ssMediation.Sobel {powerMediation} | R Documentation |
Sample size for testing mediation effectd (Sobel's test)
Description
Calculate sample size for testing mediation effect based on Sobel's test.
Usage
ssMediation.Sobel(power,
theta.1a,
lambda.a,
sigma.x,
sigma.m,
sigma.epsilon,
n.lower = 1,
n.upper = 1e+30,
alpha = 0.05,
verbose = TRUE)
Arguments
power |
power of the test. |
theta.1a |
regression coefficient for the predictor in the linear regression linking
the predictor |
lambda.a |
regression coefficient for the mediator in the linear regression linking
the predictor |
sigma.x |
standard deviation of the predictor. |
sigma.m |
standard deviation of the mediator. |
sigma.epsilon |
standard deviation of the random error term
in the linear regression linking
the predictor |
n.lower |
lower bound of the sample size. |
n.upper |
upper bound of the sample size. |
alpha |
type I error rate. |
verbose |
logical. |
Details
The sample size is for testing the null hypothesis \theta_1\lambda=0
versus the alternative hypothesis \theta_{1a}\lambda_a\neq 0
for the linear regressions:
m_i=\theta_0+\theta_{1a} x_i + e_i, e_i\sim N(0, \sigma^2_e)
y_i=\gamma+\lambda_a m_i+ \lambda_2 x_i + \epsilon_i, \epsilon_i\sim N(0, \sigma^2_{\epsilon})
Test statistic is based on Sobel's (1982) test:
Z=\frac{\hat{\theta}_{1a}\hat{\lambda_a}}{\hat{\sigma}_{\theta_{1a}\lambda_a}}
where \hat{\sigma}_{\theta_{1a}\lambda_a}
is the estimated standard deviation
of the estimate \hat{\theta}_{1a}\hat{\lambda_a}
using multivariate
delta method:
\sigma_{\theta_{1a}\lambda_a}=\sqrt{\theta_{1a}^2\sigma_{\lambda_a}^2+\lambda_a^2\sigma_{\theta_{1a}}^2}
and
\sigma_{\theta_{1a}}^2=\sigma_e^2/(n\sigma_x^2)
is the
variance
of the estimate \hat{\theta}_{1a}
, and
\sigma_{\lambda_a}^2=\sigma_{\epsilon}^2/(n\sigma_m^2(1-\rho_{mx}^2))
is the variance
of the estimate \hat{\lambda_a}
, \sigma_m^2
is the variance of the
mediator m_i
.
From the linear regression m_i=\theta_0+\theta_{1a} x_i+e_i
, we have the
relationship \sigma_e^2=\sigma_m^2(1-\rho^2_{mx})
. Hence, we can simply
the variance \sigma_{\theta_{1a}, \lambda_a}
to
\sigma_{\theta_{1a}\lambda_a}=\sqrt{\theta_{1a}^2\frac{\sigma_{\epsilon}^2}{n\sigma_m^2(1-\rho_{mx}^2)}+\lambda_a^2\frac{\sigma_{m}^2(1-\rho_{mx}^2)}{n\sigma_x^2}}
Value
n |
sample size. |
res.uniroot |
results of optimization to find the optimal sample size. |
Note
The test is a two-sided test. For one-sided tests, please double the
significance level. For example, you can set alpha=0.10
to obtain one-sided test at 5% significance level.
Author(s)
Weiliang Qiu stwxq@channing.harvard.edu
References
Sobel, M. E. Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology. 1982;13:290-312.
See Also
powerMediation.Sobel
,
testMediation.Sobel
Examples
ssMediation.Sobel(power=0.8, theta.1a=0.1701, lambda.a=0.1998,
sigma.x=0.57, sigma.m=0.61, sigma.epsilon=0.2,
alpha = 0.05, verbose = TRUE)