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Related Concept Videos

Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects or...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:

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Related Experiment Video

Updated: Jul 13, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Resampling dependent concordance correlation coefficients.

John M Williamson1, Sara B Crawford, Hung-Mo Lin

  • 1Division of Parasitic Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30341, USA. jow5@cdc.gov

Journal of Biopharmaceutical Statistics
|July 7, 2007
PubMed
Summary
This summary is machine-generated.

This study evaluates resampling methods for testing the concordance correlation coefficient (CCC). The bootstrap method is valid for CCC reproducibility, though small sample sizes may inflate errors.

Related Experiment Videos

Last Updated: Jul 13, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Area of Science:

  • Biostatistics
  • Reproducibility Studies
  • Statistical Inference

Background:

  • The concordance correlation coefficient (CCC) is widely used to assess the reproducibility of continuous measurements.
  • Hypothesis testing for dependent CCCs often relies on methods with stringent assumptions or large sample requirements.

Purpose of the Study:

  • To evaluate the performance of permutation testing and bootstrap resampling for hypothesis tests on dependent concordance correlation coefficients.
  • To compare the validity and error rates of these resampling methods under various conditions.

Main Methods:

  • The study employed simulation to assess the type-I error rates and validity of permutation tests and bootstrap methods for dependent CCCs.
  • Data from a carotid stenosis screening study was used for illustrative analysis.

Main Results:

  • Permutation testing demonstrated limited applicability due to its exchangeability assumption.
  • Bootstrap inference for dependent CCCs was found to be valid, but exhibited inflated type-I error rates with small sample sizes (approximately 30).

Conclusions:

  • The bootstrap method offers a flexible and valid approach for hypothesis testing on dependent concordance correlation coefficients.
  • Researchers should be cautious of inflated type-I errors when using the bootstrap with small sample sizes.