Related Experiment Video
Updated: Nov 7, 2025

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running
Published on: July 17, 2020
Discriminant Analysis of Anthropometric and Training Variables among Runners of Different Competitive Levels
Mabliny Thuany1, Raphael F de Souza2, Lee Hill3
1Centre of Research, Education, Innovation and Intervention in Sport (CIFI2D), Faculty of Sports, University of Porto, 4200-450 Porto, Portugal.
Abstract:
The purpose of this study was to investigate the multivariate profile of different types of Brazilian runners and to identify the discriminant pattern of the distinct types of runners, as a runners' ability to self-classify well. The sample comprised 1235 Brazilian runners of both sexes (492 women; 743 men), with a mean age of 37.94 ± 9.46 years. Individual characteristics were obtained through an online questionnaire: Sex, age, body height (m) and body mass (kg), socioeconomic status, and training information (i.e., self-classification, practice time, practice motivation, running pace, frequency and training volume/week). Multivariate analysis of variance was conducted by sex and the discriminant analysis was used to identify which among running pace, practice time, body mass index and volume/training could differentiate groups such as "professional athletes", "amateur athletes" and "recreational athletes". For both sexes, running pace was the variable that better discriminated the groups, followed by BMI and volume/week. The practice time is not a good indicator to differentiate runner's types. In both sexes, semi-professional runners were those that better self-classify themselves, with amateur runners presenting the highest classification error. This information can be used to guide the long-term training, athlete's selection programs, and to identify the strengths and weaknesses of athletes.
Related Concept Videos
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
One-Way ANOVA: Unequal Sample Sizes
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Wald-Wolfowitz Runs Test II
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Wald-Wolfowitz Runs Test I
The test works...

