R: Fits a moderated mediation model

mdt_moderated {JSmediation}

R Documentation

Fits a moderated mediation model

Description

Given a data frame, a predictor (IV), an outcome (DV), a mediator (M), and a moderator (Mod) conducts a joint-significant test for moderated mediation (see Yzerbyt, Muller, Batailler, & Judd, 2018).You can learn about moderated mediation in vignette("moderated-mediation")

add_index.moderated_mediation computes the moderated mediation index. compute_indirect_effect_for is used to compute the indirect effect index for a specific value of the moderator.

Usage

mdt_moderated(data, IV, DV, M, Mod)

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.
`Mod`	An unquoted variable in the data frame which will be used as the moderator.

Details

With moderated mediation analysis, one tests whether the indirect effect of X on Y through M is moderated by Mod. 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.

Total moderation of the indirect effect of X on Y can be described as follows:

c * Mod = c' * Mod + (a * Mod) * b + a * (b * Mod)

with c * Mod the total moderation of the indirect effect, c' * Mod the moderation of the direct effect, (a * Mod) * b, the moderation of the indirect effect passing by the moderation of a, and a * (b * Mod), the moderation of the indirect effect passing by the moderation of b (see Models section; Muller et al., 2005).

Either both a * Mod and b or both a and b * Mod need to be simultaneously significant for a moderation of the indirect effect to be claimed (Muller et al., 2005).

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

In a moderated mediation model, three models will be used:

Y_i = b_{40} + \mathbf{b_{41}} X_i + b_{42} Mo_i + \mathbf{b_{43}} XMo_i
M_i = b_{50} + \mathbf{b_{51}} X_i + b_{52} Mo_i + \mathbf{b_{53} XMo_i}
Y_i = b_{60} + \mathbf{c'_{61}} X_i + b_{62} Mo_i + \mathbf{b_{63} Xmo_i} + \mathbf{b_{64} Me_i} + \mathbf{b_{65} MeMo_i}

with Y_i, the outcome value for the ith observation, X_i, the predictor value for the ith observation, Mo_i, the moderator value for the ith observation, and M_i, the mediator value for the ith observation.

Coefficients associated with a, a \times Mod, b, b \times Mod, c, c \times Mod, c', and c' \times Mod, paths are respectively b_{51}, b_{53}, b_{64}, b_{65}, b_{41}, b_{43}, b_{61}, and b_{63} (see Muller et al., 2005).

Variable coding

Because joint-significance tests use 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.

If you need to convert a dichotomous categorical variable to a numeric one, please refer to the build_contrast function.

Note that variable coding is especially important in models with multiple predictors as is the case in the model used to conduct a joint-significance test of moderated mediation. Muller et al. (2005) recommend using variables that are either contrast-coded or centered. Using mdt_moderated with a DV, a mediator, or a moderator that is neither contrast-coded nor centered will give a warning message.

References

Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89(6), 852-863. doi: 10.1037/0022-3514.89.6.852

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