Related Experiment Video
Updated: Jul 25, 2025

06:51
Physical Activity Measurement in Children Accepting Table Tennis Training
Published on: July 27, 2022
2.0K
Mapping the youth soccer: A bibliometrix analysis using R-tool.
Bo Liu1, Chang-Jing Zhou1, Hao-Wei Ma1
1School of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Digital Health
|June 26, 2023
Summary
Scientific research on youth soccer is growing, with US and UK scholars leading. Key areas include performance, talent development, and injury prevention, reflecting evolving research needs.
Area of Science:
- Sports Science
- Bibliometrics
- Youth Sports Research
Background:
- Increasing scientific output in youth soccer research globally.
- Lack of a comprehensive overview of research trends in this field.
- Need to understand the evolution of research topics and key contributors.
Purpose of the Study:
- To map global research trends in youth soccer from 2012-2021.
- To analyze research output across sources, authors, documents, and keywords.
- To identify emerging and dominant research themes.
Main Methods:
- Bibliometric analysis of 2606 articles from Web of Science (WoS).
- Utilized Biblioshiny software for data analysis.
- Examined publications spanning a decade (2012-2021).
Main Results:
- US and UK researchers are the most prolific in youth soccer studies.
- Research topics are adapting to current needs, with a focus on performance.
- Key areas of interest include talent identification and development, performance optimization, injury prevention, and concussion management.
Conclusions:
- Provides a global perspective on youth soccer research evolution.
- Highlights dominant research themes and geographical contributions.
- Offers insights to guide future research directions in youth soccer and related sports science domains.
Related Concept Videos
Introduction to R
434
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
434
Interpreting R Charts
87
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
87
Statistical Methods for Analyzing Epidemiological Data
426
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
426
Friedman Two-way Analysis of Variance by Ranks
250
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
250
Coefficient of Correlation
6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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...
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...
6.2K
Statistical Analysis: Overview
6.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.7K

