| mdt_within_wide {JSmediation} | R Documentation |
Joint-significance test for simple mediation (wide-format input)
Description
Given a data frame, a predictor (IV), an outcome
(DV), a mediator (M), and a grouping variable (group)
conducts a joint-significant test for within-participant mediation (see
Yzerbyt, Muller, Batailler, & Judd, 2018).
Usage
mdt_within_wide(data, DV_A, DV_B, M_A, M_B)
Arguments
data |
a data frame containing the variables in the model. |
DV_A |
an unquoted numeric variable in the data frame which will be used as the dependent variable value for the "A" independent variable condition. |
DV_B |
an unquoted numeric variable in the data frame which will be used as the dependent variable value for the "B" independent variable condition. |
M_A |
an unquoted numeric variable in the data frame which will be used as the mediatior variable value for the "A" independent variable condition. |
M_B |
an unquoted numeric variable in the data frame which will be used as the mediatior variable value for the "b" independent variable condition. |
Details
With within-participant mediation analysis, one tests whether the
effect of X on Y goes through a third variable M. The
specificity of within-participant mediation analysis lies in the repeated
measures design it relies on. With such a design, each sampled unit (e.g.,
participant) is measured on the dependent variable Y and the mediator
M in the two conditions of X. The hypothesis behind this test
is that X has an effect on M (a) which has an effect on
Y (b), meaning that X has an indirect effect on Y
through M.
As with simple mediation, the total effect of X on Y can be
conceptually 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 (Judd,
Kenny, & McClelland, 2001; Montoya & Hayes, 2011).
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
|
Data formatting
To be consistent with other mdt_* family
functions, mdt_within takes a long-format data frame as data
argument. With this kind of format, each sampled unit has two rows, one for
the first within-participant condition and one for the second
within-participant condition. In addition, each row has one observation for
the outcome and one observation for the mediator (see
dohle_siegrist for an example.
Because such formatting is not the most common among social scientists
interested in within-participant mediation, JSmediation contains the
mdt_within_wide function which handles wide-formatted data
input (but is syntax-inconsistent with other mdt_* family
functions).
Variable coding
Models underlying within-participant mediation use
difference scores as DV (see Models section). mdt_within_wide uses
M_A - M_B and DV_A - DV_B in these
models.
Models
For within-participant mediation, three models will be fitted:
-
Y_{2i} - Y_{1i} = c_{11} -
M_{2i} - M_{1i} = a_{21} -
Y_{2i} - Y_{1i} = c'_{31} + b_{32}(M_{2i} - M_{1i}) + d_{33}[0.5(M_{1i} + M_{2i}) - 0.5(\overline{M_{1} + M_{2}})]
with Y_{2i} - Y_{1i} the difference score between DV
conditions for the outcome variable for the ith observation,
M_{2i} - M_{1i} the difference score between DV conditions
for the mediator variable for the ith observation, M_{1i} +
M_{2i} the sum of mediator variables values for DV conditions
for the ith observation, and \overline{M_{1} + M_{2}} the mean sum of mediator variables values for DV conditions across
observations (see Montoya & Hayes, 2011).
Coefficients associated with a, b, c, and c' paths
are respectively a_{21}, b_{32}, c_{11},
and c'_{31}.
References
Judd, C. M., Kenny, D. A., & McClelland, G. H. (2001). Estimating and testing mediation and moderation in within-subject designs. Psychological Methods, 6(2), 115-134. doi: 10.1037//1082-989X.6.2.115
Montoya, A. K., & Hayes, A. F. (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22(1), 6-27. doi: 10.1037/met0000086
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