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
Figure 1.1: Prof. Dr. Stephan Huber
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.
Contact:
Prof. Dr. Stephan Huber
Hochschule Fresenius für Wirtschaft & Medien GmbH
Im MediaPark 4c
50670 Cologne
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.
Literature:
In the lecture I frequently refer to Cunningham (2021), Huntington-Klein (2022), Illowsky & Dean (2018), Békés & Kézdi (2021), and Paldam (2021).
Cunningham, S. (2021). Causal inference: The mixtape. Accessed January 30, 2023; Yale University Press. https://mixtape.scunning.com/
Huntington-Klein, N. (2022). The effect: An introduction to research design and causality. Accessed January 30, 2023; CRC Press. https://theeffectbook.net
Illowsky, B., & Dean, S. (2018). Introductory statistics. Openstax.
Békés, G., & Kézdi, G. (2021). Data analysis for business, economics, and policy. Cambridge University Press.
Paldam, M. (2021). Methods used in economic research: An empirical study of trends and levels. Economics, 15(1), 28–42.
Bergstrom, C. T., & West, J. D. (2021). Calling bullshit: The art of skepticism in a data-driven world. Penguin Books.
Chivers, T., & Chivers, D. (2021). How to read numbers: A guide to statistics in the news (and knowing when to trust them). Weidenfeld & Nicolson.
Harford, T. (2020). How to make the world add up: Ten rules for thinking differently about numbers. The Bridge Street Press.
Huff, D. (1954). How to lie with statistics. WW Norton & company.
Jones, B. (2020). Avoiding data pitfalls: How to steer clear of common blunders when working with data and presenting analysis and visualizations. John Wiley & Sons.
Spiegelhalter, D. (2019). The art of statistics: Learning from data. Penguin UK.
Above that, there are tons of books around that are both insightful and entertaining and support the lecture. In Figure 1.2, I present a short list of books I recommend: Bergstrom & West (2021), Chivers & Chivers (2021), Cunningham (2021), Harford (2020), Huff (1954), Huntington-Klein (2022), Jones (2020), and Spiegelhalter (2019).
Figure 1.2: Books for data literacy
Content:
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)
Richard P. Feynman (1918-1988) was a team leader at the Manhatten Project (see Figure 1.3) 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)
Feynman, R. P. (1985). Surely you’re joking, Mr. Feynman!: Adventures of a curious character. W.W. Norton.
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.
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.
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.
