modmed14 {pwr2ppl}

Compute Power for Conditional Process Model 14 Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power

Description

Compute Power for Conditional Process Model 14 Joint Significance Requires correlations between all variables as sample size. This is the recommended approach for determining power

Usage

modmed14(
  rxw,
  rxm,
  rxxw = 0,
  rxy,
  rwm = 0,
  rxww = 0,
  rwy,
  rxwm = 0,
  rxwy,
  rmy,
  n,
  alpha = 0.05,
  rep = 5000
)

Arguments

rxw

Correlation between predictor (x) and moderator (w)

rxm

Correlation between predictor (x) and mediator (m)

rxxw

Correlation between predictor (x) and xweraction term (xw) - defaults to 0

rxy

Correlation between DV (y) and predictor (x)

rwm

Correlation between moderator (w) and mediator (m)

rxww

Correlation between moderator (w) and xweraction (xw) - defaults to 0

rwy

Correlation between DV (y) and moderator (w)

rxwm

Correlation between mediator (m) and xweraction (xw) - Key value

rxwy

Correlation between DV (y) and xweraction (xw) - defaults to 0

rmy

Correlation between DV (y) and mediator (m)

n

Sample size

alpha

Type I error (default is .05)

rep

Number of samples drawn (defaults to 5000)

Value

Power for Model 14 Conditional Processes

Examples

modmed14(rxw=.2, rxm=.2, rxy=.31,rwy=.35, rxwy=.2,
rmy=.32, n=200, rep=1000,alpha=.05)