Lehrveranstaltungen

Dozent/in:: Florian Weiler
Angaben:: Hauptseminar, 2 SWS
Termine:: Mo, 10:00 - 12:00, RZ/00.05
Voraussetzungen / Organisatorisches:: Type: BAGSS-Seminar and MA Seminar (Hauptseminar Analyse politischer Institutionen) Time and venue: Monday, 10 - 12 am, room RZ/00.05 (Starting 06.10.2014) Registration: Flex Now, 01.09. - 20.10.2014 (Deregistration until 27.10.2014) Grading: Term paper (50%), 3 problem sets (50%) Prerequisite: Knowledge of introductory statistics ECTS: 8
Inhalt:: Course Description: This course is designed for students interested in statistical analysis who already possess some basic knowledge about statistics and, if possible, regression analysis. In the first part of the course we will discuss the basics of statistical modeling, i.e. what is a model, how does it relate to the data generating process, and which are the elements needed in each statistical model. After this short theoretical part, we will cover the classical linear regression model, the assumptions we make when running such a model, and how violations of these assumptions can be detected and fixed. Next we will discuss maximum likelihood estimation and then apply this technique to binary and categorical dependent variables (logit, probit, count models, etc.). In all these parts of the course we will discuss how to improve the basic models. The focus of the course in not on mathematics, but to give students an intuition of how the different modeling techniques actually work. In addition, the course will be very hands-on and application-oriented. Thus, at the end of the course participants should be able to apply the covered material to their own research. In addition, students should learn how to graphically present the results of the models for professional publications.
Empfohlene Literatur:: Introductory Readings Wooldridge, Jeffrey M. (2002). Introductory Econometrics. A Modern Approach. Mason, OH: Cengate Learning. Fox, John (2008). Applied Regression Analysis. Los Angeles, London: Sage. Fox, John and Sanford Weisberg(2011). An R Companion to Applied Regression. Los Angeles, London: Sage.

MA/SM: Introduction to R [MA/SM Introduction to R]

Dozent/in:: Florian Weiler
Angaben:: Vorlesung
Termine:: Blockveranstaltung, 16.3.2015 9:00 - 19.3.2015 18:00, RZ/01.03
Voraussetzungen / Organisatorisches:: Time and date: Monday, 30 March - Thursday, 2 April, 9am-16:00pm Venue: Room RZ/01.03 Registration: Please register by sending a mail to Marc Scheibner (marc.scheibner@uni-bamberg.de). Requirements: Doctoral students admitted to the Bamberg Graduate School of Social Sciences (BAGSS); MA in Political Science or equivalent qualification for visiting students
Inhalt:: Course Description: This workshop introduces students to many of the most commonly used features of R, an open source program for statistical computation. R provides the user with a wide variety of pre-programmed modeling and graphing techniques. But R is also a powerful programming language and allows users to adjust existing functions to their needs, and to write their own functions. This workshop intends to introduce students first to the R language, R s object oriented approach to statistical modeling, and the basics of writing functions, and second to the most commonly used pre-proprammed statistical techniques. The Introduction to R lectures in the morning will be accompanied by lab sessions in the afternoon to provide students with a hands-on experience of the techniques covered in class. The lab is structured to be relatively unguided, providing participants the opportunity to begin digging into the R computing environment at their own pace. During each lab session I will hand out exercises to be completed independently, or in collaboration with other students. I will be at hand to answer questions and help with the almost inevitable coding problems beginners of R are usually faced with. At the end of the course students should be able to work with R independently.
Empfohlene Literatur:: Introductory Readings: Zuur, Alain, Elena Ieno, and Erik Meesters (2009). A Beginner s Guide to R. Springer. Dordrecht, Heidelberg, London, New York: Springer. Fox, John, and Sanford Weisberg (2011). An R Companion to Applied Regression. 2nd edition, Thousand Oaks: Sage Publications, Inc. Muenchen, Robert A., and Joseph M. Hilbe (2010). R for Stata Users. Dordrecht, Heidelberg, London, New York: Springer. King, Garry, Kosuke Imai, and Olivia Lau (2008). Zelig: Everyone s Statistical Software. http://http://projects.iq.harvard.edu/zelig.

S: MA/SM Qualitative Methods of Comparative Social Inquiry

Dozent/in:: Ariadna Ripoll Servent
Angaben:: Seminar, 2 SWS, ECTS: 8, BAGSS PhD Kurs und MA Hauptseminar der Vergleichenden Politikwissenschaft
Termine:: Di, 14:00 - 16:00, FG1/00.06
Voraussetzungen / Organisatorisches:: BAGSS PhD Kurs und MA Hauptseminar der Vergleichenden Politikwissenschaft
Eligibility Requirements: The working language of this course is English. All coursework must be completed in English. Regular attendance and knowledge of the required readings will be expected.
MA students: To obtain a certificate, participants will have to give a presentation and a term paper (5000 to 6000 words – including title page, text, footnotes/endnotes, references, bibliography, annexes, etc.). Please note: The number of participants is limited. Student registration via FlexNow! is required. Submission of term paper deadline: 6 April 2015.
Inhalt:: This seminar-based course offers a broad introduction to the field of qualitative methods from a comparative perspective and beyond. It aims to situate the use of qualitative methods in different research traditions with the aim to uncover their advantages and limitations. The course is divided in four parts: the first part investigates the meaning of qualitative methods and its links with particular ways to investigate and understand the social world; the second part concentrates on various methods to gather qualitative data; the third (and main part) looks at how primary and secondary qualitative data can be used and analysed. It discusses the importance of theory, causality and how they are linked to the way we interpret and present our data. The final part of the course deals with the assessment of qualitative data and methods – discussing the standards of validity, reliability and generalisability as well as broader questions of ethics in social science research



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