Empirical research methods

Empirical Research in Management and Economics

(WI000258 VO/UE)

Winter term 2025/2026

When

Lecture: Mondays 4:45–6:15 pm

Exercise: Tuesdays 8:00–9:30 am (Groups 1 & 2); Tuesdays 1:15–2:45 pm (Group 3); Wednesdays 4:45-6:15 pm (Group 4)

Where

Lecture: N 1179, Wilhelm-Nusselt-Hörsaal (0101.02.179)

Exercise: Group 1 (Linus Hof): Theresianum, 0601 (0506.EG.601); Group 2 (Thorsten Pachur): Karl-Max-von-Bauernfeind-Hörsaal, 2750 (0507.03.750); Group 3 (Sebastian Hellmann): 0220 (0502.EG.220); Group 4 (Rebecca West & Nuno Busch): Lothar-Rohde-Hörsaal (0101.Z1.070)

Start: 13 October 2025

Instructor

Prof. Dr. Thorsten Pachur

Chair of Behavioral Research Methods

School of Management

Technical University of Munich

Arcisstr. 21

D-80333 Munich, Germany

Website: https://www.msl.mgt.tum.de/en/brm/team/thorsten-pachur/

Team website: https://www.msl.mgt.tum.de/en/brm/home/

Email: pachur@tum.de

Office: 0505.02.534

Office hours: Please email me, and we can set up an appointment.

For exercise sessions also: Linus Hof, Rebecca West, Sebastian Hellmann, Nuno Busch

Description and Goals

This module prepares students to conduct empirical research, for example in the context of their Master’s thesis. It offers an introduction to both basic and more advanced topics in research methods and data analysis. The skills acquired in the course are not only essential for academic work but also highly relevant for students’ future professional careers and for informed participation in society. Topics covered in the module include: hypothesis development, research design, descriptive and inferential statistics, linear and logistic regression, factor and cluster analysis, conjoint analysis, and open science practices.

After successfully completing the module, students will understand the key stages of empirical research, including research design, data collection, data analysis, and interpretation. They will be able to read, evaluate, and conduct empirical studies, plan and implement their own research projects, apply a range of data-analytic techniques, and appropriately interpret the resulting findings.

Software

For the statistical analyses conducted in the exercise sessions (please bring a laptop), we will mostly be working with JASP and R. Please install the latest version of the JASP software (free!) here: https://jasp-stats.org/download/

Background Readings

Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th ed.). Oxford University Press.

Field, A., van Doorn, J., & Wagenmakers, E.-J. (2025). Discovering statistics using JASP. Sage Publications.

Goss-Sampson, M. A. (2025). Statistical analysis in JASP: A guide for students. (free pdf)

Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2023). Multivariate analysis: An application-oriented introduction (2nd ed). Springer.

Spiegelhalter, D. (2019). The art of statistics. Pelican.

Exam

Please make sure to register for the exam before the deadline (see TUMonline for details). If you miss the deadline, according to the resolution of the examination board from December 2022, it is no longer possible to register and you cannot participate at the exam. It is your duty as a student to keep an eye on the deadlines. There will be a re-take exam in the summer (usually at the end of July or first half of August; the re-take exam will be listed in TUMonline once it has been scheduled). In case you are registered for the exam but cannot participant due to illness, you can find more information here.

Syllabus

Session 1

13 October

Introduction and overview (slides) Note: Due to technical issues with the recording software (Opencast), no recording of the lecture is available. A recording from last year's lecture can be found here: video

Exercise (slides) (video/audio recording)

For Exercise I:

https://archive.nytimes.com/economix.blogs.nytimes.com/2010/06/29/do-hungry-people-take-bigger-financial-risks/

Background reading:

Tharenou, P., Donohue, R., & Cooper, B. (2007). The research process (p. 3–29). In: Management research methods. Cambridge University Press.

Session 2

20 October

Descriptive data analysis (slides) (video)

Exercise (slides) (video)

Materials

Background reading:

Spiegelhalter, D. (2019). Chapters 1–2 (p. 19–72). The art of statistics. Pelican.

Session 3

27 October

Hypothesis development and measurement (slides) (video)

Exercise (slides) (video)

Materials

Background readings:

Dienes, Z. (2008). Karl Popper and demarcation. In: Z. Dienes, Understanding psychology as a science: An introduction to scientific and statistical inference (p. 1–32). Palgrave Macmillan.

Beins, B. C. (2019). Measurement and sampling. In: B. C. Beins, Research methods (p. 127–159). Cambridge University Press.

Session 4

3 November

Inferential data analysis I (slides) (video)

Exercise (slides) (video)

Background readings:

Dienes, Z. (2008). Neyman, Pearson and hypothesis testing. In: Z. Dienes, Understanding psychology as a science: An introduction to scientific and statistical inference (p. 55–81). Palgrave Macmillan.

Howell, D. C. (2017). Sampling distributions and hypothesis testing. In: D. C. Howell, Fundamental statistics for the behavioral sciences (9th ed.) (p. 150–182). Wadsworth Cengage Learning, Belmont.

Session 5

10 November

Inferential data analysis II (slides) (video)

Exercise (slides) (video)

Materials

Background reading:

Howell, D. C. (2017). Chapters 14–17. In: D. C. Howell, Fundamental statistics for the behavioral sciences (9th ed.) (p. 351–475). Wadsworth Cengage Learning, Belmont.

Howell, D. C. (2017). Chi-square. In: D. C. Howell, Fundamental statistics for the behavioral sciences (9th ed.) (p. 495–523). Wadsworth Cengage Learning, Belmont.

Session 6

17 November

Simple regression (slides) (video)

Exercise (slides) Due to technical issues with the recording software (Opencast), no recording of the exercise is available. A recording from last year's exercise can be found here: video

Materials

Background reading:

Howell, D. C. (2017). Regression. In: D. C. Howell, Fundamental statistics for the behavioral sciences (9th ed.) (p. 226–264). Wadsworth Cengage Learning, Belmont.

Session 7

24 November

Multiple regression (slides) Due to technical issues with the recording software (Opencast), no recording of the lecture is available. Please attend the lectures in person!

Exercise (slides) (video)

Materials

Background reading:

Howell, D. C. (2017). Multiple regression. In: D. C. Howell, Fundamental statistics for the behavioral sciences (9th ed.) (p. 265–298). Wadsworth Cengage Learning, Belmont.

Field, A., van Doorn, J., Wagenmakers, E.-J. (2025). Moderation and mediation. In A. Field, J. van Doorn, & E.-J. Wagenmakers (2025). Discovering statistics using JASP (p. 395–423). Sage Publications.

Session 8

1 December

Logistic regression (slides) (video)

Exercise (slides) (video)

Materials

Background reading:

Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2021). Logistic regression. In: K. Backhaus, B. Erichson, S. Gensler, R. Weiber, & T. Weiber, Multivariate analysis: An application-oriented introduction (p. 267–354). Springer.

Session 9

8 December

Factor analysis (slides) (video)

Exercise (slides) (video)

Materials

Background reading:

Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2021). Factor analysis. In: K. Backhaus, B. Erichson, S. Gensler, R. Weiber, & T. Weiber, Multivariate analysis: An application-oriented introduction (p. 381–450). Springer.

Session 10

15 December

Cluster analysis (slides) (video)

Exercise (slides) (video)

Materials

Background reading:

Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2021). Cluster analysis. In: K. Backhaus, B. Erichson, S. Gensler, R. Weiber, & T. Weiber, Multivariate analysis: An application-oriented introduction (p. 451–530). Springer.

Session 11

12 January

Conjoint analysis (slides)

Exercise (slides)

(Please install R/RStudio before the exercise: https://www.rstudio.com/products/rstudio/download/#download)

Materials

Background reading:

Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2021). Conjoint analysis. In: K. Backhaus, B. Erichson, S. Gensler, R. Weiber, & T. Weiber, Multivariate analysis: An application-oriented introduction (p. 531–598). Springer.

Session 12

19 January

The replication crisis and open science

Exercise

For preparation, please watch this brief tutorial on how to do a preregistration: link

Materials

Background readings:

Pennington, C. R. (2023). A student's guide to open science. In: C. R. Pennington, A student's guide to open science. Using the replication crisis to reform psychology (p. 56–75). McGraw Hill, Open University Press.

Andraszewicz, S., Scheibehenne, B., Rieskamp, J., Grasman, R., Verhagen, J., & Wagenmakers, E. J. (2015). An introduction to Bayesian hypothesis testing for management research. Journal of Management, 41(2), 521–543.

Session 13

26 January

Summary, questions, and discussion

Exercise

Please attend the exercise for a feedback discussion! Thank you.

11 February

Exam

When: 2–4pm

Where: 2300, Friedrich von Thiersch Hörsaal (0503.02.300); N 1190, Hans-Heinrich-Meinke-Hörsaal (0101.02.190); 0980, Audimax (0509.EG.980); 2750, Karl Max von Bauernfeind Hörsaal (0507.03.750)

Additional Resources

Tutorials and further materials for doing data analysis with JASP: link

Mock exam

pdf

Note: The purpose of the mock exam is to give you a sense of the type of questions that you might encounter in the real exam. You should be able to work out the correct answers for all questions based on the materials of the module (lecture slides, exercises, background readings). An answer sheet for the mock exam is not provided.

Doing Bayesian data analysis

Glenberg, A. M., & Andrzejewski, M. E. (2024). Introduction to Bayesian statistics. In A. M. Glenberg & M. E. Andrzejewski, Learning from data: An Introduction to statistical reasoning using JASP (4th Ed.) (p. 497–507). Routledge

Avoiding common statistical mistakes

Makin, T. R., & Orban de Xivry, J. J. (2019). Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. ELife, 8, e48175.

On appreciating ignorance as driver of science

Schwartz, M. A. (2008). The importance of stupidity in scientific research. Journal of Cell Science, 121(11), 1771–1771.