Related Experiment Videos
Correlation coefficients in medical research: from product moment correlation to the odds ratio
1Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, MC5717 Stanford, CA 94305, USA. hck@stanford.edu
Objective:
Presentation of effect sizes that can be interpreted in terms of clinical or practical significance is currently urged whenever statistical significance (a 'p-value') is reported in research journals. However, which effect size and how to interpret it are not yet clearly delineated. The present focus is on effect sizes indicating strength of correlation, that is, effect sizes that describe the strength of monotonic association between two random variables X and Y in a population.
Methods:
A logical structure of measures of association is traced, showing the interrelationships among the many measures of association. Advantages and disadvantages of each are discussed.
Conclusions:
Suggestions are made for the future use of measures of association in research to facilitate considerations of clinical significance, emphasizing distribution-free effect sizes such as the Spearman correlation coefficient and Kendall's coefficient of concordance for ordinal versus ordinal associations, weighted and intraclass kappa for binary versus binary associations and risk difference (RD) for binary versus ordinal association.
Related Concept Videos
Odds Ratio
Hazard Ratio
For example, in a clinical trial evaluating a...
Correlations
Coefficient of Correlation
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 strength of the linear...
Correlation and Regression
Comparing the Survival Analysis of Two or More Groups