Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Kendall's Tau Test01:16

Kendall's Tau Test

724
Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value...
724
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

407
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...
407
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
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:
6.0K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

849
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...
849
Coefficient of Correlation01:12

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...
6.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Gab1 but not Grb2 mediates tumor progression in Met overexpressing colorectal cancer cells.

Carcinogenesis·2008
Same author

Long-term donor-specific tolerance in rat cardiac allografts by intrabone marrow injection of donor bone marrow cells.

Transplantation·2008
Same author

Lsr2 of Mycobacterium tuberculosis is a DNA-bridging protein.

Nucleic acids research·2008
Same author

Amphetamine selectively enhances avoidance responding to a less salient stimulus in rats.

Journal of neural transmission (Vienna, Austria : 1996)·2008
Same author

Retrospective analysis of anterior correction and fusion for adolescent idiopathic thoracolumbar/lumbar scoliosis: the relationship between preserving mobile segments and trunk balance.

International orthopaedics·2008
Same author

Intrarenal antigens activate CD4+ cells via co-stimulatory signals from dendritic cells.

Journal of the American Society of Nephrology : JASN·2008

Related Experiment Video

Updated: Jul 18, 2025

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice
06:11

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Published on: October 19, 2019

19.9K

Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context

Ming Li1, Qian Gao1, Tianfei Yu2

  • 1Department of Computer Science and Technology, College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, China.

BMC Cancer
|August 25, 2023
PubMed
Summary

Accurate inter-rater reliability (IRR) assessment is vital for research. This analysis reviews Kappa statistics for measuring agreement between two raters, emphasizing correct application for reliable findings.

Keywords:
Cohen’s KappaDWIIntra-rater reliabilityKappa statisticLiver metastasesRECIST 1.1 criteriaWeighted Kappa

More Related Videos

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.5K
A Protocol of Manual Tests to Measure Sensation and Pain in Humans
07:28

A Protocol of Manual Tests to Measure Sensation and Pain in Humans

Published on: December 19, 2016

21.0K

Related Experiment Videos

Last Updated: Jul 18, 2025

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice
06:11

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Published on: October 19, 2019

19.9K
Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.5K
A Protocol of Manual Tests to Measure Sensation and Pain in Humans
07:28

A Protocol of Manual Tests to Measure Sensation and Pain in Humans

Published on: December 19, 2016

21.0K

Area of Science:

  • Statistics
  • Biostatistics
  • Research Methodology

Background:

  • Assessing inter-rater reliability (IRR) is crucial in observational studies with multiple raters.
  • Many studies lack proper statistical procedures for IRR, impacting interpretation and statistical power.
  • This article examines methodological issues in IRR assessment, focusing on Kappa statistics.

Discussion:

  • The Kappa statistic is suitable for two raters with two categories or unordered categorical variables.
  • Weighted Kappa is recommended for assessing agreement between two raters for ordered categorical variables.
  • Proper selection, computation, interpretation, and reporting of IRR statistics are essential.

Key Insights:

  • The choice of Kappa statistic depends on the nature of the categorical variables (ordered vs. unordered).
  • Accurate IRR assessment ensures the reliability and validity of research findings.
  • Disputes over statistical methods highlight the need for rigorous application of IRR measures.

Outlook:

  • Future research should prioritize robust IRR assessment to enhance statistical power and data interpretation.
  • Standardized reporting guidelines for IRR statistics would improve study reproducibility.
  • Continued focus on statistical best practices in reliability analysis is necessary.