Applied Quantitative Methods for Management Research - 2021

Course coordinator: Tünde Cserpes

Lecturers: Carsten Bergenholtz, John Thøgersen, and Tünde Cserpes

Workload: 5 ECTS

Administrative assistance: Lisbeth Widahl

Office/student hours: on Fridays between 10:00-12:00 or by appointment

Time: Tuesdays, except for 8 October and 26 November (Friday), between 9:00 - 15:00 

Week 40: 8 October 2021
Week 41: 12 October 2021
Week 42: Fall break
Week 43 : 26 October 2021
Week 44: 2 November 2021
Week 45: 9 November 2021
Week 46: 16 November 2021
Week 47: 23 November 2021
Week 47: NB! new date: 26 November 2021 (Friday) instead of 30 November in week 48.

Place: Aarhus BSS, Aarhus University; Fuglesangs Allé 4, DK-8210 Aarhus V

Room: 8 October, 2, 9, 16 and 23 November: 2628-M303, 12 and 26 October: 2636-U215, 26 November: 2636-U113.

Important! We are closely monitoring the COVID situation and will modify course policy according to the guidelines set out by the Danish authorities and university officials. 

Course description
What does it mean when seminar participants or journal referees claim that your manuscript has an endogeneity problem or no identification strategy? How can you create a research design that allows us to make causal claims in a paper? With a point of departure in counterfactual thinking, this PhD course introduces a toolbox of analytical techniques to better ground causal claims in academic texts.

The covered methods are the state-of-the-art quantitative tools in management research. We focus on visualizing the assumptions underlying specific research designs using directed acyclic graphs, and introduce several statistical techniques based broadly on two perspectives that address the limits and opportunities of making causal claims based on observational data, i.e. structural equation modeling and quasi-experimental design. We learn the methods by reading about the underlying intuition behind them, by seeing them work in academic texts, and by replicating them using software packages.

The ultimate goal of this class is to spark an interest in quantitative analysis and de-mystify the concepts and skills involved. In doing so, it will facilitate that PhD students become engaged and collaborative contributors to the academic community. 

Learning outcomes
This class provides the tools for all PhD students to understand the intuition behind the different analytical techniques. Student outcomes can be divided into competences, knowledge and task-specific skills.

1. Competences

After taking this course, all participants will be better at creating strong research designs. In particular, students will have the foundations to apply counterfactual methods to their own work, should they choose to do so. Students who do not work with quantitative approaches will acquire the foundations to engage with their colleagues who use counterfactual methods.

They will become better reviewers, conference presiders, and seminar participants.

2. Knowledge

Key concepts:

  • identification, ladder of causality, selection, directed acyclic graphs

Statistical techniques:

  • randomized controlled trials (RCTs), matching / regression adjustment, instrumental variables (IV), regression discontinuity design (RDD), differences-in-differences (DiD), structural equation modeling (SEM)

3. Task-specific skills

In general, participants will become better consumers of data analysis outputs such as tables and graphs. Their data literacy will be strengthened by in-class coding exercises. 

Prerequisites
The class assumes that you already have a basic understanding of probability theory and inferential statistics. Based on previous years’ experience, those students who take the class with limited statistical experience under their belt will have to put significantly more effort into preparing for this class. Reach out to me as soon as possible if you are in this category, so I can recommend you ways to prepare.

Here is a list of books if you are interested in brushing up on your skills:

  • Fox, J. 2009. A Mathematical Primer for Social Statistics. Newbury. Park, CA: Sage.
  • Rowntree, D. 2018. Statistics Without Tears. United Kingdom: Penguin Books.
  • Spiegelhalter, D. 2019. The Art of Statistics: How to Learn from Data. New York, NY: Basic Books. 

Course requirements
There are four main requirements to this course:

1) Pre-course survey

2) Reading and questioning

3) Attendance and active participation

4) Final exam

1) Pre-course survey
Course participants fill out a short quantitative survey before the first class to assess their level of familiarity with statistical concepts.

2) Reading
This course requires you to read both conceptual applied texts, which will sometimes seem abstract and difficult. You will have to read approximately 50-100 pages per week, a conceptual chapter from Mastering Metrics and a published application article. Depending on your level of familiarity with reading quantitative texts, reading time will vary from person to person. However, I suggest budgeting at least 3-4 hours of preparation time for each class.

I ask you to buy two books before the first day of class. Both of these are worth adding to your personal library as they are an accessible guide to the essential tools of econometric research.

I will distribute a detailed lecture plan to admitted students, which will include additional readings (mostly journal articles) for each class session. We will use Brightspace as classroom platform. 

Textbooks

  • Joshua D. Angrist and Jörn-Steffen Pischke (2014), Mastering ‘Metrics. Princeton University Press.
  • Niels Blunch (2013), Introduction to Structural Equation Modeling using IBM SPSS Statistics and Amos, Sage Publishing.

3) Attendance and active participation

Classes combine lectures, discussions, and in-class individual or group work to facilitate active learning. We will spend class time making connections between the assigned readings and empirical problems through various exercises.

Attending class is pivotal. The topics build on each other, so if you miss a day, you will have to first learn the material for that day before you can start preparing for the next one. Moreover, it is the requirement of the Ph.D. School that students attend all class sessions. Therefore, I ask you to block out class time in your calendar to minimize the likelihood that you create scheduling conflicts. 

Attendance Policy

1. Participation on all 8 days is desired. Attending at least 80% of the class is one of the requirements of a passing grade, mandated by the PhD school.  

2. If for some reason you cannot participate, send an email to Tünde and present your case. It is important that we maintain a written trail of our correspondence because requests are  evaluated on a case by case basis.  

3. Tünde will tailor a recommendation to your specific case, how you can best keep up with class materials. In all cases, we will ask that you read all materials for the session, read the instructor slides, complete in-class assignments and data exercises.  

4) Final exam
Each participant submits a written assignment. The assignments will be evaluated in terms of pass or fail. Deadline for submission is December 14, 2021. I will post assignments to Brightspace in Week 46. 

Application / intent to register
Send your application and intent to register no later than September 5, 2021 to Lisbeth Widahl, Aarhus BSS, Aarhus University, Department of Management (by email). The application form can be downloaded from the website: Application Form. Please note that your registration is binding. 

Fee
Participants from other universities than Aarhus University are asked to bear the cost of meals, including lunch and refreshments on each day of the course. If Corona-related regulations prevent us from providing meals, we will adjust our policy and communicate it to participants.

For more information regarding dietary plans and fee structure, please contact Lisbeth Widahl.

Participants who might be commuting from other places need to arrange accommodation on their own. 

Software
During the first six lectures, we will use Stata 16. The second part of the course demonstrates structural equation modeling using SPSS 27 and AMOS 27. Please buy all these statistical software packages by the first day of class. You can only expect your instructor to answer installation-related questions before the first day of class.

Aarhus BSS PhD students can buy a copy of Stata 16 IC, SPSS 27, and AMOS 27 from the BSS IT department using the following link: IT licenses (au.dk)

Please contact BSS IT with further software-related questions: bss.it@au.dk.

Important! Aarhus BSS PhD students should delay purchasing these software packages until after September 1, 2021 to enjoy a full one-year subscription. 

Availability to course participants
If you have any questions, worries, or constructive feedback, please feel free to contact me at tunde.cserpes@mgmt.au.dk. I will be available for meetings during office hours or by appointment if those times conflict with your schedule. 

Lecture Plan
I will distribute a detailed lecture plan to admitted students.