| mdt_simple {JSmediation} | R Documentation |
Joint-significance test for simple mediation
Description
Given a data frame, a predictor (IV), an outcome
(DV), and a mediator (M), conducts a joint-significant test
for simple mediation (see Yzerbyt, Muller, Batailler, & Judd, 2018).
Usage
mdt_simple(data, IV, DV, M)
Arguments
data |
A data frame containing the variables to be used in the model. |
IV |
An unquoted numeric variable in the data frame which will be used as independent variable. |
DV |
An unquoted numeric variable in the data frame which will be used as dependent variable. |
M |
An unquoted numeric variable in the data frame which will be used as mediator. |
Details
With simple mediation analysis, one is interested in finding if the
effect of X on Y goes through a third variable M. The
hypothesis behind this test is that X has an effect on M
(a) that has an effect on Y (b), meaning that X
has an indirect effect on Y through M.
The total effect of X on Y can be described as follows:
c = c' + ab
with c the total effect of X on Y, c' the direct of
X on Y, and ab the indirect effect of X on Y
through M (see Models section).
To assess whether the indirect effect is different from the null, one has
to assess the significance against the null for both a (the effect of
X on M) and b (effect of M on Y
controlling for the effect of X). Both a and b need to
be simultaneously significant for an indirect effect to be claimed (Cohen &
Cohen, 1983; Yzerbyt, Muller, Batailler, & Judd, 2018).
Value
Returns an object of class "mediation_model".
An object of class "mediation_model" is a list containing at least
the components:
type |
A character string containing the type of model that has been
conducted (e.g., |
method |
A character string containing the approach that has been
used to conduct the mediation analysis (usually
|
params |
A named list of character strings describing the variables used in the model. |
paths |
A named list containing information on each relevant path of the mediation model. |
indirect_index |
A boolean indicating whether an indirect effect index
has been computed or not. Defaults to |
indirect_index_infos |
(Optional) An object of class
|
js_models |
A list of objects of class |
data |
The original data frame that has been passed through
|
Models
In a simple mediation model, three models will be fitted:
-
Y_i = b_{10} + \mathbf{c_{11}} X_i -
M_i = b_{20} + \mathbf{a_{21}} X_i -
Y_i = b_{30} + \mathbf{c'_{31}} X_i + \mathbf{b_{32}} M_i
with Y_i, the outcome value for the ith observation,
X_i, the predictor value for the ith observation, and
M_i, the mediator value for the ith observation (Cohen &
Cohen, 1983; Yzerbyt, Muller, Batailler, & Judd, 2018).
Coefficients associated with a, b, c, and c' paths
are respectively a_{21}, b_{32},
c_{11}, and c'_{31}.
Variable coding
Because joint-significance tests uses linear models
behind the scenes, variables involved in the model have to be numeric.
mdt_simple will give an error if non-numeric variables are
specified in the model.
To convert a dichotomous categorical variable to a numeric one, please
refer to the build_contrast function.
References
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed). Hillsdale, N.J: L. Erlbaum Associates.
Yzerbyt, V., Muller, D., Batailler, C., & Judd, C. M. (2018). New recommendations for testing indirect effects in mediational models: The need to report and test component paths. Journal of Personality and Social Psychology, 115(6), 929–943. doi: 10.1037/pspa0000132
See Also
Other mediation models:
mdt_moderated(),
mdt_within()
Examples
## fit a simple mediation model
data(ho_et_al)
ho_et_al$condition_c <- build_contrast(ho_et_al$condition,
"Low discrimination",
"High discrimination")
mdt_simple(data = ho_et_al,
IV = condition_c,
DV = hypodescent,
M = linkedfate)