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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

rXYZ=rXYrXZ×rYZ1rYZ2×1rYZ2,

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

rXY=a×b+c×sEX2×rXYZ×sEY2×d.

Figure 1 Path diagram of partial correlation. Variables X, Y, and Z are standardized

Figure

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, SEX2 and SEY2, 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., c=1rYZ2 and d=1rYZ2.

After substituting terms, Equation 2 now becomes

rXY=rXZ×rYZ+1rYZ2×rXYZ×1rYZ2.

Equation 3 can be rearranged as

rXYZ×1rXZ2×1rYZ2=rXYrXZ×rYZ.

Dividing both sides of this rearrangement by 1rXZ21rYZ2 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. rXYZ2 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 (ηp2) 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

rXYA=RX|YA2RX|A21RX|A2=RY|XA2RY|A21RY|A2,

where A is set of variables, RX|YA2 is the squared multiple correlation from a regression analysis with X being the outcome and Y and A being the predictors, and RX|A2 squared multiple correlation with only A as the predictors (likewise for RY|XA2 and RY|A2 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:

1rp2nk1,

where n is the sample size, k is the number of variables in the correlation minus 1, and rp2 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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