Applied Quantitative Methods for Management Research - 2025

Course coordinator: Michela Beretta

Lecturers: Michela Beretta, NN

Workload: 5 ECTS

Administrative assistance: Lisbeth Widahl

Time and place: 

Week 41, Monday – Friday, 6-10 October 2025, all days 9-16

Aarhus BSS, Aarhus University, Fuglesangs Allé 4, Aarhus V. Room: 6 October: 2628-M211; 7-8 October: 2628-M102; 9-10 October: 2610-530.

Application deadline: 2 September 2025

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How can you design a study that allows you to make credible causal claims, when randomized experiments are not feasible? How can you address concerns about endogeneity or flawed research design raised by reviewers and colleagues? This PhD course equips students with the tools and strategies to tackle these questions, focusing on the challenges of causal inference in management research. Grounded in counterfactual thinking, the course introduces a range of tools designed to uncover causal relationships, focusing on observational and quasi-experimental methods.

Course objectives:

• Understand key concepts of causality, threats to causal inference and strategies to address them in management research.
• Learn about important techniques used in management research to address challenges of casual inference and handle endogeneity issues, such as fixed-effects models, instrumental variables, matching and difference-in-differences.
• Learn how to apply some of these techniques using data from published articles.
• Critically review scientific papers to assess the validity of causal claims and strengths and weaknesses of the chosen methods.

The course progresses from understanding the core principles of causality (day 1) to exploring tools and techniques for addressing specific challenges in causal inference (days 2–5). Throughout the course, an emphasis is placed on understanding the intuition behind these techniques, seeing them applied in published empirical articles, and replicating them using software packages.

The course is designed to help students critically assess causal claims, identify appropriate techniques given the available data, and contribute effectively to academic discourse in management research.

Day 1: Intro – Research design and Causality

Day 1 sets the stage for the course by introducing the concept of causality, its importance in management research, and the key challenges faced when working with observational data. Students will explore the sources of bias and learn foundational causal frameworks like Directed Acyclic Graphs (DAGs). The students will also be introduced to RCTs as the gold standard.

• What is causality, counterfactual thinking and causal claims.

• Randomized Control Trials. What are RCTs? The gold standard for causal inference.
• Endogeneity - confounding, omitted variable bias, selection bias, common method, measurement error...
• Using Directed Acyclic Graphs (DAGs) to visualize assumptions and identify confounding
• Hands-on exercises

Day 2: Regression-Based Approaches and Limited Dependent Variable Models

Day 2 introduces regression as an adjustment strategy to mitigate confoundedness. Regression models are foundational tools for causal inference, but their use requires understanding their limitations and extensions. This day focuses on basic regression concepts and introduces methods for handling binary and count outcomes through Limited Dependent Variable (LDV) models.

• Linear regression (OLS) refresher

• Control strategies: handling confounders, adding interaction terms…
• Hands-on exercises
• Introduction to limited dependent variable (LDV) models: ex. logit and count (Poisson) models à the idea is for students to grasp the intuition behind them, especially when likely to encounter categorical or non-continuous dependent variables.

Day 3: Panel data methods (NN)

Day 3 dives into panel data techniques. Students will explore fixed and random effects models and learn when and how to use these methods.

• Introduction to panel data: cross-sectional vs. panel data
• Fixed effects models for panel data
• Random effects
• Hands-on exercises
• Lagged dependent variables and temporal relationships.

Day 4: Difference-in-Differences (DiD)

Difference-in-Differences (DiD) offers a robust quasi-experimental method to estimate causal effects. This day teaches students how to apply and test DiD models using pre/post and treated/control comparisons.

• The logic of DiD: Pre/post and treated/control groups, key assumptions
• Model extensions
• Dealing with staggered treatments, applications in management research, with practical hands-on exercises.

Day 5: Matching and Instrumental variables (NN)

Day 5 introduces advanced techniques for addressing endogeneity and simulating experimental conditions. Students will learn the intuition behind these methods and critically evaluate their applicability.

• Matching
  • The logic of matching on observable characteristics.
  • Types of matching – eg. Propensity score matching, coarsened exact matching
  • Strengths and weaknesses
  • Hands-on exercises
• Instrumental variables – the idea here is to give the main intuition behind the use of IVs, less focus on the application part
  • When and why IV is used, finding valid instruments

Assignment
Pre-course: there will be pre-readings to prepare for the course. Moreover, participants are expected to submit a short project description (max 1 page) with a focus on the method section.

Post-course: The focus of the final assignment (4-5 pages) will be on applying the course concepts and methods to their research projects and critically evaluate causal inference.

Application
Deadline for application: 2 September 2025. Please download and fill in the application form. The application form should be sent by email to: Department of Management, Aarhus BSS, Aarhus University, att. Lisbeth Widahl. Please note that your application is binding.

Fee
External participants (from outside Aarhus University) will have to pay a fee to cover lunch and refreshments. For more information, please contact Lisbeth Widahl. Participants will have to make their own arrangements regarding travel and accommodation.