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: 2020-03-12
- Course end: 2020-06-17
- 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
March 12th 2020. Register via email <firstname.lastname@example.org>
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:
Short Course Description
- 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
During the course we will treat the following topics:
- 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
- Data collection
- Data collection strategy, sampling design, interpretations
- Sampling and non-sampling error
- Data visualization and decision making
- Good and bad graphs
- Exploratory Data Analysis
- Common biases and decision framing
- Analytic Hierarchy Process
- Basic statistical inference
- p-value, hypothesis testing, confidence intervals
- Statistical significance and practical significance
- Paired and independent t-test
- Comparing k treatment - ANOVA
- Design and analysis of data generated from experiments
- Factorial design
- Applications of factorial design
- Multivariate data analysis
- Multiple regression
- Discriminant analysis
- Principal component analysis
- Factor analysis
- Cluster analysis
- Statistical association and causation
- Path analysis
- Partial Least Square Path Modelling (variance-based "SEM")
- Covariance-based Structural Equation Modelling (CB-SEM)
- Mediation and moderation
- Introduction to discrete choice models
- Random Utility theory
- Types of choice data: revealed vs stated preferences
- Binary Probit/Logit models
- Multinomal Probit/Logit models
The examination will include
- Active participation in the seminars
- Approved exercises (with SPSS, AMOS, JMP, R or other software), which have to be handed in before the end of the course.
- 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.
- Interactive exam (based on the participants' own questions, details will be revealed in due course)
- Individual reflection diary on the learning process with respect to statistical thinking
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.
Examiners Associate professor Dan Andersson and Associate professor Hendry Raharjo