# Index of Moderated Mediation and Index of Moderated Moderated Mediation

Source:`R/index_mome.R`

`index_of_mome.Rd`

It computes the index of moderated mediation and the index of moderated moderated mediation proposed by Hayes (2015, 2018).

## Usage

```
index_of_mome(
x,
y,
m = NULL,
w = NULL,
fit = NULL,
boot_ci = FALSE,
level = 0.95,
boot_out = NULL,
R = 100,
seed = NULL,
progress = TRUE,
mc_ci = FALSE,
mc_out = NULL,
ci_type = NULL,
ci_out = NULL,
boot_type = c("perc", "bc"),
...
)
index_of_momome(
x,
y,
m = NULL,
w = NULL,
z = NULL,
fit = NULL,
boot_ci = FALSE,
level = 0.95,
boot_out = NULL,
R = 100,
seed = NULL,
progress = TRUE,
mc_ci = FALSE,
mc_out = NULL,
ci_type = NULL,
ci_out = NULL,
boot_type = c("perc", "bc"),
...
)
```

## Arguments

- x
Character. The name of the predictor at the start of the path.

- y
Character. The name of the outcome variable at the end of the path.

- m
A vector of the variable names of the mediator(s). The path goes from the first mediator successively to the last mediator. If

`NULL`

, the default, the path goes from`x`

to`y`

.- w
Character. The name of the moderator.

- fit
The fit object. Can be a lavaan::lavaan object, a list of

`lm()`

outputs, or an object created by`lm2list()`

. It can also be a`lavaan.mi`

object returned by`semTools::runMI()`

or its wrapper, such as`semTools::sem.mi()`

.- boot_ci
Logical. Whether bootstrap confidence interval will be formed. Default is

`FALSE`

.- level
The level of confidence for the bootstrap confidence interval. Default is .95.

- boot_out
If

`boot_ci`

is`TRUE`

, users can supply pregenerated bootstrap estimates. This can be the output of`do_boot()`

. For`indirect_effect()`

and`cond_indirect_effects()`

, this can be the output of a previous call to`cond_indirect_effects()`

,`indirect_effect()`

, or`cond_indirect()`

with bootstrap confidence intervals requested. These stored estimates will be reused such that there is no need to do bootstrapping again. If not supplied, the function will try to generate them from`fit`

.- R
Integer. If

`boot_ci`

is`TRUE`

,`boot_out`

is`NULL`

, and bootstrap standard errors not requested if`fit`

is a lavaan object, this function will do bootstrapping on`fit`

.`R`

is the number of bootstrap samples. Default is 100. For Monte Carlo simulation, this is the number of replications.- seed
If bootstrapping or Monte Carlo simulation is conducted, this is the seed for the bootstrapping or simulation. Default is

`NULL`

and seed is not set.- progress
Logical. Display bootstrapping progress or not. Default is

`TRUE`

.- mc_ci
Logical. Whether Monte Carlo confidence interval will be formed. Default is

`FALSE`

.- mc_out
If

`mc_ci`

is`TRUE`

, users can supply pregenerated Monte Carlo estimates. This can be the output of`do_mc()`

. For`indirect_effect()`

and`cond_indirect_effects()`

, this can be the output of a previous call to`cond_indirect_effects()`

,`indirect_effect()`

, or`cond_indirect()`

with Monte Carlo confidence intervals requested. These stored estimates will be reused such that there is no need to do Monte Carlo simulation again. If not supplied, the function will try to generate them from`fit`

.- ci_type
The type of confidence intervals to be formed. Can be either

`"boot"`

(bootstrapping) or`"mc"`

(Monte Carlo). If not supplied or is`NULL`

, will check other arguments (e.g,`boot_ci`

and`mc_ci`

). If supplied, will override`boot_ci`

and`mc_ci`

.- ci_out
If

`ci_type`

is supplied, this is the corresponding argument. If`ci_type`

is`"boot"`

, this argument will be used as`boot_out`

. If`ci_type`

is`"mc"`

, this argument will be used as`mc_out`

.- boot_type
If bootstrap confidence interval is to be formed, the type of bootstrap confidence interval. The supported types are

`"perc"`

(percentile bootstrap confidence interval, the default and recommended type) and`"bc"`

(bias-corrected, or BC, bootstrap confidence interval).- ...
Arguments to be passed to

`cond_indirect_effects()`

- z
Character. The name of the second moderator, for computing the index of moderated moderated mediation.

## Value

It returns a
`cond_indirect_diff`

-class object.
This class has a `print`

method
(`print.cond_indirect_diff()`

), a
`coef`

method for extracting the
index (`coef.cond_indirect_diff()`

),
and a `confint`

method for extracting
the confidence interval if
available
(`confint.cond_indirect_diff()`

).

## Details

The function
`index_of_mome()`

computes the *index
of moderated mediation* proposed by
Hayes (2015). It supports any path in
a model with one (and only one)
component path moderated. For
example, `x->m1->m2->y`

with `x->m1`

moderated by `w`

. It measures the
change in indirect effect when the
moderator increases by one unit.

The function `index_of_momome()`

computes the *index of moderated
moderated mediation* proposed by
Hayes (2018). It supports any path in
a model, with two component paths
moderated, each by one moderator. For
example, `x->m1->m2->y`

with `x->m1`

moderated by `w`

and `m2->y`

moderated by `z`

. It measures the
change in the index of moderated
mediation of one moderator when the
other moderator increases by one
unit.

## Functions

`index_of_mome()`

: Compute the index of moderated mediation.`index_of_momome()`

: Compute the index of moderated moderated mediation.

## 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

Hayes, A. F. (2018). Partial, conditional, and moderated moderated
mediation: Quantification, inference, and interpretation.
*Communication Monographs, 85*(1), 4-40.
doi:10.1080/03637751.2017.1352100

## Examples

```
library(lavaan)
dat <- modmed_x1m3w4y1
dat$xw1 <- dat$x * dat$w1
mod <-
"
m1 ~ a * x + f * w1 + d * xw1
y ~ b * m1 + cp * x
ind_mome := d * b
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
# R should be at least 2000 or even 5000 in real research.
# parallel is set to TRUE by default.
# Therefore, in research, the argument parallel can be omitted.
out_mome <- index_of_mome(x = "x", y = "y", m = "m1", w = "w1",
fit = fit,
boot_ci = TRUE,
R = 42,
seed = 4314,
parallel = FALSE,
progress = FALSE)
out_mome
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind CI.lo CI.hi Sig m1~x y~m1
#> 1 1 1 0.063 -0.693 0.491 0.697 0.091
#> 2 0 0 0.042 -0.439 0.330 0.463 0.091
#>
#> == Index of Moderated Mediation ==
#>
#> Levels compared: Row 1 - Row 2
#>
#> x y Index CI.lo CI.hi
#> Index x y 0.021 -0.254 0.171
#>
#> - [CI.lo, CI.hi]: 95% percentile confidence interval.
#>
coef(out_mome)
#> y~m1~x
#> 0.02126581
# From lavaan
print(est[19, ], nd = 8)
#> lhs op rhs label est
#> 19 ind_mome := d*b ind_mome 0.02126581
confint(out_mome)
#> 2.50% 97.50%
#> y~m1~x -0.2540199 0.1710191
library(lavaan)
dat <- modmed_x1m3w4y1
dat$xw1 <- dat$x * dat$w1
dat$m1w4 <- dat$m1 * dat$w4
mod <-
"
m1 ~ a * x + f1 * w1 + d1 * xw1
y ~ b * m1 + f4 * w4 + d4 * m1w4 + cp * x
ind_momome := d1 * d4
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
# See the example of index_of_mome on how to request
# bootstrap confidence interval.
out_momome <- index_of_momome(x = "x", y = "y", m = "m1",
w = "w1", z = "w4",
fit = fit)
out_momome
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1, w4
#> Moderator(s) represented by: w1, w4
#>
#> [w1] [w4] (w1) (w4) ind m1~x y~m1
#> 1 1 1 1 1 -0.119 0.697 -0.172
#> 2 1 0 1 0 -0.070 0.697 -0.101
#> 3 0 1 0 1 -0.079 0.463 -0.172
#> 4 0 0 0 0 -0.047 0.463 -0.101
#>
#> == Index of Moderated Moderated Mediation ==
#>
#> Levels compared:
#> (Row 1 - Row 2) - (Row 3 - Row 4)
#>
#> x y Index
#> Index x y -0.017
#>
#>
coef(out_momome)
#> y~m1~x
#> -0.01659799
print(est[32, ], nd = 8)
#> lhs op rhs label est
#> 32 ind_momome := d1*d4 ind_momome -0.01659799
```