[PDF][PDF] Value of r (2) in statistical analysis by Pearson correlation coefficient

S Mondal, H Mondal - J Clin Diagn Res, 2017 - academia.edu
J Clin Diagn Res, 2017academia.edu
We read a research article contributed by Usha Shenoy and Jagadamba in April 2017 issue
of your journal [1]. After reading the abstract, we were attracted to the article to know more
about increment of lung age along with increased degree of central obesity. However, as we
proceeded further in results section of the study, we found some uncommon interpretation of
correlation coefficient in the article. Hence, we intended to share our views about the
interpretation of Pearson correlation coefficient with journal readers. In the result section of …
We read a research article contributed by Usha Shenoy and Jagadamba in April 2017 issue of your journal [1]. After reading the abstract, we were attracted to the article to know more about increment of lung age along with increased degree of central obesity. However, as we proceeded further in results section of the study, we found some uncommon interpretation of correlation coefficient in the article. Hence, we intended to share our views about the interpretation of Pearson correlation coefficient with journal readers.
In the result section of the study, authors stated that there was a “weak positive” correlation between Conicity Index (CI) and lung age in obese subjects. In presented data ([Table/Fig-1] in said article), the correlation coefficient between CI and lung age was r= 0.098. In addition, researchers found a “negative weak” correlation between CI and lung age in non-obese subjects. In that case, correlation coefficient was r=-0.023. Furthermore, authors stated r= 0.020 as “weak positive” and r= 0.141 as “significant positive” correlation. Authors could be more cautious during interpretation of correlation coefficient as any correlation coefficient (r)< 0.20 is commonly considered “very weak” or often “negligible”[2].
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