Partial Corr Notes Output Created 13-FEB-2024 22:31:15 Comments Input Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data File 9282 Missing Value Handling Definition of Missing User defined missing values are treated as missing. Cases Used Statistics are based on cases with no missing data for any variable listed. Syntax PARTIAL CORR /VARIABLES=AGE PSYCHO BY ABUSE /SIGNIFICANCE=TWOTAIL /MISSING=LISTWISE. Resources Processor Time 00:00:00.05 Elapsed Time 00:00:00.04 Correlations Control Variables Age Number of psychotic symptoms Frequency of childhood abuse experiences Age Correlation 1.000 -.021 Significance (2-tailed) . .043 df 0 9148 Number of psychotic symptoms Correlation -.021 1.000 Significance (2-tailed) .043 . df 9148 0 Given the partial correlation, is the correlation confounded by the control variable? Explain why or why not. If you computed a partial correlation between lifespan and exercise, controlling for genetics, it would subtract the correlations of genetics with both exercise and lifespan from their correlation with each other. If the partial r is lower than their original r, then genetics confounded their correlation; the correlation between exercise and lifespan is lower after the effects of genetics have been removed. If the original and the partial r are similar, then the control variable, genetics did not confound the correlation of exercise and longevity