Applied Quantitative Methods for Management Research - 2022

Course Syllabus

Course coordinator: Tünde Cserpes

Lecturer: 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. Click on the “schedule time with me” button on my website.


  • Week 40 (October 4, Friday): 9:00-15:00
  • Week 41 (October 11, Tuesday): 9:00-15:00
  • Week 42: Fall break
  • Week 43 (October 25, Tuesday): 9:00-15:00
  • Week 44 (November 1, Tuesday): 9:00-15:00
  • Week 45 (November 8, Tuesday): 9:00-15:00
  • Week 46 (November 15, Tuesday): 9:00-15:00
  • Week 47 (November 22, Tuesday): 9:00-15:00
  • Week 48 (November 28, Monday): 9:00-15:00

Place: Fuglesangs Allé 4, Aarhus V


  • Week 40 (October 4, Friday): 2636-U113
  • Week 41 (October 11, Tuesday): 2636-U113
  • Week 42: Fall break
  • Week 43 (October 25, Tuesday):
    NB! 9-12: room 2636-U117, 12-15: room 2627-129
  • Week 44 (November 1, Tuesday): 2636-U214
  • Week 45 (November 8, Tuesday): 2636-U214
  • Week 46 (November 15, Tuesday): 2636-U214
  • Week 47 (November 22, Tuesday): 2636-U214
  • Week 48 (November 28, Monday): 2636-U117

Course description
What does it mean when seminar participants or journal referees claim that your manuscript has an endogeneity problem or a failed identification strategy? How do you create a research design which allows you to make causal claims in a paper? With a point of departure in counterfactual thinking, this Ph.D. course introduces a toolbox of quantitative analytical techniques to better ground causal claims in academic texts.

This course will not resemble the statistics courses you have taken in the past. The course covers two perspectives that take different approaches to establish causality: design-based quasi-experimental methods and structural equation modeling. Instead of focusing on proofs and equations, we use directed acyclic graphs to visualize the assumptions underlying our research questions. We learn the methods by reading about the underlying intuition behind them, seeing them work in published empirical articles, and replicating them using software packages. 

As the tradition of counterfactual thinking has its roots in econometrics, the textbook and several of the assigned papers will also come from this field. But we will not use these texts as economists, we will use the teaching materials to discuss issues that pertain to common research practices in management academia.

The goal of this class is to de-mystify the concepts and skills involved in quantitative analysis, to spark your interest in designing your own study, and to facilitate that you become an engaged and collaborative contributor to the academic community.

Learning outcomes
This class arms you with a quantitative toolkit to understand the intuition behind the different analytical techniques.

1. Knowledge

  • identify and implement the following methods: randomized controlled trials (RCTs), regression adjustment/matching, instrumental variables (IV), regression discontinuity design (RDD), differences-in-differences (DiD, and structural equation modeling (SEM)
  • explain and apply concepts such as identification, the ladder of causality, selection, directed acyclic graphs

2. Skills

  • interpret tables and figures in academic papers
  • become a better peer-reviewer of academic papers
  • replicate published research findings with statistical software

3. Competences

  • engage with colleagues who use counterfactual methods
  • apply counterfactual methods to their own work

While the course does not emphasize formulas or equations, the covered material assumes that you already have a basic understanding of probability theory and inferential statistics.

A list of books if you aim to brush 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
3. Attendance & active participation
4. Final assignment

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

A note on pre-class reading
It is a matter of professional courtesy that you come to class prepared. You will have to read approximately 50-100 pages per week, a chapter from one of the core books, and a published empirical article. Depending on your level of familiarity with reading quantitative texts, pre-class preparation time will vary from person to person. However, I suggest budgeting at least 3-4 hours before each class. I will also provide a list of discussion questions in advance to help adjust to the heavy load.

Reading materials
There are two core books and a selection of empirical articles. I expect you to gain access to the books on your own. The empirical articles will be available on Brightspace.

Core books

  • 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 & 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. Therefore, I expect that you ask questions and contribute to the discussions.

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 early to minimize the likelihood of scheduling conflicts.

 Attendance policy

  1. The goal is that you participate in each class. Attending at least 80% of the classes is a requirement for a passing grade, mandated by the Ph.D. school.

  2. If you have to miss a class, email your instructor in advance. It is important that we maintain a written trail of our correspondence.

  3. Tünde or John will then suggest ways on how you can best make up for the missed class. In all cases, we will at a minimum ask that you do the readings, complete the in-class assignments and the data exercises. In some cases, we will also ask you to hand in a written assignment.

4. Final assignment

Students write a three-pager review (excluding references) of an article published in a top generalist journal. I match students to pre-selected articles with a roll of dice on the last day of class.

Pass or fail.

Learning goal
Reviewing colleagues’ work in an informal capacity (= friendly reviews) and through journal requests (= formal reviews) is an unpaid but rewarding part of the academic career. You get early exposure to new ideas and your feedback will shape the quality of the presentation of those ideas. This assignment aims to nudge you to see with a reviewer’s eye when reading scientific articles.

One outcome is that it will force you to think about your audience. What you write as a reviewer affects the author(s) of the paper! For that reason, focusing on a few key issues and how you frame your criticism is key.

Another outcome is that this exercise might prompt you to seek out opportunities as a reviewer through your adviser and at conferences.

Final exam

Deadline: December 13 via Brightspace.

Application/intent to register
The deadline to register is September 5, 2022, via email to Lisbeth Widahl Christensen ( The application form is available under this link. Note that registration is binding.

The course is free of charge for Ph.D. students from Aarhus University. Participants from outside Aarhus University will have to pay a fee to cover lunch and refreshments.

We order vegetarian lunch for all attendees by default. If you prefer vegan or meat options and, if you need to report food allergies, write an email to Lisbeth Widahl Christensen before the course starts.

Important! Licenses renew each year on September 1, so be mindful of when you purchase the software. During the first six lectures, we use Stata 17.

MGMT Ph.D. students can order Stata using project number 10569 using the following link:
Please have Stata ready on the first day of class.

During the last two lectures, we use SPSS 27 and AMOS 27.

AMOS only works on Windows machines. Mac users should only buy the software if they already have Windows installed as another operating system on their computer. If they would rather opt-out, they can borrow a PC from BSS IT with AMOS installed for the two weeks when we use the program. Contact BSS IT with further software-related questions:

We ask that Windows users purchase SPSS 27 and AMOS 27 through the IT webshop. The Department of Management will reimburse the expense (MGMT Ph.D. students).

Availability to course participants
If you have any questions, worries, or constructive feedback, contact me at or swing by my office.

Lecture plan
CB: Carsten Bergenholtz, TC: Tünde Cserpes, JT: John Thøgersen

Note that the reading list is subject to change. Two weeks before the course starts, I circulate the final version of the lecture plan and upload papers to Brightspace.


Week 40:

Oct 4 9.00-15.00

Room: 2636-U113

Instructor: TC

Introduction to the counterfactual framework and Stata


  • Angrist, J. and J.S. Pischke. (2009) “Questions about questions.” Pp. 3-9 in Mostly

Harmless Econometrics: An Empiricist’s Companion.

  • Sørensen, J. (2012). Note on endogeneity.
  • Maula M. and W. Stam. (2019). “Enhancing rigor in quantitative entrepreneurship research.” Entrepreneurship Theory and Practice, 44, 1-32.
  • Optional: watch three Mastering ‘metrics videos (20 minutes)

Week 41:

Oct 11 9.00-15.00

Room: 2636-U113

Instructor: TC



  • Mastering ‘Metrics, Chapter 1
  • Bertrand, M. and S. Mullainathan. (2004). “Are Emily and Greg More Employable Than Lakisha and Jamal?” The American Economic Review, 94, 991-1013. [Reading guide available on Brightspace]
  • Optional: watch three Mastering ‘metrics video on RCTs (10 minutes)

Week 43:

Oct 25 9.00-15.00

Room: 2636-U117 /12:00-15:00: room 2627-129

Instructor: CB, TC

Regression and matching


  • Mastering ‘Metrics, Chapter 2 (disregard the Appendix)
  • Eriksen L. M., H.S. Nielsen & M. Simonsen. (2014). “Bullying in Elementary School”. Journal of Human Resources, 49, 839-871. (Read up to page 852. Disregard section V and section VI and the discussions of their instrumental variables strategy.) [Reading guide available on Brightspace]
  • Optional: Keele, L., R. T. Stevenson, & F. Elwert (2020). “The causal interpretation of estimated associations in regression models.” Political Science Research and Methods. 8, 1-13.

Week 44:

Nov 1 9.00-15.00

Room: 2636-U214

Instructor: TC

Instrumental variables


  • Mastering ‘Metrics, Chapter 3 (skip the Appendix)
  • Eriksen L. M., H.S. Nielsen & M. Simonsen. (2014). “Bullying in Elementary School”. Journal of Human Resources, 49, 839-871. (Reread the article and pay special attention to Sections V and VI)
  • Dinkelman, T. (2011). “The Effects of Rural Electrification on Employment: New

Evidence from South Africa”. The American Economic Review. 101, 3078-3108. (Read from 3078-3098 up to and including Section V.B. Focus on Tables 1-5.)

  • Optional: watch three Mastering ‘metrics video on IVs (13 minutes)

Week 45:

Nov 8 9.00-15.00

Room: 2636-U214

Instructor: TC

Regression discontinuity


  • Mastering ‘Metrics, Chapter 4
  • Reimers, I. (2019). “Copyright and Generic Entry in Book Publishing”. The American Economic Journal: Microeconomics. (11):257-284. (Read Sections 1 to 4.) [Reading guide available on Brightspace]

Week 46:

Nov 15 9.00-15.00

Room: 2636-U214

Instructor: TC

Differences in differences


  • Mastering ‘Metrics, Chapter 5 (disregard the Appendix)
  • Card D. and A.B. Krueger. (1994). “Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania.” The American Economic Review. 84, 772-793. [Reading guide available on Brightspace]

Week 47:

Nov 22 9.15-15.00

Room: 2636-U214

Instructor: JT

Structural equation modeling, the basics


  • Introduction to Structural Equation Modeling, Chapters 1-7
  • Read (at least) one of these articles:
  • Bagozzi & Yi, (2012) “Specification, evaluation and interpretation of structural equation models,” JAMS
  • Davcik, (2014) “The use and misuse of structural equation modeling in management research: A review and critique,” JAMR

See also:

  • Gaskination's StatWiki (and the accompanying YouTube videos):

  • List of AMOS fit indices

Week 48:

Nov 28 9.00-15.00

Room: 2636-U117

Instructor: JT

Structural equation modeling, multi-group and cross-lagged panel analysis


  • Introduction to Structural Equation Modeling, Chapters 8-9
  • Thøgersen & Ölander, (2012) “Human values and the emergence of a sustainable consumption pattern: A panel study”, J. of Economic Psychology