Monte Carlo Method for Indirect Effect in a Simple Mediation Model Assuming \(\alpha\) and \(\beta\) Follow a \(t\) Distribution (Sampling Distribution)

In this method \(\alpha\) and \(\beta\) are assumed to follow a \(t\) distribution with \(df = n - 2\) and \(df = n - 3\) respectively.

mc.t(R = 20000L, alphahat, sehatalphahat, betahat, sehatbetahat, n)

Arguments

R	Integer. Monte Carlo replications.
alphahat	Numeric. Estimated slope of path from x to m \(\left( \hat{\alpha} \right)\) .
sehatalphahat	Numeric. Estimated standard error of slope of path from x to m \(\left( \widehat{se}_{\hat{\alpha}} \right)\) .
betahat	Numeric. Estimated slope of path from m to y \(\left( \hat{\beta} \right)\) .
sehatbetahat	Numeric. Estimated standard error of slope of path from m to y \(\left( \widehat{se}_{\hat{\beta}} \right)\) .
n	Integer. Sample size.

See also

Other monte carlo method functions: beta_ols_mc.mvn_pcci_simulation(), beta_ols_mc.mvn_pcci_task(), beta_ols_mc.mvn_simulation(), beta_ols_mc.mvn_task(), beta_ols_mc.mvn(), exp_ols_mc.mvn_pcci_simulation(), exp_ols_mc.mvn_pcci_task(), exp_ols_mc.mvn_simulation(), exp_ols_mc.mvn_task(), exp_ols_mc.mvn(), mc.mvn(), mc.wishart(), mvn_mar_10_mc.mvn_pcci_simulation(), mvn_mar_10_mc.mvn_pcci_task(), mvn_mar_10_mc.mvn_simulation(), mvn_mar_10_mc.mvn_task(), mvn_mar_10_mc.mvn(), mvn_mar_20_mc.mvn_pcci_simulation(), mvn_mar_20_mc.mvn_pcci_task(), mvn_mar_20_mc.mvn_simulation(), mvn_mar_20_mc.mvn_task(), mvn_mar_20_mc.mvn(), mvn_mar_30_mc.mvn_pcci_simulation(), mvn_mar_30_mc.mvn_pcci_task(), mvn_mar_30_mc.mvn_simulation(), mvn_mar_30_mc.mvn_task(), mvn_mar_30_mc.mvn(), mvn_mcar_10_mc.mvn_pcci_simulation(), mvn_mcar_10_mc.mvn_pcci_task(), mvn_mcar_10_mc.mvn_simulation(), mvn_mcar_10_mc.mvn_task(), mvn_mcar_10_mc.mvn(), mvn_mcar_20_mc.mvn_pcci_simulation(), mvn_mcar_20_mc.mvn_pcci_task(), mvn_mcar_20_mc.mvn_simulation(), mvn_mcar_20_mc.mvn_task(), mvn_mcar_20_mc.mvn(), mvn_mcar_30_mc.mvn_pcci_simulation(), mvn_mcar_30_mc.mvn_pcci_task(), mvn_mcar_30_mc.mvn_simulation(), mvn_mcar_30_mc.mvn_task(), mvn_mcar_30_mc.mvn(), mvn_mnar_10_mc.mvn_pcci_simulation(), mvn_mnar_10_mc.mvn_pcci_task(), mvn_mnar_10_mc.mvn_simulation(), mvn_mnar_10_mc.mvn_task(), mvn_mnar_10_mc.mvn(), mvn_mnar_20_mc.mvn_pcci_simulation(), mvn_mnar_20_mc.mvn_pcci_task(), mvn_mnar_20_mc.mvn_simulation(), mvn_mnar_20_mc.mvn_task(), mvn_mnar_20_mc.mvn(), mvn_mnar_30_mc.mvn_pcci_simulation(), mvn_mnar_30_mc.mvn_pcci_task(), mvn_mnar_30_mc.mvn_simulation(), mvn_mnar_30_mc.mvn_task(), mvn_mnar_30_mc.mvn(), mvn_ols_mc.mvn_pcci_simulation(), mvn_ols_mc.mvn_pcci_task(), mvn_ols_mc.mvn_simulation(), mvn_ols_mc.mvn_task(), mvn_ols_mc.mvn(), mvn_sem_mc.mvn_pcci_simulation(), mvn_sem_mc.mvn_pcci_task(), mvn_sem_mc.mvn_simulation(), mvn_sem_mc.mvn_task(), mvn_sem_mc.mvn(), mvn_std_mc.mvn.delta_pcci_simulation(), mvn_std_mc.mvn.delta_pcci_task(), mvn_std_mc.mvn.delta_simulation(), mvn_std_mc.mvn.delta_task(), mvn_std_mc.mvn.delta(), mvn_std_mc.mvn.sem_pcci_simulation(), mvn_std_mc.mvn.sem_pcci_task(), mvn_std_mc.mvn.sem_simulation(), mvn_std_mc.mvn.sem_task(), mvn_std_mc.mvn.sem(), mvn_std_mc.mvn.tb_pcci_simulation(), mvn_std_mc.mvn.tb_pcci_task(), mvn_std_mc.mvn.tb_simulation(), mvn_std_mc.mvn.tb_task(), mvn_std_mc.mvn.tb(), mvn_std_mc.wishart_pcci_simulation(), mvn_std_mc.wishart_pcci_task(), mvn_std_mc.wishart_simulation(), mvn_std_mc.wishart_task(), mvn_std_mc.wishart(), vm_mod_ols_mc.mvn_pcci_simulation(), vm_mod_ols_mc.mvn_pcci_task(), vm_mod_ols_mc.mvn_simulation(), vm_mod_ols_mc.mvn_task(), vm_mod_ols_mc.mvn(), vm_mod_sem_mc.mvn_pcci_simulation(), vm_mod_sem_mc.mvn_pcci_task(), vm_mod_sem_mc.mvn_simulation(), vm_mod_sem_mc.mvn_task(), vm_mod_sem_mc.mvn(), vm_sev_ols_mc.mvn_pcci_simulation(), vm_sev_ols_mc.mvn_pcci_task(), vm_sev_ols_mc.mvn_simulation(), vm_sev_ols_mc.mvn_task(), vm_sev_ols_mc.mvn(), vm_sev_sem_mc.mvn_pcci_simulation(), vm_sev_sem_mc.mvn_pcci_task(), vm_sev_sem_mc.mvn_simulation(), vm_sev_sem_mc.mvn_task(), vm_sev_sem_mc.mvn()

Author

Ivan Jacob Agaloos Pesigan

Examples

thetahatstar <- mc.t(
  R = 20000L, alphahat = 0.338593, sehatalphahat = 0.12236736,
  betahat = 0.451039, sehatbetahat = 0.14597405,
  n = 20
)
hist(thetahatstar)