compute_indirect_effect_for {JSmediation} | R Documentation |
Compute the indirect effect index for a specific value of the moderator
Description
When computing a moderated mediation, one assesses whether an indirect
effect changes according a moderator value (Muller et al., 2005).
mdt_moderated
makes it easy to assess moderated mediation, but it does
not allow accessing the indirect effect for a specific moderator values.
compute_indirect_effect_for
fills this gap.
Usage
compute_indirect_effect_for(
mediation_model,
Mod = 0,
times = 5000,
level = 0.05
)
Arguments
mediation_model |
A moderated mediation model fitted with
|
Mod |
The moderator value for which to compute the indirect effect. Must
be a numeric value, defaults to |
times |
Number of simulations to use to compute the Monte Carlo indirect
effect confidence interval. Must be numeric, defaults to |
level |
Alpha threshold to use for the indirect effect's confidence
interval. Defaults to |
Details
The approach used by compute_indirect_effect_for
is similar to the
approach used for simple slope analyses. Specifically, it will fit a new
moderated mediation model, but with a data set with a different variable
coding. Behind the scenes, compute_indirect_effect_for
adjusts the
moderator variable coding, so that the value we want to compute the
indirect effect for is now 0
.
Once done, a new moderated mediation model is applied using the new data
set. Because of the new coding, and because of how one interprets
coefficients in a linear regression, a \times b
is now the
indirect effect we wanted to compute (see the Models section).
Thanks to the returned values of a
and b
(b_51
and b_64
, see the Models section), it is now
easy to compute a \times b
. compute_indirect_effect_for
uses
the same approach than the add_index
function. A Monte Carlo simulation
is used to compute the indirect effect index (MacKinnon et al., 2004).
Models
In a moderated mediation model, three models are used.
compute_indirect_effect_for
uses the same model specification as
mdt_moderated
:
-
Y_i = b_{40} + \mathbf{b_{41}} X_i + b_{42} Mo_i + \mathbf{b_{43}} XMo_i
-
M_i = b_{50} + \mathbf{b_{51}} X_i + b_{52} Mo_i + \mathbf{b_{53} XMo_i}
-
Y_i = b_{60} + \mathbf{c'_{61}} X_i + b_{62} Mo_i + \mathbf{b_{63} Xmo_i} + \mathbf{b_{64} Me_i} + \mathbf{b_{65} MeMo_i}
with Y_i
, the outcome value for the ith observation,
X_i
, the predictor value for the ith observation,
Mo_i
, the moderator value for the ith observation, and
M_i
, the mediator value for the ith observation.
Coefficients associated with a
, a \times Mod
, b
,
b \times Mod
, c
, c \times Mod
,
c'
, and c' \times Mod
, paths are respectively
b_{51}
, b_{53}
, b_{64}
,
b_{65}
, b_{41}
, b_{43}
,
b_{61}
, and b_{63}
(see Muller et al., 2005).
References
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods. Multivariate Behavioral Research, 39(1), 99-128. doi: 10.1207/s15327906mbr3901_4
Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89(6), 852-863. doi: 10.1037/0022-3514.89.6.852
Examples
# compute an indirect effect index for a specific value in a moderated
# mediation.
data(ho_et_al)
ho_et_al$condition_c <- build_contrast(ho_et_al$condition,
"Low discrimination",
"High discrimination")
ho_et_al <- standardize_variable(ho_et_al, c(linkedfate, sdo))
moderated_mediation_model <- mdt_moderated(data = ho_et_al,
DV = hypodescent,
IV = condition_c,
M = linkedfate,
Mod = sdo)
compute_indirect_effect_for(moderated_mediation_model, Mod = 0)