative Methods. (pp 407-437) Oxford: Oxford University Press. pdf
  • Kaplan, D. & Park, S. (2013). Analyzing international large-scale assessment data within a Bayesian framework. In L. Rutkowski, M. Von Davier, and D. Rutkowski (eds.), A Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis. (pp 547-581). London: Chapman Hall/CRC Press. pdf
  • Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press. 
  • Kaplan, D. (2015). The future of quantitative inquiry in education: Challenges and opportunities. In M. J. Feuer, A. I. Berman, and R. C. Atkinson (eds.), The past as prologue: The National Academy ofEducation at 50. Members reflect. (pp. 109{115). Washington, DC. National Academy of Education. pdf
  • Kaplan, D. & Lee, C. (2015). Bayesian model averaging over directed acyclic graphs with implications for the predictive performance of structural equation models. Structural Equation Modeling.doi:10.1080/10705511.2015.1092088 pdf
  • van de Schoot, R., Kaplan, D., Denissen, J., Asndorpf, J. B., Neyer, F. J. & van Aken, M. A. G. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. DOI: 10.1111/cdev.12169 pdf

 

Workshop Outline

Day 1

Morning:

1.  Major differences between the Bayesian and frequentist paradigms of statistics.
2.  Bayes’ theorem; The likelihood, The nature of priors; The posterior distribution.
3.  Bayesian hypothesis testing; Contrasts with frequentist hypothesis testing.

Afternoon:

1.  Bayesian computation; MCMC; diagnostics
2.  Introduction to "rjags”.
3.  Exploration of distributions under different priors

Day 2

Morning:

1.   Bayesian model building.
1.   Bayesian model evaluation.
2.   Bayesian model averaging.
3.   Bayesian linear regression.

Afternoon:

1.  Student analyses – Bayesian regression analysis

Day 3

Morning:

1.  Advanced topics; HLM; factor analysis (time permitting)
2.   Final philosophical issues

 Afternoon:

1.  Final student analyses

 

End of course assignment to be completed within 4 weeks of the workshop.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.05.17
24.05.17
09:00
16:00
A 301 in B6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
31.03.17
07.04.17
Friday
09:00
16:00
EO 154
28.04.17
05.05.17
Friday
09:00
16:00
509 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

Lecture "Longitudinal Data Analysis"

 

The lecture gives a broad overview over methods of longitudinal data analysis. The focus of the course lies on methods for panel and event history data analysis and their application in the social sciences. Attendance of the complementary course "Data Sources in Social Sciences" is highly recommended as the course illustrates the practical application of the methods in Stata and deepens understanding of the theoretical content of the lecture.

Lab Course "Data Sources in Social Sciences"

Using Stata we practice methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) with examples mainly from the German SOEP. Attendance of the complementary lecture " Longitudinal Data Analysis " is highly recommended as firm knowledge of the lecture content is presumed. In addition, a further prerequisite for participation is firm knowledge of data preparation and estimation of simple linear regressions in Stata.

Suggested Readings:

  • Blossfeld, H.-P., K. Golsch, and G. Rohwer (2009): Event History Analysis with Stata. New York/ London: Psychology Press. [But avoid the philosophical part of the book on causality in chapter 1]
  • Andreß, H.J.,K. Golsch, and A. Schmidt (2013) Applied Panel Data Analysis for Economic and Social Surveys. Springer.

6 ECTS will be awarded for successful completion of an exam and an additional 2 ECTS can be awarded for participation in the lab course and submission of two practical assignments.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
15.02.17
31.05.17
Wednesday
10:15
11:45
B 244 in A 5, 6 entrance B
Tutorial
Lab Course
16.02.17
01.06.17
Thursday
15:30
17:00
C-108 (PC/Methods Lab) in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Prerequisites: Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

cran.r-project.org/manuals.html
www.rstudio.com/online-learning/
www.statmethods.net


Course Content

This course provides an introduction to supervised statistical learning techniques such as regression trees, random forests and boosting and discusses their potential applications in the social sciences. These methods focus on predicting an outcome Y based on some data-driven function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. In connection with the empirical applications, this course will also discuss some aspects of data gathering, curation and data quality within the big data framework. The discussed methods will be implemented using the statistical programming language R.


Competences acquired

At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods. Furthermore, students will gain insights into new types of data, their advantages and their potential drawbacks.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesday
10:15
11:45
A305 in B 6, 23-25 entrance A
28.02.17
Tuesday
13:45
15:15
A303 in B 6, 23-25 entrance A

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
14.02.17
23.05.17
Tuesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

The course includes theoretical sessions on formal model foundations as well as empirical demonstrations and practical exercises with Mplus. Students have to be familiar with the basics of structural equation modeling and multilevel analysis from introductory Master courses on multivariate statistics.


Course Content

The seminar introduces fundamentals and advanced topics in linear structural equation modeling and multilevel modeling, including

  • Principles of Structural Equation Modeling
  1. ­       Model equations
  2. ­       Parameter estimation and model testing
  3. ­       Model specification with Mplus
  4. ­       Model applications
  • Advanced Topics in Structural Equation Modeling
  1. ­       Modeling continuous and discrete variables
  2. ­       Multi-group designs and measurement invariance
  3. ­       Mixed structural equation models
  4. ­       Structural equation models for longitudinal data
  • Principles of Multilevel Modeling
  1. ­       Regression models for hierarchical data structures
  2. ­       Equivalence of multilevel and structural equation models
  3. ­       Model specification with Mplus
  4. ­       Model applications
  •  Advanced Topics in Multilevel Modeling
  1. ­       Generalized multilevel models
  2. ­       Latent growth curve analysis and dynamic predictors
  3. ­       Crossed multilevel models
  4. ­       Multilevel structural equation models

 

Sessions:

Friday, 24th March, 9:00-13:00

Friday, 7th April, 9:00-13:00

Friday, 5th May, 9:00-13:00

Friday, 19th May, 9:00-13:00

Friday, 2nd June, 9:00-13:00, 14:00-16:00

Friday, 9th June, 9:00-13:00, 14:00-16:00

 All in room EO 162 CIP-Pool


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - first session 24 March
24.03.17
Fridays
09:00
13:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Mathematics for Social Scientists


Course Content

The lectures aim at introducing students to stochastic models such as Markov chains and their applications. In a first part a minimum level of needed mathematical tools such as basic linear algebra will be introduced, followed by an introduction to Markov chains. Mathematical content will be addressed on a level accessible to students without specialized Math background. The final part of the lectures gives examples Markov chains and some applications. Overall, we intend to raise awareness of interdisciplinary research between Mathematics and Social Science without using an overkill of methodology.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.17
29.05.17
Monday
10:15
11:45
C 116 in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Prerequisites

  • 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 ( www.r-project.org )
  • A Beaker Lab notebook account (free):
    https://lab.beakernotebook.com


Course Content

This syllabus is updated occasionally, so please check back at least weekly to see if new information has been posted

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: http://www.metafor-project.org ).

Course topics:

  • The R package ´metafor´
    http://www.metafor-project.org
  • Interim report presentations by participants:  Data structure and sample data

  • Meta-Analysis with R Exercise

  • Final presentations by participants: Results

Literature:

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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
17.02.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
every three weeks
02.03.16
13.04.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
biweekly
27.04.16
11.05.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
01.06.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
irregular, please check for dates in Portal 2
15.02.17
24.05.17
Wednesday
15:30
18:45
B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

Course Readings:

  • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
  • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
  • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is necessary for the assignments which complement the lecture (6 ECTS).


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.17
31.05.17
Wednesday
08:30
10:00
B 143 in A 5, 6 entrance B
Tutorial
16.02.17
01.06.17
Thursday
08:30
10:00
B 317, A 5, 6 entrance B

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
16.02.17
01.06.17
Thursday
10:15
11:45
EO 256, Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:
* Generalized linear mixed models with lme4 (also known as hierarchical models)
* Simple structural equation models
* Basic set-up of Monte-Carlo simulations
* Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
16.02.17
30.03.17
Thursday
13:45
17:25
EO 162 CIP-Pool, Schloss Ehrenhof Ost
04.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
18.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
01.06.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
13.02.17
29.05.17
Monday
08:30
10:00
B 143 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: POL

Credits: 8

Course Content

The seminar addresses a burning issue in international relations: the linkage between displacement, refugee flows, international migration and conflict. According to a June 2015 report by the UN High Commissioner for Refugees (UNHCR), worldwide displacement has now hit an all time high, with close to 60 million people forced to leave home and seek safety elsewhere. To come to grips with the complex relation between conflict and displacement, we start off with some fact-finding and definitions: Who is a refugee, an asylum seeker and an economic migrant? Which overlaps exist between the different concepts and how can they be separated? What are recent global and regional trends and how do they compare to past records? In a second step, we will examine those factors that lead people to leave home and flee across boarders or even seas What are the major causes of displacements and refugees flows, and how do they compare to other patterns of international migration? When and how do people seek refugee in another country? What are the motives behind migration? What are the inter-linkages between conflict and migration? In addition, we will have a closer look at the effects of humanitarian aid and assess possible risks associated with the establishment of refugees camp especially, notably the recruitment of combatants. In the light of the current mass refugee movements, we also study the consequences that refugee and migration flows have in the recipient countries. We look at direct neighbors of conflict-ridden regions, notably the EU and other developed countries. What are their policies towards refugees and migrants? Which social and economic issues are at stake? What instruments have been or are being put in place at the international level to address displacement, and how effective are they? In a final part of the seminar, we will apply the knowledge and insights gained and deep-dive into several case studies.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
17.02.17
02.06.17
Friday
10:15
11:45
A102 in B6, 23-25, entrance A

Lecturer(s)


Course Type: elective course

Course Number: POL/SOC

Credits: 8

Prerequisites

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.


Course Content

Comparison is essential to research in political sociology. At the same time, it raises multiple issues of comparability and equivalence. What is ‘similar’, what is ‘different’? And how can we figure out whether measures are equivalent or not? In this seminar, we will address the conceptual and methodological issues in measurement in comparative political research. Students will review the latest empirical studies in the field and prepare research papers in which they analyze specific questions using available data sets.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesdays
12:00
13:30
B 318 in A 5, 6 entrance B

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

  • Foundations of linear algebra and probability theory (high school level)
  • Computer skills that allow you to get familiar with complex applications fast

Course Content

The course presents methods for the computer assisted automatic analysis of digital documents as a basis for further quantitative content analyses used in social and cultural sciences.

In the beginning we will present some possible analyses computational linguistics can offer to social and cultural sciences using the software GATE. This is followed by a short programming course in the Python programming language introducing a more flexible way of pre-processing texts and also access to text data through web crawling and conversion of different file formats. More advanced methods on text classification and clustering are presented later on along with more tools that can be used. In the final part of the course participants will present their own project work to each other.

Passing the course is based on:

  • Implementation of a project
  • Final presentation
  • Report (~ 15 pages)

Topics by class (always from 2-5pm):

24.02.2017 – Introduction to the course; Presentation of methods and tools that can be used to enhance quantitative content analysis; Examples of works in computational social science; Discussion about initial ideas for the course projects.

28.02.2017 – Introduction to Python; Lecture and hands-on exercises.

07.03.2017 – Introduction to NLTK (natural language processing tools in Python); Lecture and hands-on exercises (guest teacher: Federico Nanni).

21.03.2017 – Crawling and downloading relevant data with Python; Lecture and hands-on exercises (guest teacher: Federico Nanni); preparation of data for machine learning experiments (Python).

05.05.2017 – Introduction to machine learning; classification and clustering; Lecture and step-by-step demonstration in Weka; discussions about the projects.

12.05.2017 – Machine learning hands-on exercises using Weka; final discussions about the projects.

09.06.2017 – Project presentations (course participants).

Final project report submissions deadline: 30.06.2017


Competences acquired

Basic programming skills in Python, familiarity with natural language processing tools, ability to use machine learning (classification and clustering algorithms)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
29.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
Intro session, further dates in the list above.
24.02.17
Friday
14:00
17:00
C-109 in A5, 6 entrance C
Presentations
09.06.17
Friday
14:00
16:00
C-109 in A5, 6 entrance C


Course Type: elective course

Course Number: RES

Credits: 2

Prerequisites

Advanced PhD-Students (3rd year) with first experience in writing journal articles (at least one submitted manuscript). PostDocs are also welcome. Max. 20 participants


Course Content

Reviews provided by experienced researchers in the field are a key instrument in order to assure high quality in research. Writing reviews, however, does not only require expertise in the field of research. It requires also knowledge on the review process itself and skills in writing clear reviews in order to make a convincing point to the editor and to the author. This course will provide you with the necessary knowledge and skills in order to write better reviews for academic journals. Such skills and knowledge will hopefully not only be helpful for writing better reviews. I am convinced that a profound knowledge about reviews and the review process will also be helpful for one’s own writing of research papers. 

Topics of the workshop

-       Functions of peer – review

-       Forms of peer – review

-       Process of reviewing

-       General rules for writing reviews

-       Structure of reviews

-       Do’s and don’ts in reviewing

-       Specific problems

In the working part, you will work on your own review (which you are asked to prepare in advance) and revise it according to suggestions given in the workshop.

Recommended reading (for your interest – no need to read in advance)

Hames, I. (2007). Peer review and manuscript management in scientific journals: Guidelines for good practice. Oxford: Blackwell.

In order to register, please send an eMail: registra@mail.uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
Kick-off Meeting
17.05.17
Wednesday
14:00
15:30
B 318 in A 5, entrance B
01.06.17
Thursday
09:00
18:00
B 316 in A 5, entrance B

Lecturer(s)


Course Type: core course

Course Number: DIS

Credits: 2+8

Prerequisites

CSSR, Literature Review

You should be prepared to address the following questions: What makes a particular research question an interesting question? Is it an important question? What contributions would this question and the answers make to the scholarly literature? What strategies are there to answer your research question(s)?


Course Content

The goal of this course is to provide support and crucial feedback on writing students' dissertation proposal. Such a proposal is a research outline that delineates the doctoral thesis project, including the motivation for research question(s), the survey of the relevant theoretical and empirical contributions (building on the Literature Review), the development of a theoretical framework, the specification of the methodology and planned empirical analysis.

Nota bene: Further meeting dates and locations will be determined during the first session.

Information on how to submit the dissertation proposal (8 ECTS) can be retrieved from the CDSS regulations section.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.03.16
Monday
09:00
13:30
Room 307 in D 7, 27
11.03.16
Friday
09:00
13:30
Room 307 in D 7, 27
25.04.16
Monday
09:00
13:30
Room 307 in D 7, 27
06.05.16
Friday
09:00
13:30
Room 307 in D 7, 27
1st meeting
15.02.17
Wednesday
10:15
11:45
307 in D7, 27


Course Type: core course

Course Number: RES

Credits: 2

Prerequisites

TCBI, CSSR, Dissertation Proposal


Course Content

Please check with individual chairs in the Psychology department for dates and times of research colloquia.



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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
13.02.17
29.05.17
Monday
15:30
17:00
EO 259

Lecturer(s)


Course Type: core course

Course Number: RES

Credits: 3

Prerequisites

CSSR, Literature Review


Course Content

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


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
16.02.17
01.06.17
Thursday
12:00
13:30
B 143 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET

Credits: up to 12

Prerequisites

CDSS PhD students have privileged access to the GESIS Summer School in Survey Methodology. Course credits will be recognized. To obtain information about the summer school program and registration, please refer to the GESIS website.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Summer School
03.08.17
25.08.17
09:00
18:00
GESIS, Cologne

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Will require attendance of the Bayesian Statistics for Social Scientist I course taught by Prof. Traunmüller or knowledge of the topics covered.


Course Content

Bayesian statistics has long been overlooked in the quantitative methods training for social scientists.  Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class.  This is not surprising.  First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software.  Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful (and free) statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.

The orientation of this workshop is to introduce social scientists to advanced elements of Bayesian statistics and to show through discussion and practice, why the Bayesian perspective provides a powerful alternative to the frequentist perspective.  It is assumed that students of the workshop will have a background in basic Bayesian statistics, though the workshop will contain some review.  Some exposure to multilevel modeling and factor analysis is desirable.

Readings

  • Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14, 382–417. pdf
  • Kaplan, D. & Depaoli, S. (2012). Bayesian structural equation modeling. In R. Hoyle (ed.), Handbook of Structural Equation Modeling. (pp 650-673), New York: Guilford Publications, Inc. pdf
  • Kaplan, D. & Depaoli, S. (2013). Bayesian statistical methods. In T. D. Little (ed.), Oxford Handbook of Quantitative Methods. (pp 407-437) Oxford: Oxford University Press. pdf
  • Kaplan, D. & Park, S. (2013). Analyzing international large-scale assessment data within a Bayesian framework. In L. Rutkowski, M. Von Davier, and D. Rutkowski (eds.), A Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis. (pp 547-581). London: Chapman Hall/CRC Press. pdf
  • Kaplan, D. (2014). Bayesian Statistics for the Social Sciences. New York: Guilford Press. 
  • Kaplan, D. (2015). The future of quantitative inquiry in education: Challenges and opportunities. In M. J. Feuer, A. I. Berman, and R. C. Atkinson (eds.), The past as prologue: The National Academy ofEducation at 50. Members reflect. (pp. 109{115). Washington, DC. National Academy of Education. pdf
  • Kaplan, D. & Lee, C. (2015). Bayesian model averaging over directed acyclic graphs with implications for the predictive performance of structural equation models. Structural Equation Modeling.doi:10.1080/10705511.2015.1092088 pdf
  • van de Schoot, R., Kaplan, D., Denissen, J., Asndorpf, J. B., Neyer, F. J. & van Aken, M. A. G. (2013). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. DOI: 10.1111/cdev.12169 pdf

 

Workshop Outline

Day 1

Morning:

1.  Major differences between the Bayesian and frequentist paradigms of statistics.
2.  Bayes’ theorem; The likelihood, The nature of priors; The posterior distribution.
3.  Bayesian hypothesis testing; Contrasts with frequentist hypothesis testing.

Afternoon:

1.  Bayesian computation; MCMC; diagnostics
2.  Introduction to "rjags”.
3.  Exploration of distributions under different priors

Day 2

Morning:

1.   Bayesian model building.
1.   Bayesian model evaluation.
2.   Bayesian model averaging.
3.   Bayesian linear regression.

Afternoon:

1.  Student analyses – Bayesian regression analysis

Day 3

Morning:

1.  Advanced topics; HLM; factor analysis (time permitting)
2.   Final philosophical issues

 Afternoon:

1.  Final student analyses

 

End of course assignment to be completed within 4 weeks of the workshop.

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.05.17
24.05.17
09:00
16:00
A 301 in B6, 23-25 entrance A


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

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


Course Content

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

Course readings

  • Lynch 2007. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. Chapters 2, 3, 6, and 8.1.
  • Jackman 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 2.5.
  • Jackman and Western 1994. Bayesian Inference for Comparative Research. American Political Science Review 88, pp. 412-423.
  • Johnson and Albert 1999. Ordinal Data Modeling. New York: Springer. Chapter 3.
  • Gelman and Hill 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press. Chapters 12, 13 and 14.
  • Gill 2008. Bayesian Methods for the Social and Behavioral Sciences. Boca Raton: Chapman & Hall/CRC. Chapter 9.

Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
31.03.17
07.04.17
Friday
09:00
16:00
EO 154
28.04.17
05.05.17
Friday
09:00
16:00
509 in L9, 7

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6+3

Course Content

Lecture "Longitudinal Data Analysis"

 

The lecture gives a broad overview over methods of longitudinal data analysis. The focus of the course lies on methods for panel and event history data analysis and their application in the social sciences. Attendance of the complementary course "Data Sources in Social Sciences" is highly recommended as the course illustrates the practical application of the methods in Stata and deepens understanding of the theoretical content of the lecture.

Lab Course "Data Sources in Social Sciences"

Using Stata we practice methods of longitudinal data analysis (especially first-difference-models, random/fixed effects-models, event history analysis) with examples mainly from the German SOEP. Attendance of the complementary lecture " Longitudinal Data Analysis " is highly recommended as firm knowledge of the lecture content is presumed. In addition, a further prerequisite for participation is firm knowledge of data preparation and estimation of simple linear regressions in Stata.

Suggested Readings:

  • Blossfeld, H.-P., K. Golsch, and G. Rohwer (2009): Event History Analysis with Stata. New York/ London: Psychology Press. [But avoid the philosophical part of the book on causality in chapter 1]
  • Andreß, H.J.,K. Golsch, and A. Schmidt (2013) Applied Panel Data Analysis for Economic and Social Surveys. Springer.

6 ECTS will be awarded for successful completion of an exam and an additional 2 ECTS can be awarded for participation in the lab course and submission of two practical assignments.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
15.02.17
31.05.17
Wednesday
10:15
11:45
B 244 in A 5, 6 entrance B
Tutorial
Lab Course
16.02.17
01.06.17
Thursday
15:30
17:00
C-108 (PC/Methods Lab) in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 6

Prerequisites

Prerequisites: Although this course will include a short introduction to R, students are encouraged to work through one or more R tutorials prior or during the first weeks of the course. Some resources can be found here:

cran.r-project.org/manuals.html
www.rstudio.com/online-learning/
www.statmethods.net


Course Content

This course provides an introduction to supervised statistical learning techniques such as regression trees, random forests and boosting and discusses their potential applications in the social sciences. These methods focus on predicting an outcome Y based on some data-driven function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. In connection with the empirical applications, this course will also discuss some aspects of data gathering, curation and data quality within the big data framework. The discussed methods will be implemented using the statistical programming language R.


Competences acquired

At the completion of this course, students will have a profound understanding of tree-based prediction methods and the machine learning perspective on statistical modeling. Students will learn the computational skills to apply and evaluate these methods. Furthermore, students will gain insights into new types of data, their advantages and their potential drawbacks.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
14.02.17
30.05.17
Tuesday
10:15
11:45
A305 in B 6, 23-25 entrance A
28.02.17
Tuesday
13:45
15:15
A303 in B 6, 23-25 entrance A

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
14.02.17
23.05.17
Tuesday
13:45
17:00
Room A 102 in B 6, 23-25 entrance A

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

The course includes theoretical sessions on formal model foundations as well as empirical demonstrations and practical exercises with Mplus. Students have to be familiar with the basics of structural equation modeling and multilevel analysis from introductory Master courses on multivariate statistics.


Course Content

The seminar introduces fundamentals and advanced topics in linear structural equation modeling and multilevel modeling, including

  • Principles of Structural Equation Modeling
  1. ­       Model equations
  2. ­       Parameter estimation and model testing
  3. ­       Model specification with Mplus
  4. ­       Model applications
  • Advanced Topics in Structural Equation Modeling
  1. ­       Modeling continuous and discrete variables
  2. ­       Multi-group designs and measurement invariance
  3. ­       Mixed structural equation models
  4. ­       Structural equation models for longitudinal data
  • Principles of Multilevel Modeling
  1. ­       Regression models for hierarchical data structures
  2. ­       Equivalence of multilevel and structural equation models
  3. ­       Model specification with Mplus
  4. ­       Model applications
  •  Advanced Topics in Multilevel Modeling
  1. ­       Generalized multilevel models
  2. ­       Latent growth curve analysis and dynamic predictors
  3. ­       Crossed multilevel models
  4. ­       Multilevel structural equation models

 

Sessions:

Friday, 24th March, 9:00-13:00

Friday, 7th April, 9:00-13:00

Friday, 5th May, 9:00-13:00

Friday, 19th May, 9:00-13:00

Friday, 2nd June, 9:00-13:00, 14:00-16:00

Friday, 9th June, 9:00-13:00, 14:00-16:00

 All in room EO 162 CIP-Pool


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
irregular - first session 24 March
24.03.17
Fridays
09:00
13:00
EO 162 CIP-Pool

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 4

Prerequisites

Mathematics for Social Scientists


Course Content

The lectures aim at introducing students to stochastic models such as Markov chains and their applications. In a first part a minimum level of needed mathematical tools such as basic linear algebra will be introduced, followed by an introduction to Markov chains. Mathematical content will be addressed on a level accessible to students without specialized Math background. The final part of the lectures gives examples Markov chains and some applications. Overall, we intend to raise awareness of interdisciplinary research between Mathematics and Social Science without using an overkill of methodology.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.17
29.05.17
Monday
10:15
11:45
C 116 in A 5, 6 entrance C

Lecturer(s)


Course Type: elective course

Course Number: MET

Credits: 7

Prerequisites

  • 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 ( www.r-project.org )
  • A Beaker Lab notebook account (free):
    https://lab.beakernotebook.com


Course Content

This syllabus is updated occasionally, so please check back at least weekly to see if new information has been posted

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: http://www.metafor-project.org ).

Course topics:

  • The R package ´metafor´
    http://www.metafor-project.org
  • Interim report presentations by participants:  Data structure and sample data

  • Meta-Analysis with R Exercise

  • Final presentations by participants: Results

Literature:

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.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
17.02.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
every three weeks
02.03.16
13.04.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
biweekly
27.04.16
11.05.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
01.06.16
Wednesday
15:15
18:30
A 305, B 6, 23-25 Bauteil A
irregular, please check for dates in Portal 2
15.02.17
24.05.17
Wednesday
15:30
18:45
B318 in A 5, 6 entrance B


Course Type: elective course

Course Number: MET/POL

Credits: 6+2

Prerequisites

Knowledge of Multivariate Analysis


Course Content

This course serves  as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R).

Course Readings:

  • Eliason, Scott R. 1993. Maximum Likelihood Estimation: Logic and Practice. Newbury Park: Sage.
  • Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.
  • King, Gary. 2008. Unifying political methodology: the likelihood theory of statistical inference. Ann Arbor, MI: University of Michigan Press.

Students who wish to pass this course must complete homework assignments and produce a research paper. Participation in the tutorial session (2 ECTS) is necessary for the assignments which complement the lecture (6 ECTS).


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.17
31.05.17
Wednesday
08:30
10:00
B 143 in A 5, 6 entrance B
Tutorial
16.02.17
01.06.17
Thursday
08:30
10:00
B 317, A 5, 6 entrance B

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
16.02.17
01.06.17
Thursday
10:15
11:45
EO 256, Schloss Ehrenhof Ost

Lecturer(s)


Course Type: elective course

Course Number: MET/PSY

Credits: 4

Course Content

This seminar will provide an introduction how to use R, a powerful programming language that is often used for statistical analyses, simulations, and cognitive modeling. The seminar first will provide a thorough introduction covering the core functionality such as objects, functions, data management, and plotting.
 
The last sessions of the seminar will address how to perform specific statistical analyses in R such as:
* Generalized linear mixed models with lme4 (also known as hierarchical models)
* Simple structural equation models
* Basic set-up of Monte-Carlo simulations
* Simple cognitive modeling (e.g., signal detection or multinomial processing trees)

It is planned that participants practice R in homework assignments and work on small group projects such as analyzing own data, replicating a paper, or running a small simulation.


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Seminar
biweekly
16.02.17
30.03.17
Thursday
13:45
17:25
EO 162 CIP-Pool, Schloss Ehrenhof Ost
04.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
18.05.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost
01.06.17
Thursday
13:45
17:00
EO 162 CIP-Pool, Schloss Ehrenhof Ost

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 II of the lectures addresses the following topics: interaction with others (e.g., altruism and helping behavior; game theory and social decision-making), relationships (e.g., attachment; separation, rejection, and loneliness), groups and identity (e.g., identify and self-concept; in- and outgroups and prejudice; herds, crowds, and religion), morality and antisocial behavior (e.g., neuroscience of morality, anger and aggression; control and responsibility), and social developmental (e.g., social learning during infancy, childhood, adolescence).


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
17.02.17
02.06.17
Friday
10:15
11:45
016/17 in L13, 15-17

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

  • Foundations of linear algebra and probability theory (high school level)
  • Computer skills that allow you to get familiar with complex applications fast

Course Content

The course presents methods for the computer assisted automatic analysis of digital documents as a basis for further quantitative content analyses used in social and cultural sciences.

In the beginning we will present some possible analyses computational linguistics can offer to social and cultural sciences using the software GATE. This is followed by a short programming course in the Python programming language introducing a more flexible way of pre-processing texts and also access to text data through web crawling and conversion of different file formats. More advanced methods on text classification and clustering are presented later on along with more tools that can be used. In the final part of the course participants will present their own project work to each other.

Passing the course is based on:

  • Implementation of a project
  • Final presentation
  • Report (~ 15 pages)

Topics by class (always from 2-5pm):

24.02.2017 – Introduction to the course; Presentation of methods and tools that can be used to enhance quantitative content analysis; Examples of works in computational social science; Discussion about initial ideas for the course projects.

28.02.2017 – Introduction to Python; Lecture and hands-on exercises.

07.03.2017 – Introduction to NLTK (natural language processing tools in Python); Lecture and hands-on exercises (guest teacher: Federico Nanni).

21.03.2017 – Crawling and downloading relevant data with Python; Lecture and hands-on exercises (guest teacher: Federico Nanni); preparation of data for machine learning experiments (Python).

05.05.2017 – Introduction to machine learning; classification and clustering; Lecture and step-by-step demonstration in Weka; discussions about the projects.

12.05.2017 – Machine learning hands-on exercises using Weka; final discussions about the projects.

09.06.2017 – Project presentations (course participants).

Final project report submissions deadline: 30.06.2017


Competences acquired

Basic programming skills in Python, familiarity with natural language processing tools, ability to use machine learning (classification and clustering algorithms)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
22.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
29.04.16
Friday
12:00
17:00
C-108 PC-Lab in A5, 6
Intro session, further dates in the list above.
24.02.17
Friday
14:00
17:00
C-109 in A5, 6 entrance C
Presentations
09.06.17
Friday
14:00
16:00
C-109 in A5, 6 entrance C


Course Type: elective course

Course Number: RES

Credits: 2

Prerequisites

Advanced PhD-Students (3rd year) with first experience in writing journal articles (at least one submitted manuscript). PostDocs are also welcome. Max. 20 participants


Course Content

Reviews provided by experienced researchers in the field are a key instrument in order to assure high quality in research. Writing reviews, however, does not only require expertise in the field of research. It requires also knowledge on the review process itself and skills in writing clear reviews in order to make a convincing point to the editor and to the author. This course will provide you with the necessary knowledge and skills in order to write better reviews for academic journals. Such skills and knowledge will hopefully not only be helpful for writing better reviews. I am convinced that a profound knowledge about reviews and the review process will also be helpful for one’s own writing of research papers. 

Topics of the workshop

-       Functions of peer – review

-       Forms of peer – review

-       Process of reviewing

-       General rules for writing reviews

-       Structure of reviews

-       Do’s and don’ts in reviewing

-       Specific problems

In the working part, you will work on your own review (which you are asked to prepare in advance) and revise it according to suggestions given in the workshop.

Recommended reading (for your interest – no need to read in advance)

Hames, I. (2007). Peer review and manuscript management in scientific journals: Guidelines for good practice. Oxford: Blackwell.

In order to register, please send an eMail: registra@mail.uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Workshop
Kick-off Meeting
17.05.17
Wednesday
14:00
15:30
B 318 in A 5, entrance B
01.06.17
Thursday
09:00
18:00
B 316 in A 5, entrance B

Register

Social Sciences Spring 2017

Dissertation Tutorial: Sociology (Gautschi/Hillmann/Keusch/Kreuter)
DIS
Dissertation Proposal Workshop
RES
CDSS Workshop: Sociology
RES
English Academic Writing
RES
MZES A Colloquium "European Societies and their Integration"
MET
6th GESIS Summer School in Survey Methodology
MET
Advanced Workshop on Bayesian Statistics for the Social Sciences
MET
Bayesian Statistics for Social Scientists I
MET
Longitudinal Data Analysis (Lecture + Lab Course)
MET
Machine Learning in the Social Sciences
MET
Multilevel Modeling
MET
Selected Multivariate Methods
MET
Stochastic Modeling for the Social Sciences
MET
Systematic Reviews and Meta-Analysis
MET/POL
Advanced Quantitative Methods
MET/PSY
Intensive Longitudinal Methods
MET/PSY
Research and applied methods: Programming in R
POL/SOC
Selected Topics in Comparative Politics: Travelling across time and space: Conceptual and measurement issues in comparative political sociology
RES
Computer-based Content Analysis (Bridge Course)
RES
Reviewing
SOC
Economy & the Welfare State: Life Course and Social Policy
SOC
Economy & the Welfare State: Political Networks
SOC
Economy & the Welfare State: Topics in Economic Sociology
SOC
Family, Education & Labour Market: Social structure, social inequality and social mobility
SOC
Migration & Integration: Current issues in social stratification: wealth inequality
SOC
Migration & Integration: Immigrants and the Receiving Society - the Emergence of Anti-immigrants Sentiment
RES
CDSS Workshop: Political Science
RES
MZES B Colloquium "European Political Systems and their Integration"
RES
SFB 884 Seminar Series
POL
Selected Topics in International Politics: Democracy, Dictatorship and the International System
POL
Selected Topics in International Politics: Exodus: Conflict, Migration and Refugees
RES
AC4/BC4: Colloquia II
RES
CDSS Workshop: Research in Psychology
PSY
Introduction to Social Cognitive Neuroscience (Part 2)