Category Archives: Quantitative Methods

University of Hamburg: Introduction to Research in Closed-Loop Supply Chains

Institution: Universität Hamburg, Fakultät für Betriebswirtschaft

Course instructor: : Prof. Gilvan Souza (Kelley School of Business, Indiana University)

Date: June 16th 2017, 09:00-13:00 h  (block course)

Room: tba

Course Value: 1 LP

Teaching language: English

Registration: Please register via email to

Course Overview:
This course will provide an overview of research and tools used in closed-loop supply chain management research in operations management. A closed-loop supply chain is a supply chain with flows of products post-consumer use from consumers to retailers, manufacturers, and/or suppliers. Examples include consumer returns, and post-lease products. Emphasis will be given to strategic decision-making, such as product line extension, choice of product quality, and take-back legislation.

Course Contents:

  • An overview to closed-loop supply chains (CLSCs): types of product returns, and types of disposition decisions.
  • Examples of strategic, tactical and operational decisions in CLSCs
  • Strategic decision 1: Should an Original Equipment Manufacturer (OEM) offer a remanufactured product in its product line?
    • Monopoly pricing for a single product, and for a vertically differentiated product line under linear demand curves and constant marginal costs
    • The fundamental trade-off: Market expansion vs. cannibalization
    • Extension: non-linear demand curves
    • Competition between an OEM and a third-party remanufacturer
  • Strategic decision II: What is the optimal product quality when there is product recovery in the form of remanufacturing and/or recycling?
    • Introduction to classical quality choice models without product recovery (Mussa and Rosen, 1978)
    • Quality choice with product recovery: monopoly (Atasu and Souza 2013)
    • Quality choice with product recovery: competition between an OEM and a third-party remanufacturer (Orsdemir et al. 2014)
  • Strategic decision III: Design of optimal take-back legislation from a policy maker’s perspective, and an OEM’s response to it
    • The concept of welfare and its components: firms’ profits plus consumer surplus minus environmental impact
    • The model by Atasu and Van Wassenhove (2009)
  • Incentives and coordination in CLSCs
    • Reducing consumer returns through retailer effort (Ferguson, Guide, and Souza, 2006)
  • Overview of tactical decision making in CLSCs
    • Production planning for remanufactured products: product acquisition, grading, and disposition decisions
    • Hybrid inventory systems

Prerequisites: Background in Operations and Supply Chain Management is preferred but not absolutely necessary.

Assessment: Participation in discussion

University of Hamburg: Statistical Analysis of Big Data

Institution: Universität Hamburg, Fakultät für Betriebswirtschaft

Course instructor: Prof. Martin Spindler (UHH)

Date: Semester course, Time: T or W, 8-10am

Place: tba

Course value: 2 SWS or 4 LP

Course overview:
The main goal of this course is to give an introduction to statistical methods for the analysis of big data. Recently developed methods are discussed, in particular various methods of machine learning are presented and basic concepts for the analysis of big data are introduced. The course is based on the recent book by Efron and Hastie (2016).

Reference: Efron, B. and T. Hastie. Computer Age Statistical Inference. Cambridge University Press 2016.

Teaching language: English

Student evaluation: paper presentation/presentation of a chapter of the book

Registration: by email to

46th GESIS Spring Seminar: Causal Inference with Observational Data

Date: 06.03 – 24.03.2017

Location: GESIS Location in Cologne. For a list with hotel recommendations, information about Cologne as well as on how to get to GESIS please click here.  

Language of instruction: English


The GESIS Spring Seminar (formerly ZA Spring Seminar) has been taking place in Cologne annually for more  than 45 years. It offers three consecutive one-week courses in advanced methods  of quantitative data analysis for Social Scientists. Language of instruction is English.

Week 1 (06.-10.03.2017)

  • Causal Analysis with Panel Data: Potentials and Limitations – Prof. Dr. Michael Windzio, Jun. Prof. Dr. Marco Giesselmann (for further information and registration please click here)

Week 2 (13.-17.03.2017)

  • Structural Equation Models (SEMs)  – Prof. Kenneth Bollen, PhD with Zachary Fisher (for further information and registration please click here)

Week 3 (20.-24.03.2017

  • Potential Outcomes and Treatment Effects: Modern Methods of Causal Inference  – Prof. Dr. Ben Jann, Dr. Rudolf Farys (for further information and registration please click here)

JMP Intro & DOE/Kriging Workshop using JMP and R on 25th November at the HSU — CANCELED!

Dear Ladies and Gentlemen,

Due to unforeseen and unfortunate circumstances, the above-mentioned workshop on Kriging using JMP and R, scheduled for 25th November at the HSU, has to be canceled. We apologize for any inconvenience.

Kind regards
Volker Kraft, JMP Academic Ambassador, and Univ.-Prof. Dr. Sven Knoth, Chair of Computational Statistics, Helmut-Schmidt-Universität Hamburg


Multivariate Analysis Methods

Institution: Fakultät für Betriebswirtschaft, Universität Hamburg

Lecturer: Tammo Bijmolt (University of Groningen, Faculty of Economics and Business)

Dates: 21st November 2016, 12th December 2016, 16th January 2017, 6th February 2017 (all sessions scheduled on Mondays)

Course Value: 3 SWS or 6 LP

Course Overview:
The PhD course deals with a variety of multivariate analysis methods. The main focus of the course is rather applied: students who have successfully finished the course should be able to apply multivariate analysis methods at an advanced level in scientific research in marketing (or more general, in business). A full-day lecture will be used to explain a particular method and to learn about conducting the analyses. There will be four topics, each with a lecture and an assignment (see below).
The course is open for students from outside Hamburg, from other departments within the Business School, and junior faculty members (max. 15-20 participants). In principle, participants could sign up for all sessions / the entire course, or cherry-pick the topic(-s) that they like.

After attending the course, students should have acquired:
a) State-of-the-art knowledge of potential application of these multivariate analysis methods
b) Understanding of the methodological underpinnings of the methods
c) Practical skills to perform the analyses

Assessment and Credits:
After the session, participants will have to work on an assignment (if the participant requires formal credits), using real data, and write a short report (about 10 pages; to be graded as pass/fail) about this. Participants who attend all sessions and pass the four assignments can attain 6 LP.

Potential topics:
Topics of the PhD course will be selected based on preferences of participants. Therefore, please indicate your preferred topic(s) out of the following methods when registering for the course. Four out of seven topics will be taught in the course.

# Topic

  1. Latent class analysis / mixture modelling
  2. Hierarchical models
  3. Hidden Markov models {assuming knowledge of 1}
  4. Moderation & mediation
  5. Meta-analysis
  6. Factor analysis & principal component analysis
  7. Duration models

Assessment and Credits: After the session, participants will have to work on an assignment (if the participant requires formal credits), using real data, and write a short report (about 10 pages; to be graded as pass/fail) about this. Participants who attend all sessions and pass the four assignments can attain 6 LP.

Registration: To register for this seminar please contact Marius Johnen ( Registration is open till 16th October 2016 and is on a first come, first serve basis.

Tammo H.A. Bijmolt is Professor of Marketing Research at the Department of Marketing. From March 2009 till November 2015, he has been Director of the research school SOM, Faculty of Economics and Business Administration, University of Groningen, The Netherlands. His research interests include conceptual and methodological issues such as consumer decision making, e-commerce, advertising, retailing, loyalty programs, and meta-analysis. His publications have appeared in international, prestigious journals, among others: Journal of Marketing Research, Journal of Marketing, Journal of Consumer Research, Marketing Science, International Journal of Research in Marketing, Psychometrika, and the Journal of the Royal Statistical Society (A). His articles have won best paper awards from International Journal of Research in Marketing (2007), Journal of Interactive Marketing (2011), and European Journal of Marketing (2015). He is member of the editorial board of International Journal of Research in Marketing and International Journal of Electronic Commerce. Tammo Bijmolt is vice-president of EIASM and lectures in the EDEN programs. He has lectured in a broad range of programs at the Bachelor, Master, PhD and executive MBA level. He has been involved in several research-based consultancy projects for a variety of companies including MetrixLab, GfK, Wehkamp, and Unilever. Finally, he served as expert in several legal cases involving market research projects.



Reminder/Update HSU-Doktorandenkurs: Combining Rigor and Relevance with Necessary Condition Analysis (NCA)

Institution: Helmut-Schmidt-University Hamburg

Lecturer: Jan Dul, Rotterdam School of Management, Erasmus University

Date: 20.10.2016 – 10 a.m. to 15 p.m.

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

Room: Seminarraum 0105

Language of instruction: English

Registration: Please notify Dr. Sven Hauff via email (

Necessary Condition Analysis (NCA) is a novel methodology, recently published in Organizational Research Methods (Dul, 2016). Reactions of editors and reviewers of papers that use NCA are very promising. For example, an editor of a 4-star journal said:
“From my perspective, [this NCA paper] is the most interesting paper I have handled at this journal, insofar as it really represents a new way to think about data analyses”.

How does NCA work?
NCA understands cause-effect relations in terms of “necessary but not sufficient”. It means that without the right level of the condition a certain effect cannot occur. This is independent of other causes, thus the necessary condition can be a bottleneck, critical factor, constraint, disqualifier, etc. In practice, the right level of necessary condition must be put and kept in place to avoid guaranteed failure. Other causes cannot compensate for this factor.

Whom is NCA for?
NCA is applicable to any discipline, and can provide strong results even when other analyses such as regression analysis show no or weak effects. By adding a different logic and data analysis approach, NCA adds both rigor and relevance to your theory, data analysis, and publications. NCA is a user-friendly method that requires no advanced statistical or methodological knowledge beforehand. It can be used in both quantitative research as well as in qualitative research. You can become one of the first users of NCA in your field, which makes your publication(s) extra attractive.

What will be discussed in the seminar?
The seminar consists of two parts:

  1. The first part (one hour) is open to anyone who is interested in NCA and its potential value. We will discuss the method and its applications in different management fields.
  2. Immediately afterwards, in the second part (1-3 hours depending on the number of participants) we will discuss the method in more detail. In particular we will focus on the participants’ research areas and datasets. If you are interested in a demonstration of the method on your dataset, please bring your dataset (scores of the variables) on a USB drive (e.g., excel.csv file). Normally, an NCA analysis takes less than 5 minutes to get the main results.

More information:

  • Dul, J. (2016) Necessary Condition Analysis (NCA): Logic and methodology of “necessary but not sufficient” causality, Organizational Research Methods, 19(1), 10-52.

JMP Intro & DOE / Kriging Workshop using JMP and R

Institution: Helmut-Schmidt-University Hamburg, hosted by Univ.-Prof. Dr. Sven Knoth, WiSo (

Presenter: Volker Kraft, JMP Academic Ambassador

Time: 25th November 2016, 10am – 5pm

Location: Helmut-Schmidt-Universität, Holstenhofweg 85, 22043, Hamburg,  WiSo Hörsaal 3 (or PC-Pool)

Registration: Please click here an fill out the form to register for the workshop.

Number of attendees: 25 max. (hands-on only)

10-12: Introduction to JMP, Design of Experiments and Predictive Modeling (live demo & discussion)
12-13: Lunch break – pizza session by JMP
13-17: Kriging Workshop – attendees should have JMP 13 pre-installed (see for 30 days trial license)

Target Group:
Morning: Anybody interested to see JMP 13, or to get ready for the afternoon hands-on workshop.
Afternoon: Anybody with applications that require to work with functions, often of many variables, that are costly to evaluate.
Knowledge of linear regression, statistical modeling, and stochastic processes is helpful but is not required for the workshop. Similarly, basic knowledge of R and/or JMP will be helpful for the hands-on lab component, but is not mandatory.

JMP is an easy-to-use, standalone statistics and graphics software from SAS Institute. It includes comprehensive capabilities for every academic field, and its interactive point-and-click interface and linked analyses and graphics make it ideal for research and for use in statistics courses, from the introductory to the advanced levels. JMP runs on Windows and Macintosh operating systems and also functions as an easy, point-and-click interface to SAS®, R, MATLAB and Excel.

The JMP INTRO SESSION introduces the interactive user interface of JMP. Sample applications will focus on Experimental Design and Data Modeling. Get to know about JMP academic resources and where to find help.

KRIGING (or Gaussian process regression) has proven to be of great interest when trying to approximate a costly to evaluate function in a closed form. The principle aim of the workshop is to show how to build useful surrogate models using this approach, and to make clear the assumptions that such models rely on. Furthermore, once it exists, we will show how a surrogate model can be used for optimization.

Lab sessions will use both R and JMP. The main aim of the lab is to quickly find optimal settings of a catapult numerical simulator that can fire the longest shot.

Workshop content and installation instructions for R (packages) and JMP will be shared mid of November.


Introduction to Regression Analysis

Institution: Fakultät für Betriebswirtschaft, Universität Hamburg

Course Instructor: Dr. Alexa Burmester (Universität Hamburg)

Dates, location: October 10. and 11. 2016; 09:15 – 13:45 h (block course), R. 4030/4031

Course Value: 1 SWS or 2 LP

Course Overview:
This course will give an introduction to regression analysis with Stata.
Course Contents: This course will focus on basic regression analysis. Topics include (1) Data preparation, (2) Summary statistics, (3) Model free evidence, (4) Regression analysis, (5) Check of model assumptions, (6) Nonlinear models & interaction effects, and (7) Panel data.
Individual (or two-person team, with permission) research assignments will be re-quired. Please schedule some time at Monday afternoon for the assignment. Own re-search questions and data are very welcome to be discussed in the course.

Software: Please bring a laptop with Stata 13 or newer. If applicable, you can bring your own data set of your research project.

Please also study the following text:
Backhaus, K., B. Erichson, W. Plinke und R. Weiber (2016): Multivariate Analysemethoden, 14. Auflage, Heidelberg (Kapitel 1: Regressionsanalyse)

Assessment: Assessment will be based on active participation and performance on assignments. Grading for students of University of Hamburg will be pass/fail.

Registration: Please e-mail Alexa Burmester: until 06. October 2016. (Please remember that places will be allocated in order of received registrations.)

Day 1:

  • Data preparation
  • Summary statistics
  • Model free evidence
  • Regression analysis
  • Check of model assumptions

Day 2:

  • Presentation of assignment
  • Nonlinear models & interaction effects
  • Panel data
  • Summary

5th Bamberg Statistics Conference 2016 on “Income Inequality and Poverty in Germany: Measurement, Findings and Political Interventions”, 21st to 22nd July 2016, Bamberg, Germany

Institution: Bayerisches Landesamt für Statistik/Otto-Friedrich-Universität Bamberg

Date: 21.-22. Juli 2016

Place: Aula der Universität Bamberg, Dominikanerstraße 2a, 96049 Bamberg


Researchers, practitioners and the interested public are invited to join the 5th Bamberg Statistics Conference, this year dealing with income inequality and poverty in Germany. It is organized by the University of Bamberg, the Institute for Employment Research of the German  Federal Employment Agency and the Bavarian State Office for Statistics. Please check the event website for more details.

Die Otto-Friedrich-Universität Bamberg und das Bayerische Landesamt für Statistik organisieren in Kooperation mit dem Institut für Arbeitsmarkt- und Berufsforschung am 21. und 22. Juli 2016 die StatistikTage Bamberg|Fürth. Ziel der Veranstaltungsreihe ist eine Stärkung des Austauschs zwischen amtlicher Statistik und Wissenschaft sowie weiteren Nutzergruppen amtlicher Daten. Das Thema im Jahr 2016 lautet “Einkommensungleichheit und Armut in Deutschland: Messung, Befunde und Maßnahmen”. Weitere Informationen zur Tagung finden Sie auf der Tagungswebsite sowie im Programmflyer.

Qualitative Methoden und Mixed Methods in der Managementforschung

Institution: Technische Universität Hamburg-Harburg

Zeit: 13. – 16. September 2016

Ort: Am Schwarzenberg-Campus 1 (Gebäude A), 21073 Hamburg

Ab sofort per Email unter Die Themenzuordnung zu den Teilnehmern, die sich bis 26. Februar 2016 angemeldet haben, erfolgt auf der Basis einer Prioritätenliste. Nachfolgende Anmeldungen werden durch die Referenten zugeordnet. Letzter möglicher Anmeldetag ist der 03. Juni 2016.

Teilnahmegebühr: 600 Euro pro Teilnehmer

Grundlegendes Ziel dieses Kurses ist es, den Teilnehmern Kenntnisse über den Prozess und die Methoden qualitativer Forschungsdesigns zu vermitteln und die Eignung solcher Designs für konkrete Problemstellungen der Teilnehmer zu diskutieren (Werkstatt-Prinzip).

  • Grundlagen und spezifische Merkmale qualitativer Forschung
  • die Indikation qualitativer Forschung und die Rolle der Wissenschaftstheorie
  • der qualitative Forschungsprozess und der Einfluss von Theorien
  • die Erhebung qualitativer Daten
  • die Auswertung qualitativer Daten
  • Grounded Theory, Qualitative Inhaltsanalyse
  • Gütekriterien und Geltungsbegründung qualitativer Befunde
  • Methodenintegrative Designs (Mixed Methods)

Prof. Dr. Thomas Wrona
(Institut für Strategisches & Internationales Management, Technische Universität Hamburg-Harburg;

Prof. Dr. Philipp Mayring
(Institut für Psychologie der Alpen-Adria-Universität Klagenfurt;

Weitere Informationen und ein Syllabus zur Veranstaltung finden sich hier bzw. hier.