Applied Quantitative Methods for Management Research - 2016

Objectives
What does it mean when seminar participants or journal referees claim that your paper has an endogeneity problem or no identification strategy? How can you deal with these challenges? With a point of departure in the ideal randomized experiments, this PhD course at the Department of Management introduces a toolbox with various techniques to deal with these questions in quantitative analysis of non-experimental data.

This course will introduce participants to state-of-the-art quantitative empirical methods in management with a focus on application and understanding the underlying intuition. This will include topics ranging from regression models to quasi-experimental designs and structural equation models. We will address the discussion of identification, correlation and causality with a point of departure in recent empirical studies from the broad field of management.

The goal is to provide participants with a foundation for collecting and analyzing datasets in management research with a focus on establishing causality. Even for students, who are not themselves working with quantitative empirics, we aim at building an understanding of the central topics, enabling them to read, understand, review and comment on quantitative empirical contributions for their thesis, at conferences and as reviewers for international journals. The ultimate goal is to spark an interest for quantitative analysis and de-mystify the concepts and skills involved.

Topics
Identification, Causality, Types of research questions, Quality of data, Matching, Differences-in-differences, Instrumental variable regression, Regression discontinuity design, Structural equation modelling.

Lectures by
Professor Michael S. Dahl and Head of Department, Professor Jacob Kjær Eskildsen.

Course coordinator
Professor Michael S. Dahl, Department of Management, Aarhus BSS, Aarhus University. Email: msd@mgmt.au.dk 


Main literature
Joshua D. Angrist and Jörn-Steffen Pischke (2014), Mastering ‘Metrics. Princeton University Press.

Joseph F. Hair, Jr., G. Tomas M. Hult, Christian M. Ringle and Marko Sarstedt (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications. (Chapter 1-6).

Please note that the complete list of readings will be provided to participants upon enrolment.

Workload
5 ECTS.

Time and place
Department of Management, Bartholins Allé 10, DK-8000 Aarhus C.
27 Sept., 4 Oct., 1 Nov., 3 Nov., 15 Nov.: Room 1325-242.
27 Oct.: Room 1483-344.
8 Nov.: Room 1483-454.

Lectures
(A complete program and list of literature will be provided upon enrolment.)

Day 1:

September 27

9.00 - 15.00

 

What is identification and why do we care in management research?

The ideal randomized experiment

 

Read: Mastering ‘Metrics, Chapter 1

MSD

Day 2:

October 4

9.00 - 15.00

Regression and matching

 

Read: Mastering ‘Metrics, Chapter 2

MSD

Day 3:

October 27

9.00 - 15.00

Instrumental variable regression

 

Read: Mastering ‘Metrics, Chapter 3

MSD

Day 4:

November 1

9.00 - 15.00

Regression discontinuity design

 

Read: Mastering ‘Metrics, Chapter 4

MSD

Day 5:

November 3

9.00 - 15.00

Differences in differences

 

Read: Mastering ‘Metrics, Chapter 5 and 6

MSD

Day 6:

November 8

9.00 - 15.00

Structural equation modeling

 

Read: A Primer on Partial Least Squares SEM, Chapter 1-6

JKE

Day 7:

November 15

9.00 - 15.00

Structural equation modeling

 

Presentation, replication and ethics (1 hour)

JKE

 

MSD

Software
The techniques introduced in the first part of this course will be demonstrated using Stata. All participants are expected to obtain and install the Stata system before the beginning of the course and be familiar with the basic operations. Aarhus BSS students should contact the IT department to obtain a Stata license (IC-version is sufficient for this course).

Structural equation modelling is demonstrated with the SmartPLS software package (www.smartpls.com). The Professional edition is required.

Prerequisites
A basic understanding of probability and statistics is required. Most master-level programs in management (including cand.merc.) have courses providing the prerequisite for participating in this course. Revisiting the text books and material from these basic classes would be recommended. Several universities are providing excellent and easy accessible online courses (so-called MOOCs, massive open online courses) in basic statistics and regression analysis, which also provide an opportunity to revisit these topics, also if you have a different background. While this course focuses on intuition and application rather than proof and formal equations, some technical content should be expected.

Exam form
Full and active participation is expected during lectures. Each participant will submit a written assignment. The assignments will be evaluated in terms of pass or fail. Deadline for submission is December 1st. Assignments will be distributed on November 1st. 

Application
Deadline: 8 August 2016 to: Lisbeth Widahl, Aarhus University, School of Business and Social Sciences, Department of Management, Bartholins Allé 10, DK-8000 Aarhus C (preferably by email: liw@mgmt.au.dk). The application form can be downloaded from the website (Application Form). Please note that your application/registration is binding.

Admission
The total number of participants is limited to 20 students. Applicants from the Department of Management (MGMT), Aarhus University will be given priority over applicants from other departments and universities. Only PhD students will be accepted.

Fee
Participants from other universities will be charged a fee that covers meals during the course (for more information, please contact Lisbeth Widahl. Participants are required to find their own accommodation.

Further information
Michael S. Dahl or Lisbeth Widahl (see contact details above).