User Guide

Chapter
24
Partial Corr
elations
The Partial Correlations procedure computes partial correlation coefficients that
describe th
e linear relationship between two variables while controlling for the
effects of one or more additional variables. Correlations are measures of linear
association. Two variables can be perfectly related, but if the relationship is not linear,
acorrelat
ion coefficient is not an appropriate statistic for measuring their association.
Example. Is there a relationship between healthcare funding and disease rates?
Although you might expect any such relationship to be a negative one, a study reports
asignifi
cant positive correlation: as healthcare funding increases, disease rates appear
to increase. Controlling for the rate of visits to healthcare providers, however, virtually
eliminates the observed positive correlation. healthcare funding and disease rates only
appear to
be positively related because more people have access to healthcare when
funding increases, which leads to more reported diseases by doctors and hospitals.
Statistics. For each variable: number of cases with nonmissing values, mean, and
standar
d deviation. Partial and zero-order correlation matrices, with degrees of
freedom and significance levels.
Data. Use symmetric, quantitative variables.
Assumpt
ions.
The Partial Correlations procedure assumes that each pair of variables is
bivariate normal.
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