PhD Courses in Denmark

Applied Quantitative Methods for Non-quantitative Doctoral Researchers in Organization and Management Studies (3 days + 2 optional days)

PhD School in Organisation and Management Studies at CBS

Faculty
Associate Professor Manuele Citi, Department of International Economics, Government and Business, CBS

Associate Professor Charles T. Tackney, Department of Management, Society and Communication, CBS

Associate Professor Jan Michael Bauer Department of Management, Society and Communication, CBS

Course coordinator
Manuele Citi & Jan M. Bauer

Prerequisites
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.

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.

Aim
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.

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 J. Bauer, will work together to present specific statistics sessions in the afternoon. The intended course runs (three) five days, combining morning and afternoon sessions.

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

a. know and understand the historical and cultural contexts within which contemporary research methods function.

b. specify the complementarities of qualitative and qualitative research within the general empirical method.

c. know the quantitative approaches appropriate to their specific research interests.

d. use statistical packages needed for their doctoral research needs.

e. evidence a nuanced ability to consider empirical research questions in organizational and management studies, so they may

f. 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.

Lecture plan
Charles Tackney = CT; Manuele Citi = MC; Jan Michael Bauer = JMB

Time/period

Faculty

Title

  Readings

25 May

11.00 - 13.00

CT

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

L (2005)

25 May

14:00 - 15.30

MC

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

M&J (2017) Ch:1; 2.1-2.4.

25 May

15.45 - 17.00

MC

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

 

26 May

09.00 - 10.30

CT

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

M&J (2017) Ch: 2.5

26 May

10.45 - 12.15

MC

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

 

26 May

13.15-15.00

MC

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

A (2008)

26 May

15.30 - 17.30

JMB

Session 7: Data handling in Stata - hands on session

 

27 May

09.00 – 10.30

JMB

Session 8: Regression analysis - OLS basics & bivariate

M&J (2017) Ch: 3

27 May

10.45 – 12.15

JMB

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

M&J (2017) Ch: 4, 5, 6

27 May

13.15 – 15.00

JMB

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

 

27 May

15.30 – 17.00

JMB

Session 11: A Session of review and remediation

 

2-days add-on

 

28 May

09.00: - 10.30

JMB

Session 12: OLS assumptions and extended methods

M&J (2017) Ch: 7, 9, 13

28 May

10.45 -12.15

CT, JMB, MC

Session 13: Class discussion and/or individual sessions on the application of quantitative methods to individual research questions

 

28 May

13.15 – 15.00

JMB

Session 14: Advanced regression models - limited dependent variables (logit), multilevel analyses

M&J (2017) Ch: 8, 9, 10

28 May

15.30 – 17.00

JMB, MC

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

 

29 May

09.00 – 10.30

JMB, MC

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

 

29 May

10.45 – 12.15

JMB

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

(G 2017), (G 2018),
(I 2014),
(A+ 2019), (W+ 2019)

29 May

13.15 – 15.00

CT, JMB, MC

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

 

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+ 2019) - Wasserstein, Ronald L., Schirm, Allen L.  & Lazar, Nicole A. (2019) Moving to a World Beyond “p < 0.05”, The American Statistician, 73:sup1, 1-19, 

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.