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Yet another introduction to data analysis with STATA

This small group seminar (maximum of 10 students) is a hands-on introduction to data analysis with the statistical software Stata. We expect participants to have a basic understanding of statistics. However, no prior knowledge of advanced statistics, econometrics or the specific software is required. The course is intended to familiarize students with the software, specifically data handling and simulations. We address statistical tests, OLS regression analyses and various frequently used extensions, including bootstrapping. To better understand the output of commands, we will not only apply these commands to real data but also to self-simulated data. The focus of this course is on application of methods and understanding their requirements as well as interpreting the output. During the course, participants will read texts and prepare own introductory presentations and examples of analyses; the topics and examples are then discussed and explored in the bi-weekly group meetings. Being experience-based, the course may complement but does not substitute a fully-fledged statistics or econometrics course.

Active participation and own presentations are required to pass the seminar.

The course is especially suitable for master and doctoral students without substantial prior knowledge in data analyses but with the aim to analyze empirical data in Business, Economics and Management research.

The seminar will take place on the following dates from 14:00 to 18:00 in room P08.16 (JZ):

May 7  
May 28
June 11 
June 18
June 25
July 9

For participating in this course, please apply via email before March 31 to Prof. Vivien Procher (procher{at} and to Prof. Diemo Urbig (urbig{at}, who will both jointly run this course. Your brief application should include your motivation to participate in this course. Applicants will by notified by End of April whether they are admitted to the seminar.

If all participants speak German, we may switch to German as working language, else we stick to English and consider this small group setting a good opportunity for practicing English.


Average grade in this course: ∅1,9  

(based on the participating Master/Bachelor students, no grading for doctoral students)



Cameron, A.C., & Trivedi, P.K. (2009) Microeconometrics Using Stata. Stata Press.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Bauer, T.B., Schmidt, C.M. &  Fertig, M. (2009) Empirische Wirtschaftsforschung: Eine Einführung. Springer Verlag.

David J. Sheskin (2011) Handbook of Parametric and Nonparametric Statistical Procedures. (5th ed.). Chapman and Hall/CRC. (pages 1-100).

Kohler, U. &  Kreuter, F. (2012) Datenanalyse mit Stata: Allgemeine Konzepte der Datenanalyse und ihre praktische Anwendung. Oldenbourg Wissenschaftsverlag.


Course credits

For doctoral students this course is credited as doctoral seminar of the Schumpeter School of Business and Economics. 

For Master students credits for this seminar can be given for the following modules:

MWiWi 1.4 - "Innovations- und Technologiemanagement" (Witt)

MWiWi 1.14 - "Energie- und Projektmanagement" (Witt)

MWiWi 1.8 – „Personalmanagement“ (Fallgatter)

MWiWi 2.4 - "Entrepreneurship und Wirtschaftsentwicklung" (Volkmann)

MWiWi 2.4 - "Infrastruktur und Wirtschaftsentwicklung" (Volkmann)

MWiWi 2.6 - "Industrieökonomik" (Bönte)


Work packages with responsible participants and earliest day for presentation


June, 11

1. Standard errors & Hypothesis tests (Cohen et al., 2003, pp. 86-88, Bauer et al. 2009, pp. 41-66)

2. Outlier analysis (Cohen et al. 2003, pp. 390-418, Kohler & Kreuter 2012, pp. 276-284, look at the stata lvr2plot)


June, 18

3. Correlation, beta-coefficients, and R-squared (Cohen et al., 2003, pp. 69-74, 79-89, Green – please check the corresponding pages yourselves)

4. Multicollinearity & control variables (Cohen et al. 2003, pp. 75-78, 419-430, Kraha et al. 2012, Tools to support interpreting multiple regression in the face of multicollinearity. Frontiers in Psychology. Volume 3. Article 44. Angrist & Pischke, 64-68)

5. Variable transformations (Stata script from Diemo Urbig, and Cohen et al., 2003, pp. 193-253)


June, 25

6. Dummies & Contrast Codes (Hardy, 1993, Regression with Dummy Variables. Bauer et al. 2009, pp. 244-252)

7. Interaction & Moderation (Cohen et al. 2003, pp. 255-301)


July, 9

8. Mediation (Cohen et al., 2003, pp. 69-74, papers by MacKinnon provided by Urbig)

9. Dealing with Missing values (some suggestions provided by Procher, Kohler & Kreuter 2012, pp.110-112)