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When data are skewed, the between article variance, i.e. σarticle, is low, and thus the kappa will be low.
Low dispersal rates strongly influence the kappa, because if the variance between articles is low, the error variance is large in relation to the article variance.
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To the Editor: We read with great interest the article "Variances in Sagittal Femoral Shaft Bowing in Patients Undergoing TKA" by Yehyawi et al.
For these articles, variances were taken as the average variance of all other studies.
The ICC was defined as the ratio of variance in quality of reporting of articles (article variability) over the total variance (including article variability, assessment variability, random error variability).
The bootstrap method based on the replication approach was used in this article for the variance estimation.
The high standard deviations reported in our study are consistent with the standard deviations of the cited articles, suggesting great variance and heterogeneity in both samples.
In the computer simulation and real data example shown in this article, the explained variances are expressed as proportions, and the S1, S2 and S3 statistics divided by eight so that they could vary between zero and one.
The explanatory power of the model (R2) is 0.14, suggesting that the model explains 14%% of the variance in article citation counts in the 774 transportation articles.
In this article we compare two variance estimators for such surveys.
The variance of article was 1.46, and the variance of journal was 0.00 in the final model; the intercept was equal to –2.53 (95% CI [ 3.00, –2.06]).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com