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Nordic Five Tech - PhD course database

Nordic Five Tech (N5T) is an alliance of the five leading technical universities in Denmark, Finland, Norway and Sweden. N5T has a joint PhD course database where you can search for courses to match your interests and needs.


Courses given by Technology Management and Economics

Quantitative Research Methods

  • Course code: FTEK025
  • Course higher education credits: 7.5
  • Department: TECHNOLOGY MANAGEMENT AND ECONOMICS
  • Graduate school: Technology Management and Economics
  • Course start: 2019-03-14
  • Course end: 2019-08-30
  • Course is normally given:
  • Language: The course will be given in English
  • Nordic Five Tech (N5T): This course is free for PhD students from N5T universities

Prerequisites:
Applicants should be enrolled as PhD-students. Priority is given to doctoral students from the department of Technology Management and Economics who, preferably, already attended RTME course.
Purpose of the course
The purpose of this course is to provide participants with necessary knowledge and skills in order to get started in doing quantitative research, but it will not guide you all the way to the finish. The focus is not on the mathematical intricacies, but rather on the following:
  • Ability to read quantitative research in a critical way
  • Basic ideas and underlying assumptions
  • Use and misuse of quantitative research
  • Use of computer packages for performing analyses
Short Course Description
During the course we will treat the following topics:
  1. Research design and measurement
    • Problem definition, research question, purpose, expected research outcome
    • Types of measurement and scales
    • Reliability and validity
    • Reflections on the interpretation of measurement data and their objectivity
  2. Data collection
    • Data collection strategy, sampling design, interpretations
    • Sampling and non-sampling error
  3. Data visualization and decision making
    • Good and bad graphs
    • Exploratory Data Analysis
    • Common biases and decision framing
    • Analytic Hierarchy Process
  4. Basic statistical inference
    • p-value, hypothesis testing, confidence intervals
    • Statistical significance and practical significance
    • Paired and independent t-test
    • Comparing k treatment - ANOVA
  5. Design and analysis of data generated from experiments
    • Factorial design
    • Applications of factorial design
  6. Multivariate data analysis
    • Multiple regression
    • Discriminant analysis
    • Principal component analysis
    • Factor analysis
    • Cluster analysis
  7. Statistical association and causation
    • Path analysis
    • Partial Least Square Path Modelling (variance-based "SEM")
    • Covariance-based Structural Equation Modelling (CB-SEM)
    • Mediation and moderation
  8. Introduction to discrete choice models
    • Random Utility theory
    • Types of choice data: revealed vs stated preferences
    • Binary Probit/Logit models
    • Multinomal Probit/Logit models
Examination
The examination will include
  1. Active participation in the seminars
  2. Approved exercises (with SPSS, AMOS, JMP, R or other software), which have to be handed in before the end of the course.
  3. Analysis and reflections on two studies preferably published in scientific journals and where at least some of the data is such that the analysis is possible to follow from A to Z.
    • Preferably, one of these studies should be in the participant¿s own area.
    • For the second study it is possible to use own data and a detailed research plan including the steps towards a journal paper based on a quantitative approach
    • The analysis may be performed in groups of two.
    • For the analysis of each study a short report and a presentation (ppt) are required
    • The report must be handed in latest one week in advance of the presentation (to the opponents and the examiner)
    • The report, the presentation, as well as the opposition will be a foundation for the examination.
    • The requirements will be further discussed and explained during the course.
  4. Interactive exam (based on the participants' own questions, details will be revealed in due course)
  5. Individual reflection diary on the learning process with respect to statistical thinking
Preliminary schedule
Venue: Seminar room 3363 Avenyn, Vasa House 2, 4th floor, Vera Sandbergs allé 8 
On 4 April and 24 April Vasa 5, ground floor, Vera Sandbergs allé 8

Date / time / tutor /
14/3 13.15-17.00 /DA
Topics / Mandatory readings
Introduction, Research Design and Measurement (e.g. Survey) / Bryman and Bell 2015 pp 160-180 (2011: 150-171), 355-357 (2011: 350-352)); Czaja and Blair (2005); ch 4 and 5 ( (2014) 177-227); Flynn et al.(1990)

Date / time / tutor
28/3 / 13.15-17.00 /DA
Topics / Mandatory readings
Data collection and discussion of assignment to design questions / Bryman and Bell 2015, pp 184-191, 197-205, 242-248, 257-274 (2011: 173-180, 185-194, 234-240, 248-264); Czaja and Blair (2005); ch 7 (pp 125-141) and 9 ((2014) 106-126, 11-19, 171-175)

Date / time / tutor
4/4 / 13.15-17.00 /HR
Topics / Mandatory readings
Data visualization / Healy and Moody (2014) 
Quantifying judgments: Analytic Hierarchy Process (AHP) / Forman and Gass (2001)

Date / time / tutor
11/4 / 13.15-17.00 / HR
Topics / Mandatory readings
Basic statistical inference / Cohen (1990); Gigerenzer and Marewski (2015), 
Data collection for design of experiment

Date / time / tutor
24/4 / 13.15-17.00 / HR
Topics / Mandatory readings
Design and analysis of data generated from experiments / Box (1976); Podsakoff and Podskakoff (2019)
Statistical association and causation / Velickovic (2015); Wright (1999)

Date / time / tutor
2/5 / 13.15-17.00 / DA
Topics / Mandatory readings
Factor analysis and cluster analysis /Bryman and Bell 365-387, and Handouts (Hair et al. (2010, or other edition))

Date / time / tutor
9/5 / 13.15-17.00 / ISD
Topics / Mandatory readings
Introduction to discrete choice models / Brown (2003); Holguín-Veras et al. (2016)

Date / time / tutor
16/5 / 13.15-17.00 / DA
Topics / Mandatory readings
Multiple regression and discriminant analysis / Handouts. (Hair et al (2010, or other editions))

Date / time / tutor
23/5 / 13.15-17.00 / HR
Topics / Mandatory readings
SEM (Covariance-based SEM) / Shah and Goldstein (2006); Williams et al. (2009)
Mediation and Moderation

Date / time / tutor
5/6 / 13.15-17.00 / HR
PLS-PM (Variance-based "SEM") / Rönkkö et al (2016), Sarstedt et al. (2016)

Date / time / tutor
13/6 / 13-17 / DA, HR
Topics / Mandatory readings
Interactive examination and discussion regarding Gould / Gould (1996) first five chapters + all above

Date / time / tutor
End of August / 09-12, 13-17 / DA, HR
Topics
Examination group 1 (project presentation) / Examination group 2 (project presentation)
Course lecturers: Dan Andersson (DA), Hendry Raharjo (HR), Ivan Sánchez-Diaz (ISD)
Literature
References
Box, G. E. P. (1976), Science and Statistics. Journal of the American Statistical Association, 71(356), 791-99.
Brown, T.C. (2003). Introduction to Stated Preference Methods. In A primer on nonmarket valuation (pp. 99-110). Springer, Netherlands.
Bryman, A., Bell, E. (2015). Business research methods. Oxford University Press, USA.
Cohen, J. (1990). Things I have learned (so far). American psychologist, 45(12), 1304.
Czaja, R., Blair, J. (2005). Designing Surveys: a guide to decisions and procedures. Thousand Oaks, Calif.: Pine Forge Press.
Fenton, N., Neil, M. (2007). Managing Risk in the Modern World: Applications of Bayesian Networks. London Mathematical Society Knowledge Transfer Report. [online] London. Available at: https://www.lms.ac.uk/sites/lms.ac.uk/files/files/reports/Bayesiannetworksfinal.pdf
Fenton, N., Neil, M. (2011). The use of Bayes and causal modelling in decision making, uncertainty and risk. CEPIS Upgrade, 12(5), 10-21.
Flynn, B., Sakakibara, S., Schroeder, R., Bates, K., Flynn, E. (1990). Empirical research methods in operations management. Journal of Operations Management, 9(2), 250-84.
Forman, E. H., Gass, S. I. (2001). The Analytic Hierarchy Process: An Exposition. Operations Research, 49(4), 469-86.
Gigerenzer, G., Marewski, J. N. (2015). Surrogate Science The Idol of a Universal Method for Scientific Inference. Journal of Management, 41(2), 421-440.
Gould, S. J. (1996). The Mismeasure of Man. New York: Norton.
Hair, J. F. (2010). Multivariate Data Analysis: a Global Perspective. Upper Saddle River, N.J.: Pearson Education.
Holguín-Veras, J., Sánchez-Díaz, I., Reim, B. (2016). ETC adoption, time-of-travel choice, and comprehensive policies to enhance time-of-day pricing: a stated preference investigation. Transportation, 43(2), 273-299.
Healy, K., & Moody, J. (2014). Data visualization in sociology. Annual review of sociology, 40, 105-128.
Jackson, M., Cox, D. (2013). The principles of experimental design and their application in sociology. Annual Review of Sociology, 39, 27-49.
Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47, 9-27.
Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies!. Journal of Business Research, 69(10), 3998-4010.
Shah, R., Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24(2), 148-169.
Velickovic, V. M. (2015). What everyone should know about statistical correlation. American Scientist, 103(1), 26.
Williams, L. J., Vandenberg, R. J., Edwards, J. R. (2009). Structural Equation Modeling in Management Research: A Guide for Improved Analysis. The Academy of Management Annals, 3(1), 543-604.
Wright, B. R. E., Caspi, A., Moffitt, T. E., Miech, R. A., Silva, P. A. (1999). Reconsidering the relationship between SES and delinquency: Causation but not correlation. Criminology, 37(1), 175-94.
Lecturers
Examiners Associate professor Dan Andersson and Associate professor Hendry Raharjo
More information

Published: Thu 22 Nov 2018.