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Partial Correlations
A partial correlation is a measure of linear association between two variables after variability in at least one other variable is removed from both variables. The traditional formula for the partial correlation between, say, variables X and Y after controlling for Z, denoted rXY·Z, is
where r is the Pearson correlation between two variables.
rX(Y·Z) is sometimes referred to as a first-order partial correlation to note that the correlation only controls for one other variable. If controlling for two variables, it would be a second-order, and so on. Zero-order correlations do not control for any other variables.
While not obvious from Equation 1, the partial correlation is really the correlation between the residuals of X and Y after regressing both variables on Z. A path model representing this interpretation is shown in Figure 1, which assumes X, Y, and Z are standardized variables.
In Figure 1, the zero-order correlation between X and Y, rXY, can be found using the tracing rules for path diagrams. It is
Figure 1 Path diagram of partial correlation. Variables X, Y, and Z are standardized

The values for paths a and b are the zero-order correlations of X with Z and Y with Z, respectively. To make the model identified (i.e., be able to find unique estimates for the paths), either c and d or the variances of Ex and Ey, and , need to be constrained. To make rXY·Z a correlation (as opposed to a covariance), constrain the variances of Ex and Ey to one (i.e., standardize these residuals). This makes the paths c and d equal the square root of the variance in X and Y not explained by Z (i.e., and .
After substituting terms, Equation 2 now becomes
Equation 3 can be rearranged as
Dividing both sides of this rearrangement by produces Equation 1.
Partial Correlation Extensions
As with zero-order correlations, partial correlations can be squared to give the amount of variance in common between two variables. indicates the variance in common between X and Y after removing what they both share with Z. It is analogous to the partial eta squared effect size often used in analysis of variance models.
If removing the variance of more than one variable (e.g., second order, third order), then Equation 1 can be generalized to
where A is set of variables, is the squared multiple correlation from a regression analysis with X being the outcome and Y and A being the predictors, and squared multiple correlation with only A as the predictors (likewise for and except Y is the outcome variable).
There are different ways to calculate the standard error of the partial correlation. One method is to use Fisher’s z transformation, as is often done with zero-order correlations. Another method is to apply the standard error formula for standardized regression coefficients:
where n is the sample size, k is the number of variables in the correlation minus 1, and is the squared partial correlation.
Example
Some didactic data are provided in Table 1. The variables are IQ, academic motivation, and GPA for six students. All variables were scaled to have a population mean of 10 and standard deviation of 3. The zero-order correlations are below the diagonal in Table 2. For example, the motivation-GPA correlation is .46. To calculate the motivation-GPA correlation after controlling for IQ, plug the appropriate values into Equation
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