PhD 646 Design and Analysis of Quantitative Research Studies in Sport Science - Feb - March 2023

PHD 646 Quantitative methods part 1 - Design and Analysis. Max 10 participants - the course will be open for external ph.d.-students but ph.d.-students from NIH will have priority.



28. februar - 16. mars


06. februar 2023






Interactive sessions, delivered in 2-hour blocks (9:00 am – 11:00 am) on 3 mornings (Tuesday, Wednesday, Thursday) per week over 3 weeks - starting week 9 / Tuesday 28th Feb2023, last session week 11 / Wednesday 16th March 2023. See details of sessions and support material/books and papers below ("Program"). 

Please see Canvas for possible updates.

See also "Course plan Quantitative methodology (Emneplan - only in Norwegian)


Professor Ørnulf Seippel, IIS, NIH (email: 

Professor Rob Herbert, Neuroscience Research Australia (NeuRA) / University of New South Wales, Sydney (email:

Prerequisites prior to start of the PhD course:

The course will build on competence similar to the course in methodology from the master study at the Norwegian School Sport Sciences (MET400) 

See also student assignments below ("Praktisk informasjon").

See compulsory and supplementary literature for the MET400 course: 

Thomas, J., Nelson, J. K., & Silverman, S. J. (2015). Research methods in physical activity (7th ed.). Chapter 1-5, 11, 14-18, Human Kinetics (u.å.) Kunnskapsbasert praksis  (in Norwegian) (u.p) Statistikk (in Norwegian)

PEDro Scale (1999)


The first 6 sessions will be at campus, while the rest will be via Zoom (from Sydney, Australia)

Session Week Day Content papers books
1 9 Regression: Exploring data, ØS   1
1 9 Regression: Introduction to linear models, ØS   1
3 9 Regression: Multiple models   1
4 9 Regression: Diagnosics   1
5 9 Th Regression; Fitting general linear models, ØS   1
6 9   Regression: Multilevel models, ØS   2
7 9 Th Asking a good research question, RH 3-5  
8 9 Th Association, prediction and causation, RH 6 7
9 10 T Designing experiments and randomised trials, RH   8,9
10 10 T Analysing experiments and randomised trials, RH 10 8,9
11 10 W Designing observational studies of casual effects, RH    11
12 10 W Analysing  observational studies of casual effects, RH  12,13 14, 15
13 10 Th Prediction, RH   16
14 10 Th Systematic reviews and meta-analysis, RH 17,18 19
15 11 T Sample size 20  
16 11 W Student presentations, KB, RH, ØS    
17 11 W Student presentations KB, RH, ØS    
18 11 Th Student presentations KB, RH, ØS    

ØS: Ørnulf Seippel, RH: Rob Herberg, KB: Kari Bø


  1. Fox J, Wesiberg S. An R Companion to Applied Regression. London: Sage; 2018.
  2. Austin PC, goel V, van Walraven C. an intorduction to multilevel regression models. Canadian Journal of Public Health. 2001;92:150-154.
  3. Hernán MA, Hsu J, Healy B. A second chance to get causal inference right: a classification of data science tasks. Chance. 2019;32(1):42-49.
  4. Hernan MA. The c-word: scientific euphemisms do not improve causal inference from observational data. Am J Public Health. May 2018;108(5):616-619.
  5. Herbert RD. Cohort studies of aetiology and prognosis: they're different. Journal of Physiotherapy. 2014;60:241-244.
  6. Herbert RD. Causal inference. Journal of Physiotherapy. 2020;66:273-277.
  7. Pearl J, McKenzie D. The Book of Why. The New Science of Cause and Effect. New York: Basic Books; 2018.
  8. Pocock SJ. Clinical Trials: A Practical Approach. Chichester [West Sussex] ; New York: Wiley; 1983.
  9. Friedman LM, DeMets DL, Furberg C. Fundamentals of Clinical Trials. 3rd ed. New York: Springer; 1998.
  10. Herbert RD, Kasza J, Bo K. Analysis of randomised trials with long-term follow-up. BMC Med Res Methodol. May 29 2018;18(1):48.
  11. Grobbee DE, Hoes AW. Clinical Epidemiology: Principles, Methods, and Applications for Clinical Research. Sudbury, MA.: Jones and Bartlett; 2009.
  12. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. Jan 1999;10(1):37-48.
  13. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package 'dagitty'. Int. J. Epidemiol. Dec 1 2016;45(6):1887-1894.
  14. Pearl J, Glymour M, Jewell NP. Causal Inference in Statistics. A Primer. Chichester: Wiley; 2016.
  15. Hernán MA, Robins JM. Causal Inference. What If. Boca Raton: Chapman & Hall/CRC. Available at; 2020.
  16. Steyerberg EW. Clinical Prediction Models: a Practical Approach to Development, Validation, and Updating. New York, NY: Springer; 2009.
  17. Herbert RD, Bø K. Analysis of quality of interventions in systematic reviews. BMJ. 2005;331(7515):507-509.
  18. Hoogeboom TJ, Kousemaker MC, van Meeteren NL, et al. i-CONTENT tool for assessing therapeutic quality of exercise programs employed in randomised clinical trials. British Journal of Sports Medicine. Nov 3 2020.
  19. Higgins J, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions (version 5.0.2). The Cochrane Collaboration; 2009.
  20. Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. An overview of variance inflation factors for sample-size calculation. Evaluation and the Health Professions. Sep 2003;26(3):239-257.


Praktisk informasjon

Student assignments:

1. Before course start (by February 21st 2023): the students must submit (in CANVAS) maximum two pages including the following information:  Research questions for all their planned PhD studies with study design and suggested statistical analyses

2. All students need to prepare a power-point presentation of their revised plans for design and statistical analyses on March 14th-16th, 2023. The students can choose to focus on one or more of their studies.



Binding registration by email to 

Kontakt oss

Kari Bø

Kari Bø


Telefon: +47 23 26 20 08 / +47 990 47 363