mdt_within | R Documentation |
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).
mdt_within(data, IV, DV, M, grouping, default_coding = TRUE)
data |
a data frame containing the variables in the model. |
IV |
an unquoted variable in the data frame which will be used as the independent variable. |
DV |
an unquoted variable in the data frame which will be used as the dependent variable. |
M |
an unquoted variable in the data frame which will be used as the mediator. |
grouping |
an unquoted variable in the data frame which will be used as the grouping variable. |
default_coding |
should the variable coding be the default? Defaults to
|
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).
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
|
For within-participant mediation, three models will be fitted:
Y2i - Y1i = c11
M2i - M1i = a21
Y2i - Y1i = c'31 + b32 * (M2i + M1i) + d33 * [0.5 * (M1i + M2i) - 0.5 * mean(M1 + M2)]
with Y2i - Y1i the difference score between DV conditions for the outcome variable for the ith observation, M2i - M1i the difference score between DV conditions for the mediator variable for the ith observation, M1i + M2i the sum of mediator variables values for DV conditions for the ith observation, and mean(M1i + M2i) 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 a21, b32, c11, and c'31.
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).
Models underlying within-participant mediation use
difference scores as DV (see Models section). Because the function input
does not allow the user to specify how the difference scores should be
computed, mdt_within
has a default coding.
mdt_within
's default behavior is to compute the difference score so
the total effect (the effect of X on Y) will be positive and
compute the other difference scores accordingly. That is, if
mdt_within
has to use Y_{2i} - Y_{1i} (instead of Y_{1i}
- Y_{2i}) so that c_{11} is positive, it will use M_{2i} -
M_{1i} (instead of M_{1i} - M_{2i} in the other models.
User can choose to have a negative total effect by using the
default_coding
argument.
Note that DV
and M
have to be numeric.
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
Other mediation models:
mdt_moderated()
,
mdt_simple()
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