Empirical research methods

Empirical Research in Management and Economics

(WI000258 VO/UE)

Winter term 2024/2025

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 (Alexandra Ortmann): 0220 (0502.EG.220); Group 4 (Sebastian Hellmann & Nuno Busch): Lothar-Rohde-Hörsaal (0101.Z1.070)

Start: 21 October 2024

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, Alexandra Ortmann, Sebastian Hellmann, Nuno Busch

Description and Goals

This module prepares students for conducting empirical research (e.g., for their Master's thesis). It provides an introduction to basic and advanced topics of research methods and data analysis. The acquired skills are also important for students in their future professional career and as citizens. The topics covered in the module include: hypothesis development; research design; descriptive and inferential data analysis; linear regression; logistic regression; factor analysis and cluster analysis; conjoint analysis; open science.

After the successful participation in the module students will be able to understand key steps of empirical research, including research design, data collection, data analysis, and interpretation. Students will learn how to understand and conduct empirical research and will be in a position to critically evaluate research findings. Students will learn the basics of how to plan, set up, and conduct an empirical research project, and learn to apply data-analytic techniques to data. Finally, students will learn to interpret the results of data analysis.

Software

For the statistical analyses conducted in the exercise sessions (please bring a laptop), we will mostly work 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., Miles, J., & Field, Z. (2012). Disovering statistics using R. 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

21 October

Introduction and overview (slides) (video)

Exercise (slides) (video)

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

28 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

4 November

Hypothesis development and research design (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

11 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

18 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

25 November

Regression analysis I (slides) (video)

Exercise (slides) (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

2 December

Regression analysis II (slides) (video)

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.

Session 8

9 December

Regression analysis III (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

16 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

13 January

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

20 January

Conjoint analysis (slides) (video)

Exercise (slides) (video)

(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

27 January

The replication crisis and open science (slides) (video)

Exercise (slides) (video)

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

3 February

Summary, questions, and discussion (slides) (video)

Exercise

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

12 February

Exam

When: 8–10am

Where: Audimax (0509.EG.980); Carl-von-Linde-Hörsaal (0502.01.200)

Important: Please note your individual room assignment!

Immatriculation numbers 03613887 - 03798503: 0980, Audimax (0509.EG.980)

Immatriculation numbers 03798531 - 03809745: 1200, Carl-von-Linde-Hörsaal (0502.01.200)

1 April

Exam review

When: 3–4pm

Where: Z534

14 August

Re-take exam

When: 8–10am

Where: 2300, Friedrich von Thiersch Hörsaal (0503.02.300)

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 types 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.