Applied Quantitative Methods for Non-quantitative Doctoral Researchers (3 days + 2 optional days) - Runs annually


Course coordinator
Manuele Citi & Jan M. Bauer


Participants must be enrolled as PhD students in an institution of tertiary education. A precondition for receiving the course diploma is that the student attends the whole course (3 ECTS for 3 days and 5 ECTS for 3 days plus the 2 optional days).

Doctoral students face a range of challenges concerning empirical methods. We first survey registered students to learn more about their particular research interests and perceived skilling needs, and adapt the course to ensure instruction and practical application of appropriate quantitative research methods.


We first assess the perceived quantitative methods skills and needs of doctoral students that participate in the course through a pre-course survey. In the course, we introduce and train students in the targeted statistical tools within a pedagogic context of a general empirical method that recognizes the complementarity between qualitative and quantitative methods.

This should significantly help prepare students for the particular challenges they immediately face as well as any future methods issue that may arise in the course of a post-doctoral career that involves organizational and management research.

Course content
1. An Introduction to General Empirical Method:

History, Culture, and Science and the Role of Critical Realism for Research Insight - Classical and Statistical Heuristic Structures- Complementarity Among and Between Insight, Heuristic Structures, and the Research Field

2. Statistical Procedures of Interest (Content will vary to a degree, depending on pre-course student survey data)

a) A Session of Review and/or Remediation: Statistics as description, Statistics for inference, how these differ: the normal distribution and the Central Limit Theorem
b) Introduction of Stata (enter data, clean data, writing procedures, data preparation)
c) Estimation and explanation of statistical models (t-tests, correlation analysis, regression analyses as indicated and to the depth needed)
d) Interpretation of results and critical reflection on their validity
e) Class discussions and/or individual sessions on the application of quantitative methods to individual research questions

Teaching style

Lectures, discussions and PC lab practicum workshops. Morning lecture and discussion sessions will be followed by afternoon PC lab and/or group work. C. Tackney will provide the initial lecture on General Empirical Method. Then we, M. Citi and A. Pizzo, will work together to present specific statistics sessions in the afternoon. The intended course runs (three) five days, combining morning and afternoon sessions.

Lecture plan

Charles Tackney = CT; Manuele Citi = MC; Alice Pizzo = AP






Day 1




  11.00 - 13.00 


Session 1: An Overview of the place of quantitative studies in general empirical method  

L (2005) 

14:00 - 15.30 


Session 2: Introduction to Stata, descriptive statistics and graphs.      

M&J (2017) Ch:12.1-2.4. 

15.45 - 17.00 


Session 3: Estimation and interpretation of descriptive statistics – hands on session 


Day 2




09.00 - 10.30 


Session 4: Testing differences (e.g., t-tests) 

M&J (2017) Ch: 2.5 

10.45 - 12.15 


Session 5: Estimation and interpretation of testing differences (e.g., t-tests) – hands on session 




Session 6: Explanation, estimation and interpretation of bivariate relationships: scatterplots and correlation analysis 

A (2008) 

15.30 - 17.00 


Session 7: Data handling in Stata - hands on session 


Day 3




09.00 – 10.30 


Session 8: Regression analysis - OLS basics & bivariate 

M&J (2017) Ch: 3 

10.45 – 12.15 


Session 9: Explanation of regression analysis - OLS multivariate & moderation effects 

M&J (2017) Ch: 4, 5, 6 

13.15 – 15.00 


Session 10: Estimation and interpretation of regression analyses – hands on session 


 15.30 – 17.00 


Session 11: A Session of review and remediation + presentation of students’ projects. 


2-days add-on 


Day 4




 09.00: - 10.30 


Session 12: OLS assumptions and extended methods 

M&J (2017) Ch: 7, 9, 13 

10.45 -12.15 


Session 13: Advanced regression models - limited dependent variables (logit), multilevel analyses 

 M&J (2017) Ch: 8, 9, 10

13.15 – 15.00 


Session 14: Class discussion and/or individual sessions on the application of quantitative methods to individual research questions

15.30 – 17.00 


Session 15 Groups challenge 1: Apply methods to answer a specific research question 


Day 5




09.00 – 10.30 


Session 16: Groups challenge 2: Finish research case and present results 


10.45 – 12.15 


Session 17: Critical discussion about statistical analyses: Robustness, Biases, and Errors. 

(G 2017), (I 2014),  (A+ 2019), (W+ 2016)

13.15 – 15.00 


Session 18: Presenting findings in a publishable regression table – hands on session. Class discussions on the application of quantitative methods to individual research questions. Evaluation of the course


Learning objectives

At the end of the course, doctoral students should be able to:

  1. Know and understand the historical and cultural contexts within which contemporary research methods function.
  2. Specify the complementarities of qualitative and qualitative research within the general empirical method.
  3. Know the quantitative approaches appropriate to their specific research interests.
  4. Use statistical packages needed for their doctoral research needs.
  5. Evidence a nuanced ability to consider empirical research questions in organizational and management studies, so they may
  6. Better understand empirical literature, with a view to improving critical reading ability, in order to g. suggest appropriate quantitative methods to address any range of research questions.

Not applicable.


It is possible to choose the basic course with a duration of 3 days. An extension of 2 days is possible if you want to go beyond the absolute basics.

Additionally, we would like to offer an opportunity for participants to receive advisement on specific quantitative methods issues involving their research. A student who chooses this option would send a 10-page paper describing a concrete methodological issue s/he is dealing with, including possible approaches to solve the issue, with questions of interest or concern. The paper would have to be submitted no later than 6 weeks after the course. Feedback on the paper and specific questions presented would be provided in writing or conversation within a reasonable timeframe. Participants who wish to use this opportunity and engage in the arrangement are eligible to 1 ECTS extra

When registering, students need to decide whether to opt for 3 days, 5 days and whether to hand in a paper or not.

Start date

End date


3 + 2 (Including add-on: + 1 ECTS). When registering, please decide whether to opt for 3 days, 5 days and whether to hand in a paper or not.


Course Literature
  • (A 2008) - Acock, Alan (2008). A Gentle Introduction to Stata. College Station: Stata Press. Pp. 189-206, 211-212.
  • (A+ 2019) - Amrhein et al. (2019) Scientists rise up against statistical significance. Available at: https://www.nature.com/articles/d41586-019-00857-9
  • (G 2017) - Gelman, Andrew (2017). Ethics and Statistics: Honesty and Transparency Are Not Enough. CHANCE, 30(1).
  • (G 2018) - Gelman, Andrew (2018). The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It. Personality and Social Psychology Bulletin, 44 (1).
  • (I 2014) - Ioannidis, John (2014). How to Make More Published Research True. PLoS Medicine, 11(10).
  • (L 2005) - Lonergan, Bernard J.F. (2005). Preface, pp. 3-9, Chapter 1, Elements, pp. 27- 31, and pp. 126-139 on the complementarity of classical and statistical heuristic structures. Insight: a Study of Human Understanding. Volume 3 of the Collected Works of Bernard Lonergan, (Frederik E. Crowe and Robert M. Doran, Eds.). Toronto: University of Toronto Press.
  • (M&J 2017) - Mehmetoglu, Mehmet & Jakobsen, Tor Georg (2017). Applied Statistics using Stata – A Guide for the Social Sciences. SAGE, London
  • (W+2016) - Wicherts, Jelte M. et al. (2016) Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Frontiers in Psychology, 7: 1832.

Other readings as suggested by the doctoral student skills and interests assessment survey.

Recommended literature
Greene, W.H. (2011). Econometric Analysis, 7th edition, Prentice Hall.

Wooldridge, J.M. (2008), “Introductory Econometrics: A Modern Approach, Thomson South- Western, 4th edition.

Weiers, R. (2007), “Introduction to Business Statistics,” Cengage Learning Services

Baum, C. (2006). An introduction to modern econometrics using Stata. College Station, TX: Stata Press.

DKK 3,900 (3 days) or 6,500 (5 days) + add-on DKK 1,300 if handing in a paper. The fee covers the course, coffee, tea and lunch.

Minimum number of participants

Maximum number of participants

Copenhagen Business School
Dalgas Have 
2000 Frederiksberg
Room:  TBA

Contact information
For administrative issues please contact PhD Support: 
Nina Iversen
Tel: +45 3815 2475

Registration deadline

NOTE: Course registration is binding after the course registration deadline.  

In case we receive more registrations for the course than we have places, the registrations will be prioritized in the following order: Students from CBS departments, students from other institutions than CBS. 
Register here