Category Archives: Quantitative Methods

R-Kurs an der Helmut-Schmidt-Universität

Institution: Helmut-Schmidt-University Hamburg

Lecturer: Prof. Dr. Torben Kuhlenkasper

Date: 03.-07.08.2015

Place: Helmut-Schmidt-Universität, Holstenhofweg 85, 22043 Hamburg

Language of instruction: German

Registration: Please mail to Vera Jahn

Contents:
In der Woche vom 03. bis 07.08.2015 wird Herr Prof. Dr. Torben Kuhlenkasper, Professor für quantitative Methoden an der Hochschule Pforzheim, bei uns einen Blockkurs zur Einführung in die Statistik-Software R geben. Dabei handelt es sich um eine von Statistikern, aber zunehmend auch von Volkswirten verwendete Statistiksoftware, die kostenlos verwendet werden kann und extrem leistungsfähig ist.

Wir freuen uns daher sehr, dass wir mit Torben Kuhlenkasper für diesen Kurs einen sehr kompetenten Dozenten gewinnen konnten, der in die Geheimnisse von R einführen wird. Er hat den Kurs in den letzten Jahren bereits mit großem Erfolg an unserer Fakultät gehalten.

Der Kurs wird ganztägig im Seminarraum 0206 stattfinden. Teilnehmer werden gebeten, ihren eigenen Laptop mitzubringen. Das Kursprogramm inklusive Zeitplan ist beigefügt.

Weitere Informationen zum Kurs finden sich hier.

Interessenten werden gebeten, sich bis zum 30.07.2015 per Mail bei Vera Jahn anzumelden.

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.

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.

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.

Universität Hamburg-Workshop: Categorical Data Analysis (22.06. – 25.06.2015)

Institution: University of Hamburg

Lecturer: Shawna N. Smith, Ph.D. (University of Michigan, USA)

Date: June 22-25, 2015

Place: University of Hamburg

Language of instruction: English

Registration: For further information on the registration process, please see this link.

Contents: Many variables of interest to social, political and behavioral scientists are non-continuous, either through nature or through measurement. Outcomes like vote choice, social class, condom use, and/or number of Facebook friends necessarily violate key assumptions of the simple linear regression framework and require other model estimation strategies. Although advances in software have made estimation of these models trivial, model non-linearities make post-estimation interpretation difficult and require investigators to make choices about which aspects of the data space best represent underlying social dynamics.

For further information, please see this link.

GIGA-Workshop: Triangulation of Qualitative and Quantitative Research (28.-29.05.2015)

Institution: GIGA Doctoral Programme

Lecturer: Prof. Nigel Fielding PhD, Surrey

Date: May 28-29, 2015

Place: GIGA German Institute of Global and Area Studies, Neuer Jungfernstieg 21 in Hamburg

Language of instruction: English

Registration: Participants need to register until May 10 via the GIGA website.

Contents: The workshop aims to introduce students to the dynamic development of social science approaches to the inter-relation and integration of qualitative and quantitative research. Tracing the movement from the classic formulations of triangulation for convergent validation to the contemporary approach of triangulation for analytic density, the workshop will feature a range of research examples and extended exemplars of triangulation in practice. It will also feature the role of information technologies in supporting and facilitating mixed methods research.

Further information

GIGA-Workshop: Event History Analysis (07.-08.05.2015)

Institution: GIGA Doctoral Programme

Lecturer: Dr. Aya Kachi, Zürich

Date: May 7-8, 2015

Place: GIGA German Institute of Global and Area Studies, Neuer Jungfernstieg 21 in Hamburg

Language of instruction: English

Registration: Participants need to register until April 10 via the GIGA website.

Contents: Event history analysis—some people call it “duration analysis” or “survival analysis”— is a class of statistical methods that is becoming increasingly popular in the social sciences. In many situations in the social sciences, we are interested in analyzing the occurrence and timing of events. Some dictatorships are terminated more quickly than others. Some government coalitions or international agreements collapse sooner than others. Similarly some treaty negotiations and conflicts last longer than others. Some countries adopt new regulations much before other countries. In these political processes, we are often interested in identifying whether and to what extent various political economic factors determine the timing of events. The simplest way to analyze such a relationship is to look at correlations between the duration of a certain political state (e.g. a regime being authoritarian) and a number of structural factors that are suspected to determine the duration (e.g. the level of economic development).

Further information