Quantitative Methods
Lecture Notes
1 Preface
1.1 About the notes
- These notes aims to support my lecture at the HS Fresenius but are incomplete and no substitute for taking actively part in class.
- A pdf version of these notes is available here
- I appreciate you reading it, and I appreciate any comments.
- This is work in progress so please check for updates regularly.
- Do not distribute without permission.
- For making an appointment, you can use the online tool that you find on my private homepage: https://hubchev.github.io/
1.2 About the author
I am a Professor of International Economics and Data Science at HS Fresenius, holding a Diploma in Economics from the University of Regensburg and a Doctoral Degree (summa cum laude) from the University of Trier. I completed postgraduate studies at the Interdisciplinary Graduate Center of Excellence at the Institute for Labor Law and Industrial Relations in the European Union (IAAEU) in Trier. Prior to my current position, I worked as a research assistant to Prof. Dr. Dr. h.c. Joachim Möller at the University of Regensburg, a post-doc at the Leibniz Institute for East and Southeast European Studies (IOS) in Regensburg, and a freelancer at Charles University in Prague.
Throughout my career, I have also worked as a lecturer at various institutions, including the TU Munich, the University of Regensburg, Saarland University, and the Universities of Applied Sciences in Frankfurt and Augsburg. Additionally, I have had the opportunity to teach abroad for the University of Cordoba in Spain, the University of Perugia in Italy, and the Petra Christian University in Surabaya, Indonesia. My published work can be found in international journals such as the Canadian Journal of Economics and the Stata Journal. For more information on my work, please visit my private homepage at hubchev.github.io.
Prof. Dr. Stephan Huber
Hochschule Fresenius für Wirtschaft & Medien GmbH
Im MediaPark 4c
50670 Cologne
Office: 4e OG-3
Telefon: +49 221 973199-523
Mail: stephan.huber@hs-fresenius.de
Private homepage: www.hubchev.github.io
Github: https://github.com/hubchev
1.3 About the course Quantitative Methods for Business
Workload of the course Quantitative & Qualitative Methods for Business
125 h = 56 h (in-class) + 21 h (guided private study hours) - 48 h (private self-study).
Workload of Quantitative Methods
62.5 h = 28 h (in-class) + 10.5 h (guided private study hours) - 24 h (private self-study).
Assessment
Students complete this module with a written exam of 120 minutes where 50% of the points stem from M-IBS 8.1 Quantitative Methods and 50% from M-IBS 8.2 Qualitative Methods. A passing grade in this module is achieved when the overall grade is greater than or equal to 4.0.
Learning outcomes:
After successful completion of the module, students are able to:
- assess and discuss coherent research paradigms, based on quantitative, qualitative, and mixed-methods research approaches,
- explain a broad set of quantitative and qualitative methods to collect, gather, illustrate, analyze, and interpret data,
- distinguish and discuss empirical strategies to identify causal mechanisms, causes, and effects.
How to prepare for the exam:
I am convinced that reading the lecture notes, preparing for class, taking actively part in class, and trying to solve the exercises without going straight to the solutions is the best method for students to
- maximize leisure time and minimize the time needed to prepare for the exam, respectively,
- getting long-term benefits out of the course,
- improve grades, and
- have more fun during lecture hours.
Literature:
Cunningham (2021), Huntington-Klein (2022), Illowsky & Dean (2018), Békés & Kézdi (2021), Paldam (2021)
Content:
Research design
- Research design
- How to measure socio-economical reality
- How to identify causes of effects
- How to identify effects of causes
- The selection problem and ways to solve it (matching, natural experiments, laboratory experiments)
Statistical toolbox
- Types of data (cross-section, panel, time-series, georeferenced)
- Types of variables (continuous, count, ordinal, categorical, qualitative)
- Data sampling methods
- Descriptive methods (data visualization, statistical moments, correlation)
- Methods of statistical inference (distribution, statistical tests)
- Mathematical and statistical software packages (R, Stata, SPSS, Excel, WolframAlpha, etc.)
Methods
- Data mining (graphical visualizations, cluster analysis, factor analysis)
- Regression analysis (matching, instrument variables, difference in difference, fixed effects, regression discontinuity)
- Other methods (time series analysis, spatial analysis, simulations, qualitative comparative analysis, etc.)
About how to learn (and prepare for the exam)
Richard P. Feynman (1918-1988) was a team leader at the Manhatten Project (see Figure 1.2) and won the Nobel Prize in 1965 in physics. He once said
“I don’t know what’s the matter with people: they don’t learn by understanding; they learn by some other way – by rote, or something. Their knowledge is so fragile!”* (Feynman, 1985)
I agree with Feynman: The key to learning is understanding. However, I believe that there is no understanding without practice, that is, solving problems and exercises by yourself with a pencil and a blank sheet of paper without knowing the solution in advance.
- Attend lectures and and take the opportunity to ask questions and actively participate in class.
- Study the lecture notes and work on the exercises.
- Review the material regularly each week. Learning in small increments is more effective than last-minute cramming.
- Test yourself with past exams that you find in Appendix A.
- If you have the opportunity to form a study group, make use of it. It is great to help each other, and it is very motivating to see that everyone has problems sometimes.
- If you have difficulties with some exercises and the solutions shown do not solve your problem, ask a classmate or contact me.
1.4 Personal note
Dear students,
If the title of this course “Quantitative & Qualitative Methods for Business” seems uninteresting to you, I assure you that it is actually quite exciting because it focuses on how we can use information to understand how the world and business works and how to interpret facts. The course will enhance your data literacy, help you think critically, and improve your personal decision-making skills.
One way we can do this is by understanding the differences between quantitative and qualitative data and how they can be used to inform our choices.
Quantitative data is information that can be measured, such as numbers and statistics, while qualitative data is information that cannot be measured and is often expressed in words or other non-numerical forms.
Both forms of information are crucial for making good decisions. Without sufficient information, it can be difficult to evaluate the options and potential outcomes of a decision, leading to poor or uninformed choices. In general, the more information a decision-maker has and the faster and better the information can be used, the better they will be to make a sound decision.
The methods we discuss in this course will help you systematically gather information and make sense of it.
Enjoy the course!