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Compute Power for Mediated (Indirect) Effects Using Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power
medjs(
rx1x2 = NULL,
rx1m1,
rx1m2 = NULL,
rx1m3 = NULL,
rx1m4 = NULL,
rx1y,
rx2m1 = NULL,
rx2m2 = NULL,
rx2m3 = NULL,
rx2m4 = NULL,
rx2y,
rym1,
rym2 = NULL,
rym3 = NULL,
rym4 = NULL,
rm1m2 = NULL,
rm1m3 = NULL,
rm1m4 = NULL,
rm2m3 = NULL,
rm2m4 = NULL,
rm3m4 = NULL,
n,
alpha = 0.05,
mvars,
rep = 1000,
pred = 1
)
Power for Mediated (Indirect) Effects
Correlation between first predictor (x1) and second predictor (x2)
Correlation between first predictor (x1) and first mediator (m1)
Correlation between first predictor (x1) and second mediator (m2)
Correlation between first predictor (x1) and third mediator (m3)
Correlation between first predictor (x1) and fourth mediator (m4)
Correlation between DV (y) and first predictor (x1)
Correlation between second predictor (x2) and first mediator (m1)
Correlation between second predictor (x2) and second mediator (m2)
Correlation between second predictor (x2) and third mediator (m3)
Correlation between second predictor (x2) and fourth mediator (m4)
Correlation between DV (y) and second predictor (x2)
Correlation between DV (y) and first mediator (m1)
Correlation between DV (y) and second mediator (m2)
Correlation DV (y) and third mediator (m3)
Correlation DV (y) and fourth mediator (m4)
Correlation first mediator (m1) and second mediator (m2)
Correlation first mediator (m1) and third mediator (m3)
Correlation first mediator (m1) and fourth mediator (m4)
Correlation second mediator (m2) and third mediator (m3)
Correlation second mediator (m2) and fourth mediator (m4)
Correlation third mediator (m3) and fourth mediator (m4)
Sample size
Type I error (default is .05)
Number of Mediators
number of repetitions (1000 is default)
number of predictors (default is one)
medjs(rx1m1=.3, rx1m2=.3, rx1m3=.25, rx1y=-.35, rym1=-.5,rym2=-.5, rym3 = -.5,
rm1m2=.7, rm1m3=.4,rm2m3=.4, mvars=3, n=150)
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