mdt_within_wide: Joint-significance test for simple mediation (wide-format...

In JSmediation: Mediation Analysis Using Joint Significance

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., "simple mediation").
method	A character string containing the approach that has been used to conduct the mediation analysis (usually "joint significance").
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 FALSE. See add_index to compute mediation index.
indirect_index_infos	(Optional) An object of class "indirect_index". Appears when one applies add_index to an object of class "mediation_model".
js_models	A list of objects of class "lm". Contains every model relevant to joint-significance testing.
data	The original data frame that has been passed through data argument.

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:

• 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.

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

JSmediation documentation built on Feb. 16, 2023, 7:25 p.m.