Kendall W
Kendall W - Related Videos
01:16
Kendall's Tau Test
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2025
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...
01:20
Kendall's Coefficient of Concordance
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2025
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...
07:17
Analysis of Motility Patterns of Stentor During and After Oral Apparatus Regeneration Using Cell Tracking
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Cited by 1 •
2021
We present a protocol for the characterization of motility and behavior of a population of hundred micron- to millimeter-sized cells using brightfield microscopy and cell tracking. This assay reveals that Stentor coeruleus transitions through four behaviorally distinct phases when regenerating a lost oral apparatus.
10:39
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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2025
This paper outlines the protocol for qualitatively and quantitatively validating a university service-learning instrument, following the parsimony criterion to create the most robust and optimal version possible. The Delphi qualitative validation method and Robust Unweighted Least Squares Exploratory Factor Analysis, as a quantitative item optimization method, are utilized for this purpose.
08:49
Assessing Agrochemical Risk to Mated Honey Bee Queens
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Cited by 9 •
2021
This protocol was developed to enhance the understanding of how agrochemicals affect honey bee (Apis mellifera) reproduction by establishing methods to expose honey bee queens and their worker caretakers to agrochemicals in a controlled, laboratory setting and carefully monitoring their relevant responses.
FAQs
After each rater’s evaluations are converted to ranks, the ranks for each item are added across raters. Items receiving similar total ranks produce little variation among rank sums, indicating stronger consensus. Greater variation means raters ordered the items differently, so the resulting concordance is lower. This comparison supplies the statistic’s central mechanism.
A tie occurs when a rater assigns the same rank to multiple items. Shared ranks change the variability that would otherwise be expected from distinct rankings, so the calculation may require a tie correction. Accounting for ties makes the comparison between observed rank-sum variability and complete disagreement more appropriate for the actual ordinal data.
Values near 0 indicate that the raters show little shared ordering of the items, whereas values near 1 indicate that their rankings are highly consistent. The statistic describes agreement in the ranking pattern, not whether the raters selected a particular item as objectively correct. Interpretation should therefore focus on consensus among the observed judgments.
The workflow begins by collecting evaluations from multiple raters for the same items. Each rater’s evaluations is converted into ranks, and ranks are summed item by item across raters. The variability of those sums is then compared with the variability expected under complete disagreement, while shared ranks receive tie corrections when needed.
Kendall W is useful when a study asks whether several raters or judges agree in how they order the same set of items. It fits ordinal evaluations in settings such as surveys, expert judgments, and clinical assessments. The result summarizes the degree of consensus without requiring the evaluations to be treated as numerical measurements.
The statistic provides a compact measure of consistency among multiple rankings, making it useful for assessing rating reliability or consensus. Because the procedure converts evaluations into ranks, its focus is the relative ordering of items rather than the original spacing between rating categories. This helps researchers summarize agreement in ordinal-data studies.