Academic Writing

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Jonathan Mole, (Europa-Universität Flensburg)

Date: Thursday, 01/10/15 (09:30 – 18:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

Writing an academic text can be a daunting and complex task requiring knowledge of a range of accepted writing conventions as well as the ability to construct sentences that are idiomatically and grammatically correct. This course aims to highlight a range of important components in the writing process through analysis and practice using authentic academic texts. Topics covered include: academic style (formality, impersonal and objective language, passive voice, caution, nominalisation); structure at sentence, paragraph and document level; reporting verbs and their forms; coherence and cohesion.

Requirement of students: Please supply at least one week before the workshop begins an abstract or proposal for your research project, or a similar extract of academic text that you have written.

You have to register for the 9th International Research Workshop to participate in this course.

Case Study Research

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Kamil Marcinkiewicz (University of Hamburg)

Date: Tuesday, 29/09/15 (14:30 – 18:00) – Wednesday, 30/09/15 (09:00 – 18:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

The case study research is frequently applied in the social sciences. It is particularly popular among political scientists, especially those specializing in area studies. The ubiquity of the case study research contrasts with the scarcity of theoretical reflection on its core methodological aspects. Also the benefits of comparative analyses are often underestimated. In this course participants will have an opportunity to learn more about what the case study research is, what are its weakness and strengths and how should we go about the core question in designing a case study: selection of cases. The course combines lectures with practical exercises and discussion of students’ projects.

Recommended literature and pre-readings:

  • Gerring, J. (2007). Case Study Research: Principles and Practices (pp. 17-63). Cambridge: Cambridge University Press.
  • George, A. L., & Bennett, A. (2005). Case Studies and Theory Development in the Social Sciences (pp. 1-34). Cambridge, MA: MIT Press.
  • Rueschemeyer, D. (2003). Can One or a Few Cases Yield Theoretical Gains? In J. Mahoney and D. Rueschemeyer (Eds.), Comparative Historical Analysis in the Social Sciences (pp. 305-337) Cambridge: Cambridge University Press.
  • Hall, P.A. (2008). Systematic Process Analysis: When and How to Use it. European Political Science, 7(3), 304-317.

You have to register for the 9th International Research Workshop to participate in this course.

Data Analysis with Stata

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Tobias Gramlich (GESIS – Leibniz Institute of Social Sciences)

Date: Monday, 28/09/15 (09:00 – 18:00) – Tuesday, 29/09/15 (09:00 – 12:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

Stata is a statistical program package widely used (not only) in the social and economical sciences; it is used for data management, statistical graphics and analysis of quantitative data. Statistical concepts will not be part of the course, so participants should have some very basic knowledge of statistics. The course should enable participants to prepare their data for analysis, perform adequate analysis using a statistical computer program and to document these tasks to keep them reproducible.

For Beginners with no or very little Stata knowledge!

Course topics cover:

  • “What You Type is What You Get”: Basic stata Command syntax
  • Getting (and Understanding) Help within stata: stata Bulit-in Help System
  • Basic Data Management: Load and Save stata Datasets, Generate and Manipulate Variables, Describe and Label Data and Variables, Perform Basic uni- and bivariate Analyses, Change the Structure of your Data
  • Basic stata Graphics: Scatterplot, Histogram, Bar Chart
  • Working with “Do-” and “Log-” Files

You have to register for the 9th International Research Workshop to participate in this course.

Qualitative Comparative Analysis (QCA)

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Jonas Buche, (Goethe-University Frankfurt)

Date: Thursday, 01/10/15 (09:30 – 18:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

Since the publication of the seminal work “The Comparative Method” by Charles Ragin in 1987, set-theoretic methods and especially Qualitative Comparative Analysis (QCA) have become a common research strategy in the social sciences. Set-theoretic methods analyze cases with regard to the identification of sufficient and necessary conditions and assume causal relationships to be equifinal, conjunctural and asymmetric. Not least since so-called fuzzy sets have been introduced to the method, there has been a rising interest in QCA as a welcome alternative to both small-n case studies and large-n statistical analyses. In short, QCA is recommended if ‘if…then’ hypotheses are analyzed; if the goal is to derive sufficient and necessary conditions; if a comparison is planned; and if there is a mid-sized number of cases (between 10 and 60+).

The course starts off from an introduction into the basics of QCA (sets, set memberships, set operations). Through the notion of necessary and sufficient conditions and of truth tables, the single elements are built into the Truth Table Algorithm. However, this algorithm is not free of problems. Therefore, some pitfalls and strategies how to overcome them are presented.

  1. The course is both conceptually and technically oriented. No prior knowledge is required.
  2. We will use the software fsQCA2.5 which can be downloaded at www.fsqca.com. Please note that the software does not operate on Apple Products!

Recommended literature and pre-readings:

  • Schneider, Carsten Q. and Claudius Wagemann (2012), Set-Theoretic Methods for the Social Sciences. Cambridge: Cambridge University Press.
  • Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press.
  • Freitag, Markus, and Raphaela Schlicht. 2009. “Educational Federalism in Germany: Foundations of Social Inequality in Education.” Governance 22 (1): 47-72.
  • Emmenegger, Patrick. 2011. “Job Security Regulations in Western Democracies: A Fuzzy Set Analysis.” European Journal of Political Research 50 (3): 336-64.

You have to register for the 9th International Research Workshop to participate in this course.

Structural Equation Modeling (SEM) with R

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Holger Steinmetz (University of Paderborn)

Date: Tuesday, 29/09/15 (14:30 – 18:00) – Wednesday, 30/09/15 (09:00 – 18:00)

Max. number of participants: 25

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

Structural equation models (SEMs) have become a powerful tool in the behavioral sciences to test hypotheses about relationships between variables and implications of causal structures. This workshop offers an introduction to the background, principles, opportunities, and limitations of SEMs. These issues are illustrated using the lavaan package (latent variable analysis) that is run within the free software platform R. Lavaan has recently become a serious competitor to commercial software packages and is delivers almost everything a user needs to perform SEM. Participation to the course requires some basic knowledge of regression analysis, variances, covariances of variables, and inferential statistics. Knowledge of R is not necessary.

Course topics cover:

  • A short treatment of causality (the counter factual approach) and introduction to causal models and their illustration with path diagrams / causal graphs.
  • The principle behind estimating parameters and basis for evaluation the adequacy of the model (e.g., chi-square test) including Wright’s path tracing rules and Pearls d-separation.
  • Treatment and modeling of latent variables and the connection to theoretical constructs.
  • Explanation of the lavaan syntax and exercises (modeling own data / models of the participants is appreciated).
  • Reasons for misfitting models, evaluation, diagnostics, and re-specification.
  • The problem of endogeneity and the valuable role of instrumental variables in SEMs.

Required packages to be installed:

  • psych
  • car
  • Hmisc
  • MASS
  • QuantPsyc
  • Boot
  • Mnormt
  • Pbivnorm
  • quadprog
  • simsem
  • lavaan

Prerequisites for attending:

  • Basic knowledge of statistics (variance, co-variance) and regression analysis.

You have to register for the 9th International Research Workshop to participate in this course.

Data Analysis with R

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Michael Großbach (Hanover University of Music, Drama and Media)

Date: Monday, 28/09/15 (09:00 – 18:00) – Tuesday, 29/09/15 (09:00  – 12:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

Data analysis is one of the key skills for quantitative researchers. But data analysis is more than just your Stats 101 course in grad school. And it’s not only more I argue, it’s different. Data are not normal, there are outliers and missing values. Data often do not comply with our hypotheses. And yet we can learn from data, given the appropriate tools.

This course introduces the interactive and programmable statistical and graphics software environment R (http://www.r-project.org/), and the Integrated Development Environment RStudio (http://www.rstudio.com/) that provide a polished interface to R. The main topics will be reading data into R, exploratory data analysis – i.e. graphically scrutinising data -, data munging and, finally statistical analysis. Participants will build an ever-expanding knowledge of R as we go along.

Intermittently, participants will be given (anonymous) tests to allow for an evaluation of and give them feedback on their learning progress.

Prerequisites for attending:

  • A basic understanding of descriptive and (classic) inferential statistics would definitely be helpful
  • A laptop equipped with a wireless adaptor and a recent web browser

You have to register for the 9th International Research Workshop to participate in this course.

Measuring Preferences using Conjoint Analytic Methods and Advanced Compositional Approaches

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Assoc. Prof. Martin Meissner (University of Southern Denmark/Department of Environmental and Business Economics)

Date: Thursday, 01/10/15 (09:30 – 18:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

The participants of this course develop a sound understanding of the benefits of using conjoint analytic preferences measurement approaches and alternative advanced compositional approaches. Participants gain practical experience of using conjoint-analytic methods, and developed a better understanding of the value of measuring preferences.

The course starts with introducing the basic concepts behind the measurement of stated preferences, specifically focusing on conjoint analysis. The most often used approaches, i.e. traditional conjoint analysis, adaptive conjoint analysis and choice-based conjoint analysis are introduced. We deliberate on advantages and disadvantages of the approaches and also discuss advanced compositional approaches, like pairwise-comparison based preference measurement and the adaptive self-explicated approach. During the workshop we will further talk about all the important stages of designing a preference measurement study. We pay special attention to the types of research questions that conjoint analysis can answer. We also discuss the most important questions you should answer before setting up your preference measurement/conjoint study: What is the optimal choice of attributes and attribute level? What is a good experimental design? How should I design my survey design and present potential choice scenarios? How do I analyze the results?

Participants will have the opportunity to use Sawtooth Software on their own laptops and build their own conjoint analysis survey during the course. Based on this experience, participants will be able to improve the planning of their own future experiments.

Recommended literature and pre-readings:

  • Bradlow, Eric T. (2005), “Current Issues and a ‘Wish List’ for Conjoint Analysis,” Applied Stochastic Models in Business and Industry, 21 (4-5), 319-323.
  • Hauser, John R. and Vithala Rao (2003), “Conjoint Analysis, Related Modeling, and Applications,” in Advances in Marketing Research: Progress and Prospects, in Marketing Research and Modeling: Progress and Prospects, Wind, Jerry and Paul Green (eds.), New York: Springer, 141-168.

You have to register for the 9th International Research Workshop to participate in this course.

Analyzing Panel and Spatial Data

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Assoc. Prof. Nisar Ahmad & Assoc. Prof. Timo Friedel Mitze (University of Southern Denmark/Department of Border Region Studies)

Date: Tuesday, 29/09/15 (14:30 – 18:00) – Wednesday, 30/09/15 (09:00 – 18:00)

Max. number of participants: 30

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

The course is basically divided into two parts: Part 1) Analyzing panel data. Part 2) Spatial Data Analysis

Part 1): Analysis of Panel Data:

This part of the course is an introduction to the panel data analysis and it provides some insights into why we use panel data. What kinds of models are available for panel data and how do we estimate such models. It also covers some extensions to the basic panel data models and finally there will be a session where you will learn how to estimate panel data using STATA.

Prerequisite: Basic knowledge of Econometrics. OLS, GLS. Please bring your laptop computers with STATA installed on it.

Recommended literature and pre-readings:

  • Relevant Chapters in Cameron, A.C. und Trivedi, P.K. Microeconometrics: Methods and Applications, 2005, Cambridge University Press, Chapter V

Part 2): Spatial Data Analysis

In research fields such as regional science, quantitative sociology and business analysis as well as real estate, labor and health economics (to name just a few), researchers are increasingly aware of the fact that “space matters”. Thus, the goal of this workshop module is to equip participants with the basic knowledge about methods and tools currently available in “spatial statistics” and “spatial econometrics”. Besides presenting the general logic and theoretical foundations of these modeling approaches for variables with an explicit geographical context, a strong focus lies on illustrating the potential for applied work with these tools in the software package STATA. The module is structured as follows: After a brief introduction of the historical evolution of spatial data analysis, different research settings in economics and related research fields are outlined, which may call for the explicit use of spatial estimation techniques, for instance, in order to identify the importance of space-time autocorrelations and neighboring effects (spatial spillovers). Following this introduction, the concept of the spatial weighting matrix is introduced and statistical approaches to measure and visualize the degree of spatial dependence for a variable under study are presented. Moving from univariate to multivariate modeling techniques, the course then derives estimation techniques used in the field of spatial econometrics and links this theoretical knowledge with hands-on applications for different spatial datasets. Finally, to serve as an outlook on future research possibilities, state-of-the-art concepts such as spatial panel data models and spatial limited dependent variable models will be presented. Datasets and STATA ado-files will be provided ahead of the course and should be installed on the participants’ computers.

Prerequisite: Basic knowledge of Econometrics. OLS, GLS.  Please bring your laptop computers with STATA installed on it.

You have to register for the 9th International Research Workshop to participate in this course.

Introduction to the German Socio-Economic Panel Study (SOEP) and Applied Survival Analysis

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: PD Dr. Elke Holst (DIW Berlin & University of Flensburg), Andrea Schäfer, SOCIUM/Universität Bremen)

Date: Monday, 28/09/15 (09:00 – 18:00) – Tuesday, 29/09/15 (09:00 – 12:00)

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents:

The German Socio-Economic Panel (SOEP) is a wide-ranging representative longitudinal study of private households. Every year, there were nearly 15,000 households, and about 25,000 persons sampled. The data provide information on all household members, consisting of Germans living in the Old and New German States, Foreigners, and recent Immigrants to Germany. The Panel was started in 1984. Some of the many topics include household composition, occupational biographies, employment, earnings, health, integration, values, personality and satisfaction indicators. The course starts with an overview of the data structure and the research designs facilitated by longitudinal household studies that go beyond conventional surveys (household analysis, intergenerational analysis, life course research, etc.). The aim of the second part of this course is to give an introduction to the topic of survival (or time to event) analysis and use SOEP data to illustrate how to plot non-parametric estimates, test for differences between groups and how to fit a Cox’s semi-parametric proportional hazard model. General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier estimator and graph, Cox proportional hazards model and parametric models. Accordingly, we will explore the different types of censoring and truncation. Further, we explore the motivation, strength and limits of Cox’s semi-parametric proportional hazard model. Finally we will recap the basis of parametric models.

Required: intermediate statistical knowledge, basic Stata skills

Recommended literature and pre-readings:

You have to register for the 9th International Research Workshop to participate in this course.

HSU-HH: CfP Nachwuchsworkshop DStatG

Institution: Helmut-Schmidt-University Hamburg

Lecturer:
Prof. Dr. Gabriel Frahm
Prof. Dr. Karl Mösler
Prof. Dr. Yarema Okhrin
Prof. Dr. Philipp Sibbertsen
Prof. Dr. Axel Werwatz

Date: September 14-15, 2015

Place: Helmut–Schmidt-University Hamburg

Registration: For further information on the registration process see the link below.

Contents:
Der Workshop wird von der Deutschen Statistischen Gesellschaft veranstaltet. Er bietet Doktorandinnen und Doktoranden, »frischgebackenen« Doktorinnen und Doktoren sowie anderen jungen Statistikerinnen und Statistikern die Möglichkeit, ihre Forschungsarbeit in einem Vortrag vorzustellen und in einer kleinen Gruppe gemeinsam mit erfahrenen Hochschullehrern zu diskutieren.

Traditionell liegt der inhaltliche Schwerpunkt in der angewandten Statistik, insbesondere mit Anwendungen im Wirtschaftsbereich, jedoch sind auch Beiträge zur statistischen Theorie und Methodik sowie zu anderen Anwendungsbereichen willkommen.

For further information, please see this link.