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Repeated measurements made on a single individual are likely to be highly correlated and therefore the use of the usual ��post-hoc�� tests is questionable. Nevertheless, it is possible to partition the variance attributable to main effects and interaction into ��contrasts��. In a repeated measures design, the shape of the ��response curve��, i.e., the regression of the measured variable on time, may be of particular interest. A significant interaction between the main-plot http://www.selleckchem.com/products/Adriamycin.html factor and the repeated measure would indicate that the response curve varied at different levels of the main-plot factor. In a longitudinal study of vergence in incipient presbyopia, repeated measures anova was used to show that with decline in amplitude of accommodation, there was a statistically significant reduction in magnitude of vergence adaptation to both base-out and base-in prism.32 Testing the degree of ��correlation�� between two variables (Y,X or X1,X2) is one of the most commonly used of all statistical methods.7,33 A test of correlation establishes whether there is a ��linear�� relationship between two different variables and the most widely used statistic is Pearson��s correlation coefficient ��r��.34 Nevertheless, Pearson��s ��r�� is often misinterpreted in studies. First, the square of the correlation coefficient ��r2�� also known as the ��coefficient of determination��, measures the proportion of the variance http://www.selleck.cn/products/dabrafenib-gsk2118436.html associated with one of the variables that can be accounted for or ��explained�� by the other variable. When large numbers of pairs of observations are present, e.g., N?>?50 pairs, the value of ��r�� although significant may be so low that one variable may account for a very small proportion of the variance in the other. For example, in the study of Nomura et?al.,35 IOP in the Japanese population was correlated inversely with age in men (r?=??0.14, p? http://www.selleckchem.com/products/DAPT-GSI-IX.html observed in observational studies of large numbers of variables in which the objective may be to ��explain�� the source of variation in one of the variables. A number of X variables may be correlated with a Y variable but each may account for only a small proportion of the total variance. Despite their statistical significance, correlations of small magnitude are not of practical value because they account for little of the total variability. Second, care is needed to ensure that only homogeneous groups are included in the correlation. Fourth, in many correlation studies a significant value of ��r�� does not imply that there is a ��causal�� relationship between the two variables.7 If the data are not parametric, then a non-parametric correlation coefficient may be more appropriate.