Quantitative methods and statistics (In Sport and Exercise Science)

Introduction

Welcome to the course Quantitative methods and Statistics (IDR4000). The course aims to give students an overview of methodological aspects within the field of sport and exercise-physiology. Specifically, planning, conducting and analyzing research projects with human participants will be covered. The lecture notes for the course can be found here. This website will contain tutorials, workshops, assignments and lectures that we will work on in the course.

Practical information

These notes were updated on 2024-11-04 and cover the course held during 2024 autumn semester. Contact Daniel Hammarström if you have any questions regarding this content.

Learning objectives

Learning objectives can be read in Norwegian here.

Learning strategies

The course will include lectures, laboratory exercises, computer exercises and workshops, seminars and student presentations. Workshops will be held in-person.

Computer exercises will eventually require that you have special computer software installed on your computer. The software is free (see specific chapters in lecture notes).

Assignments and Portfolio exam

The course is based on several assignments. Some of these assignments are to be handed in as part of a portfolio exam upon which your grade is based.

Assignments that are due during the course (arbeidskrav) are expected to be further improved after feedback from fellow students and teachers before inclusion in your portfolio.

You can use the same template for all assignments and further improve this to hand in as your portfolio exam. The template is a quarto book project with each part of the exam structured as a chapter, see and fork this GitHub repository to get started.

The portfolio exam should include:

The whole course in one table

Week Workshops/Activities Suggested reading Lectures Assignments
34 Introduction to data science, Starting up R, Creating your first graph, The quarto publishing system Course notes ch. 1-8, (Spiegelhalter 2019, Ch. 1-2), (Wickham and Grolemund 2017, ch. 1-8) Intro to data science (video)(slides), Software (slides), Git and GitHub (slides)
35 Physiological testing, group work (Hopkins 2000; Halperin, Pyne, and Martin 2015), Course notes ch. 9, (Tanner and Gore 2012)
36 Molecular laboratory, extraction and analysis of DNA (Del Coso et al. 2019), (Sharples, Morton, and Wackerhage 2022, ch. 1-5),Course notes ch. 10 Assignment: Extraction and Analysis of DNA
37 Wrangling data, creating tables, Tables, Writing reports with git and github, Summarising data Course notes ch. 1-8,(Wickham and Grolemund 2017, ch. 1-8) Data visualization (video, slides, Data wrangling (video), slides, dplyr (video, slides) Assignment 1: Reliability and tools for reproducible data science
38 The linear model, The linear model, cont, Curve-linear relationships, R functions (Spiegelhalter 2019, Ch. 3-6),Course notes ch. 11-14 Assignment 2: Regression models, predicting from data
39 P-values and confidence intervals, Power and effect sizes (Spiegelhalter 2019, Ch. 7-10), Course notes ch 16-18 Assignment 3: Drawing inference from statistical models, and statistical power
40 Molecular laboratory, extraction and analysis of RNA (Kuang et al. 2018) Assignment: Extraction and Analysis of RNA
41 Philosophy of science See Canvas See Canvas
42 Study designs, Causality, Simulations for causal designs Course notes ch. 19, (Spiegelhalter 2019, Ch. 7-14) Causality and regression models(slides)
43 Analyzing studies, pre-post designs, Analyzing studies, mixed effects models Varying effects models(slides) Assignment 4: Study designs
44 Molecular laboratory, extraction and analysis of Protein Andersen and Aagaard (2000) Assignment: Extraction and Analysis of Protein
45 Case studies, Putting it all together, reproducible science Assignment 5: Analyzing repeated measures experiments
46
47
48 EKSAMEN: Inspera Fredag 2024-11-22, kl. 14:00

References

Andersen, J. L., and P. Aagaard. 2000. “Myosin Heavy Chain IIX Overshoot in Human Skeletal Muscle.” Muscle Nerve 23 (7): 1095–1104.
Bass, J. J., D. J. Wilkinson, D. Rankin, B. E. Phillips, N. J. Szewczyk, K. Smith, and P. J. Atherton. 2017. “An Overview of Technical Considerations for Western Blotting Applications to Physiological Research.” Scand J Med Sci Sports 27 (1): 4–25. https://doi.org/10.1111/sms.12702.
Del Coso, J., D. Hiam, P. Houweling, L. M. Perez, N. Eynon, and A. Lucia. 2019. “More Than a ’Speed Gene’: ACTN3 R577X Genotype, Trainability, Muscle Damage, and the Risk for Injuries.” Eur J Appl Physiol 119 (1): 49–60. https://doi.org/10.1007/s00421-018-4010-0.
Halperin, I., D. B. Pyne, and D. T. Martin. 2015. “Threats to Internal Validity in Exercise Science: A Review of Overlooked Confounding Variables.” Journal Article. Int J Sports Physiol Perform 10 (7): 823–29. https://doi.org/10.1123/ijspp.2014-0566.
Hopkins, W. G. 2000. “Measures of Reliability in Sports Medicine and Science.” Journal Article. Sports Med 30 (1): 1–15. http://www.ncbi.nlm.nih.gov/pubmed/10907753.
Kuang, J., X. Yan, A. J. Genders, C. Granata, and D. J. Bishop. 2018. “An Overview of Technical Considerations When Using Quantitative Real-Time PCR Analysis of Gene Expression in Human Exercise Research.” PLoS One 13 (5): e0196438. https://doi.org/10.1371/journal.pone.0196438.
Sharples, Adam P., James Morton, and Henning Wackerhage, eds. 2022. Molecular Exercise Physiology: An Introduction. Second edition. New York, NY: Routledge.
Spiegelhalter, D. J. 2019. The Art of Statistics : How to Learn from Data. Book. First US edition. New York: Basic Books.
Tanner, R. K., and C. J. Gore. 2012. Physiological Tests for Elite Athletes 2nd Edition. Book. Human Kinetics. https://books.google.no/books?id=0OPIiMks58MC.
Wickham, Hadley, and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st ed. Paperback; O’Reilly Media. http://r4ds.had.co.nz/.