ci.indirect: Confidence interval for an indirect effect

In statpsych: Statistical Methods for Psychologists

Confidence interval for an indirect effect

Description

Computes a Monte Carlo confidence interval (500,000 trials) for a population unstandardized indirect effect in a path model and a Sobel standard error. This function is not recommended for a standardized indirect effect. The Monte Carlo method is general in that the slope estimates and standard errors do not need to be OLS estimates with homoscedastic standard errors. For example, LAD slope estimates and their standard errors, OLS slope estimates and heteroscedastic-consistent standard errors, and (in models with no direct effects) distribution-free Theil-Sen slope estimates with recovered standard errors also could be used.

Usage

ci.indirect(alpha, b1, b2, se1, se2)

Arguments

alpha	alpha level for 1-alpha confidence
b1	unstandardized slope estimate for first path
b2	unstandardized slope estimate for second path
se1	standard error for b1
se2	standard error for b2

Value

Returns a 1-row matrix. The columns are:

• Estimate - estimated indirect effect

• SE - standard error

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

Examples

ci.indirect (.05, 2.48, 1.92, .586, .379)

# Should return (within sampling error):
#      Estimate       SE       LL       UL
# [1,]   4.7616 1.625282 2.178812 7.972262
 

statpsych documentation built on July 9, 2023, 6:50 p.m.