HS (MA): Politikwissenschaftliche Methoden I - Grundlagen der Wissenschaftstheorie

Dozent/in:

Johannes Marx

Angaben:

Seminar, 2 SWS, ECTS: 8

Termine:

Do, 8:00 - 10:00, F21/03.79

Voraussetzungen / Organisatorisches:

Leistungsnachweis: Referat mit Hausarbeit, Abgabetermin: 24.04.2017

Beginn der Anmeldung in FlexNow (unter Prüfungen): 01.10.2016, 10:00 Uhr
Späteste An-/Abmeldung: 31.10.2016, 23:59 Uhr

Inhalt:

This course introduces into the terminology and basic methods of argumentation for political science. In the beginning of the seminar, we will deal with the appropriate form and function of arguments. By analyzing text samples, we will learn to identify valid and invalid forms of argumentation. Subsequently, the method of reconstruction will be introduced, which is applied to evaluate arguments. At the same time participants will become familiar with basic logic. In the end, students are taught to identify and avoid typical fallacies in argumentation. The aim of this course is to develop a critical stance towards scientific terminology and to acquire the competence of writing proper scientific texts. This course is taught in English.

Empfohlene Literatur:

Sinnott-Armstrong, Walter and Vogelin, Robert 2015: Understanding Arguments. An Introduction to Informal Logic.

Weitere Literatur wird in der ersten Sitzung bekannt gegeben.

HS: Politikwissenschaftliche Methoden II: Qualitative Methods of Social Inquiry

Dozent/in:

Ariadna Ripoll Servent

Angaben:

Hauptseminar, 2 SWS, Anmeldung über FlexNow ab 01.09.2016

Termine:

Einzeltermin am 17.10.2016, 12:00 - 14:00, FG1/00.06
Einzeltermin am 24.10.2016, Einzeltermin am 14.11.2016, Einzeltermin am 21.11.2016, Einzeltermin am 9.1.2017, Einzeltermin am 16.1.2017, Einzeltermin am 6.2.2017, 12:00 - 16:00, FG1/00.06

HS: Politikwissenschaftliche Methoden III: Introduction to statistics for political and social sciences

Dozent/in:

Daniel Höhmann

Angaben:

Hauptseminar, 2 SWS, Anmeldung über FlexNow ab 01.10.2016

Termine:

Mi, 10:00 - 12:00, RZ/00.05

Inhalt:

Hauptseminar: Politikwissenschaftliche Methoden III: Introduction to statistics for political and social sciences [in English] (N.N.) Wednesday, 10-12 in RZ 00.05
This course introduces and applies basic statistical methods. It is designed as a refresher course for master students with little statistical training during their Bachelor degree. Topics covered include descriptive statistics, measures of correlation, linear regression, and logistic regression. The seminar applies these techniques on sample data using the statistical software package Stata. At the end of the course, students should understand the basic logic of inferential statistics and be able to perform simple analyses using statistical software.

Introduction to R

Dozent/in:

Ulrich Schroeders

Angaben:

Seminar, 2 SWS, ECTS: 8, - Master-Students: Please register via FlexNow - BAGSS/Doctoral Students: Please send a mail to Miriam Schneider (miriam.schneider@uni-bamberg.de) to register.

Voraussetzungen / Organisatorisches:

• Master-Students: Please register via FlexNow
• BAGSS/Doctoral Students: Please send a mail to Miriam Schneider (miriam.schneider@uni-bamberg.de) to register.

Inhalt:

This seminar deals with introductory and intermediate aspects of R, an open source statistical program that has become in recent years more and more popular in the behavioral and social sciences. In principle, the R ecosystem provides the user with a huge variety of pre-programmed modeling techniques and visualization tools, but the flexibility and versatility comes at the cost of learning an often unintuitive programming language. In this seminar students are first introduced to the basic units of R (operators, objects, functions, etc.) and structures (if-else, ifelse, while, etc.). Thereafter, the entire process of data analysis is discussed in detail including a) reading in data, b) data preparation and handling, c) descriptive statistics, d) advanced statistical analyses, e) data visualization, and f) saving output. A focus of this seminar is on developing hands-on programming skills by solving real-world analytical problems, for example, how to assess large-scale data via Internet, effectively recode data or find a catchy visualization of results. Since R is taught from scratch, neither experience in R (or another programming language) nor usage of a specific software is necessary. However, prospective participants should possess a solid knowledge of basic statistical concepts (e.g., variance, covariance, correlation) and analytical techniques (e.g., regression analysis). To obtain full ECTS points, participants have to complete assignments every two weeks.

Empfohlene Literatur:

‒ Field, A., & Miles, J. (2012). Discovering Statistics Using R. London ; Thousand Oaks, Calif: Sage Publications Ltd.
‒ Kabacoff, R. (2015). R in Action: Data Analysis and Graphics with R. Shelter Island: Manning.
‒ Matloff, N. (2011). The Art of R Programming: A Tour of Statistical Software Design. San Francisco: No Starch Press.