mdt_within {JSmediation}R Documentation

Joint-significance test for within-participant mediation

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(data, IV, DV, M, grouping, default_coding = TRUE)

Arguments

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 TRUE.

Details

With within-participant mediation analysis, one tests whether the effect of XX on YY goes through a third variable MM. 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 YY and the mediator MM in the two conditions of XX. The hypothesis behind this test is that XX has an effect on MM (aa) which has an effect on YY (bb), meaning that XX has an indirect effect on YY through MM.

As with simple mediation, the total effect of XX on YY can be conceptually described as follows:

c=c+abc = c' + ab

with cc the total effect of XX on YY, cc' the direct of XX on YY, and abab the indirect effect of XX on YY through MM (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 aa (the effect of XX on MM) and bb (effect of MM on YY controlling for the effect of XX). Both aa and bb 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.

Models

For within-participant mediation, three models will be fitted:

with Y2iY1iY_{2i} - Y_{1i} the difference score between DV conditions for the outcome variable for the ith observation, M2iM1iM_{2i} - M_{1i} the difference score between DV conditions for the mediator variable for the ith observation, M1i+M2iM_{1i} + M_{2i} the sum of mediator variables values for DV conditions for the ith observation, and M1+M2\overline{M_{1} + M_{2}} the mean sum of mediator variables values for DV conditions across observations (see Montoya & Hayes, 2011).

Coefficients associated with aa, bb, cc, and cc' paths are respectively a21a_{21}, b32b_{32}, c11c_{11}, and c31c'_{31}.

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). 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 XX on YY) will be positive and compute the other difference scores accordingly. That is, if mdt_within has to use Y2iY1iY_{2i} - Y_{1i} (instead of Y1iY2iY_{1i} - Y_{2i}) so that c11c_{11} is positive, it will use M2iM1iM_{2i} - M_{1i} (instead of M1iM2iM_{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.

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

See Also

Other mediation models: mdt_moderated(), mdt_simple()


[Package JSmediation version 0.2.2 Index]