Differences In Conditional Indirect Effects

Compute the difference in conditional indirect effects between two sets of levels of the moderators.

Usage

cond_indirect_diff(output, from = NULL, to = NULL, level = 0.95)

Arguments

output: A cond_indirect_effects-class object: The output of cond_indirect_effects().
from: A row number of output.
to: A row number of output. The change in indirect effects is computed by the change in the level(s) of the moderator(s) from Row from to Row to.
level: The level of confidence for the confidence interval. Default is .95.

Value

A cond_indirect_diff-class object. This class has a print method (print.cond_indirect_diff()), a coef method (coef.cond_indirect_diff()), and a confint method (confint.cond_indirect_diff()).

Details

Ths function takes the output of cond_indirect_effects() and computes the difference in conditional indirect effects between any two rows, that is, between levels of the moderator, or two sets of levels of the moderators when the path has more than one moderator.

The difference is meaningful when the difference between the two levels or sets of levels are meaningful. For example, if the two levels are the mean of the moderator and one standard deviation above mean of the moderator, then this difference is the change in indirect effect when the moderator increases by one standard deviation.

If the two levels are 0 and 1, then this difference is the index of moderated mediation as proposed by Hayes (2015). (This index can also be computed directly by index_of_mome(), designed specifically for this purpose.)

The function can also compute the change in the standardized indirect effect between two levels of a moderator or two sets of levels of the moderators.

This function is intended to be a general purpose function that allows users to compute the difference between any two levels or sets of levels that are meaningful in a context.

This function itself does not set the levels of comparison. The levels to be compared need to be set when calling cond_indirect_effects(). This function extracts required information from the output of cond_indirect_effects().

If bootstrap or Monte Carlo estimates are available in the input or bootstrap or Monte Carlo confidence intervals are requested in calling cond_indirect_effects(), cond_indirect_diff() will also form the bootstrap confidence interval for the difference in conditional indirect effects using the stored estimates.

If bootstrap confidence interval is to be formed and both effects used the same type of interval, then that type will be used. Otherwise, percentile confidence interval will be formed.

Functions

cond_indirect_diff(): Compute the difference in in conditional indirect effect between two rows in the output of cond_indirect_effects().

References

Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1-22. doi:10.1080/00273171.2014.962683

Examples


library(lavaan)
dat <- modmed_x1m3w4y1
dat$xw1 <- dat$x * dat$w1
mod <-
"
m1 ~ a * x  + f * w1 + d * xw1
y  ~ b * m1 + cp * x
"
fit <- sem(mod, dat,
           meanstructure = TRUE, fixed.x = FALSE,
           se = "none", baseline = FALSE)
est <- parameterEstimates(fit)

# Create levels of w1, the moderators
w1levels <- mod_levels("w1", fit = fit)
w1levels
#>                 w1
#> M+1.0SD  1.2280576
#> Mean     0.2589999
#> M-1.0SD -0.7100578

# Conditional effects from x to y when w1 is equal to each of the levels
boot_out <- fit2boot_out_do_boot(fit, R = 40, seed = 4314, progress = FALSE)
out <- cond_indirect_effects(x = "x", y = "y", m = "m1",
                             wlevels = w1levels, fit = fit,
                             boot_ci = TRUE, boot_out = boot_out)
out
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m1 -> y
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]   (w1)   ind  CI.lo CI.hi Sig  m1~x  y~m1
#> 1 M+1.0SD  1.228 0.068 -0.776 0.534     0.750 0.091
#> 2 Mean     0.259 0.048 -0.522 0.377     0.523 0.091
#> 3 M-1.0SD -0.710 0.027 -0.267 0.221     0.297 0.091
#> 
#>  - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#>    nonparametric bootstrapping with 40 samples.
#>  - The 'ind' column shows the conditional indirect effects.
#>  - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#>    conditional on the moderator(s).
#> 
out_ind <- cond_indirect_diff(out, from = 2, to = 1)
out_ind
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m1 -> y
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]  (w1)   ind  CI.lo CI.hi Sig  m1~x  y~m1
#> 1 M+1.0SD 1.228 0.068 -0.776 0.534     0.750 0.091
#> 2 Mean    0.259 0.048 -0.522 0.377     0.523 0.091
#> 
#> == Difference in Conditional Indirect Effect ==
#> 
#> Levels: 
#>               w1   
#> To:   M+1.0SD 1.228
#> From: Mean    0.259
#> 
#> Levels compared: Row 1 - Row 2
#> 
#> Change in Indirect Effect:
#> 
#>        x y Change  CI.lo CI.hi
#> Change x y  0.021 -0.255 0.166
#> 
#>  - [CI.lo, CI.hi]: 95% percentile confidence interval.
#> 
coef(out_ind)
#>    y~m1~x 
#> 0.0206078 
confint(out_ind)
#>             2.50%    97.50%
#> y~m1~x -0.2547857 0.1662861