manymome (version 0.2.3)

cond_indirect_diff: Differences In Conditional Indirect Effects

Description

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)

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()).

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.

Functions

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

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.

References

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

See Also

index_of_mome() for computing the index of moderated mediation, index_of_momome() for computing the index of moderated moderated mediation, cond_indirect_effects(), mod_levels(), and merge_mod_levels() for preparing the levels to be compared.

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

# 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
out_ind <- cond_indirect_diff(out, from = 2, to = 1)
out_ind
coef(out_ind)
confint(out_ind)