Skip to Main Content

CBS PhD School logo

Applied Quantitative Methods for Non-quantitative Doctoral Researchers - 3+2+1 ECTS


Date and time

Monday 27 May 2024 at 11:00 to Friday 31 May 2024 at 15:00

Registration Deadline

Monday 8 April 2024 at 23:55

Location

Dalgas Have - room DHØ 1.108 (first floor), Dalgas Have 15, 2000 Frederiksberg Dalgas Have - room DHØ 1.108 (first floor)
Dalgas Have 15
2000 Frederiksberg

Applied Quantitative Methods for Non-quantitative Doctoral Researchers - 3+2+1 ECTS


Course coordinators: Manuele Citi and Jan M. Bauer, Department of Management, Society and Communication (MSC)

Faculty

Associate Professor Manuele Citi 
Department of Management, Society and Communication, CBS
 
Department of Management, Society and Communication (MSC)
 
 
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).

The course does not assume a prior knowledge of statistics and does not have pre-requisites in terms of other PhD courses. The course only assumes a basic (undergraduate level) understanding of essential mathematical concepts.

Aim

This course helps qualitative-inclined PhD students to develop some fundamental quantitative analysis skills, using a user-friendly statistical software like Stata. The course is useful either for students who want to enrich their qualitative-oriented PhD thesis with some quantitative analysis, or for students who want to have a deeper understanding of some relevant empirical literature which based on quantitative methods. The course covers all the most fundamental topics of introductory statistics, teaches the appropriate analytical techniques for different types of data, and trains students to run their own analyses in Stata, both independently and in groups.


This course teaches students how to use specific statistical tools within a general empirical method that helps them to complement qualitative approaches. This will equip students with the skills to deal with the current and future challenges of conducting social science research at the doctoral and post-doctoral level.

 

Content

  1.  An Introduction to the General Empirical Method.
  2. Types of variables and descriptive statistics
  3. Introduction to Stata (enter data, clean data, writing procedures, data preparation)
  4. Estimation and explanation of statistical models (t-tests, correlation analysis, simple linear regression, multiple regression)
  5. Interpretation of results and critical reflection on their validity 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. 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. Specify the complementarities of qualitative and qualitative research within the general empirical method.

b. Know the quantitative approaches appropriate to their specific research interests.

c. Use statistical packages needed for their doctoral research needs.

d. Evidence a nuanced ability to consider empirical research questions.

e. 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

Manuele Citi = MC; Jan M. Bauer = JMB

Time/period

Faculty

Title

  Readings

27 May

11.00 - 13.00

MC

Session 1: An overview of how quantitative studies can enrich qualitative research– introduction Group challenge 1

 

27 May

14:00 - 15.30

MC

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

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

27 May

15.45 - 17.00

MC, JMB

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

 

28 May

09.00 - 10.30

MC­

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

M&J (2017) Ch: 2.5

28 May

10.45 - 12.15

MC, JMB

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

 

28 May

13.15-15.00

JMB

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

A (2008)

28 May

15.30 - 17.30

JMB, MC

Session 7: Data handling in Stata - hands on session

 

29 May

09.00 – 10.30

JMB

Session 8: Regression analysis - OLS basics & bivariate

M&J (2017) Ch: 3

29 May

10.45 – 12.15

JMB

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

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

29 May

13.15 – 15.00

JMB, MC

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

 

29 May

15.30 – 17.00

JMB, MC

Session 11: Student presentations 1: Group challenge 1 and/or individual students’ projects.

 

2-days add-on

 

30 May

09.00: - 10.30

JMB

Session 12: OLS assumptions and extended methods

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

30 May

10.45 -12.15

JMB

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

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

30 May

13.15 – 15.00

JMB, MC

Session 14: Student presentations 2: Individual student project and presenting findings in a publishable regression table

 

30 May

15.30 – 17.00

JMB, MC

Session 15 Group challenge 2: Apply statistics to answer a specific research question

 

31 May

09.00 – 10.30

JMB, MC

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

 

31 May

10.45 – 12.15

JMB

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

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

31 May

13.15 – 15.00

JMB, MC

Session 18: Class discussions on the application of quantitative methods to individual research questions. Evaluation of the course

 

 
Exam

Not applicable.

Additional information

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. 

ECTS

3 + 2 (Including add-on: + 1 ECTS). 
 
 
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).

·       (I 2014) - Ioannidis, John (2014). How to Make More Published Research True. PLoS Medicine, 11(10).

·       (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.

Recommended additional 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.


Registration deadline and conditions

The registration deadline is 8 April 2024. If you want to cancel your registration on the course it should be done prior to this mentioned date. By this date we determine whether we have enough registrations to run the course, or who should be offered a seat if we have received too many registrations.

If there are more seats available on the course we leave the registration open by setting a new regsitration deadline in order to fill remaining seats. Once you have received our acceptance/welcome letter to join the course, your registration is binding and we do not refund your course fee. The binding registration date will be the registration deadline mentioned above.

Payment methods
 
Make sure you choose the correct method of payment upon finalizing your registration:
 
CBS students:
Choose payment method CBS PhD students and the course fee will be deducted from your PhD course budget.
 
Students from other Danish universities: 
Choose payment method Danish Electronic Invoice (EAN). Fill in your EAN number, attention and possible purchase (project) order number.
Do you not pay by EAN number please choose Invoice to pay via electronic bank payment (+71). 
 
Students from foreign universities:
Choose payment method Payment Card. Are you not able to pay by credit card please choose Invoice International to pay via bank transfer. 
 

Event Location

Click to view the event location on Google Maps >

Organizer Contact Information

CBS PhD School
Nina Iversen

Phone: +45 3815 2475
ni.research@cbs.dk

Organizer Contact Information

CBS PhD School
Nina Iversen

Phone: +45 3815 2475
ni.research@cbs.dk