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 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...
Kendall's Tau Test01:16

Kendall's Tau Test

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 of +1 indicates that...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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...
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:
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...

You might also read

Related Articles

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

Sort by
Same author

The chi-square test of independence.

Biochemia medica·2013
Same author

Multiple comparison analysis testing in ANOVA.

Biochemia medica·2012
Same author

Scientific inquiry: clinical statistics for primary care practitioners: Part II-absolute risk reduction, relative risk, relative risk reduction, and number needed to treat.

Journal for specialists in pediatric nursing : JSPN·2008
Same author

Psychometric tests of Expectations of Filial Piety Scale in a Mexican-American population.

Journal of clinical nursing·2007
Same author

Clinical statistics for primary care practitioners: part I--incidence, prevalence, and the odds ratio.

Journal for specialists in pediatric nursing : JSPN·2007
Same author

The role of caregiver gender and caregiver burden in nursing home placements for elderly Taiwanese survivors of stroke.

Research in nursing & health·2004

Related Experiment Video

Updated: May 17, 2026

Doppler Ultrasound-Based Leg Blood Flow Assessment During Single-Leg Knee-Extensor Exercise in an Uncontrolled Setting
09:18

Doppler Ultrasound-Based Leg Blood Flow Assessment During Single-Leg Knee-Extensor Exercise in an Uncontrolled Setting

Published on: December 15, 2023

Interrater reliability: the kappa statistic.

Mary L McHugh1

  • 1Department of Nursing, National University, Aero Court, San Diego, California, USA. mchugh8688@gmail.com

Biochemia Medica
|October 25, 2012
PubMed
Summary
This summary is machine-generated.

Cohen

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

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

Related Experiment Videos

Last Updated: May 17, 2026

Doppler Ultrasound-Based Leg Blood Flow Assessment During Single-Leg Knee-Extensor Exercise in an Uncontrolled Setting
09:18

Doppler Ultrasound-Based Leg Blood Flow Assessment During Single-Leg Knee-Extensor Exercise in an Uncontrolled Setting

Published on: December 15, 2023

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

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

Area of Science:

  • Statistics
  • Biostatistics
  • Health Research Methodology

Background:

  • Interrater reliability is crucial for ensuring data accuracy in studies.
  • Traditionally, percent agreement was used, but it doesn't account for chance agreement.
  • Cohen's kappa was developed to correct for chance agreement, offering a more robust measure.

Purpose of the Study:

  • To evaluate the suitability of Cohen's kappa for assessing interrater reliability in health research.
  • To compare Cohen's kappa with percent agreement.
  • To propose acceptable thresholds for kappa and percent agreement in healthcare studies.

Main Methods:

  • Review and critique of Cohen's kappa statistic.
  • Comparison of Cohen's kappa with percent agreement.
  • Analysis of interpretation guidelines for kappa in health research contexts.

Main Results:

  • Cohen's kappa is a widely used statistic for interrater reliability, accounting for chance agreement.
  • Cohen's suggested interpretation of kappa values may be too lenient for health research, with 0.41 potentially being acceptable.
  • Percent agreement is a simpler but less rigorous measure.

Conclusions:

  • The interpretation of Cohen's kappa requires careful consideration in health research.
  • Current guidelines for kappa may not be stringent enough for critical healthcare studies.
  • Recommended levels for both kappa and percent agreement should be established for healthcare research.