Applied Quantitative Methods for Non-quantitative Doctoral Researchers
CBS PhD School
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Associate Professor Manuele Citi (MSC) Associate Professor Charles T. Tackney (MSC) |
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Manuele Citi & Jan M. Bauer |
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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. |
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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. |
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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) |
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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. |
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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:1; 2.1-2.4.
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
13.15-15.00 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
AP, MC
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 |
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At the end of the course, doctoral students should be able to:
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Not applicable. |
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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. |
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15/05/2023 |
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19/05/2022 |
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PhD |
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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. |
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English |
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Other readings as suggested by the doctoral student skills and interests assessment survey. Recommended literature 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. |
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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. |
For full course description and course registration please visit the CBS web site.