I have a mediation model with 1 latent IV, 1 latent mediator, and 1 latent DV. I want to see if this mediation process works differently in:
1) two national contexts (moderator 1) 2) for people high and low on perceived discrimination (moderator 2, measured with multiple continuous items) 3) a combination of 1 and 2: low discrimination in country 1, high discrimination in country 1, low discrimination in country 2, high discrimination in country 2 (so a possible three-way interaction)
I started off by using multiple group modelling to test these three moderated mediation models, first using country as a grouping variable, then using dichotomized discrimination (computing a mean score and spliting the participants into high/low), and then grouping the data into four groups combining country and discrimination.
However, a reviewer wants me to test a latent interaction between the IV and discrimination instead. I've done this, but I do not seem to manage to answer question 3 in this way – Mplus does not allow me to use command TYPE=RANDOM and at the same time estimate a multiple group model for the two countries.
Do you have a solution to this? Can I interact the latent interaction term with the dichotomous country variable? If so, how is this done?
Hi, I have a question regarding which value of a moderator to choose for probing the indirect effect. Specifically, I run a moderated mediation model with a latent interaction (both X and Mod are continuous). I want to calculate (and bootstrap) an index for moderated mediation - for this, I need to select at which value of the moderator I want the mediation/indirect effect (I want low, middle, high).
I somehow think that with the 'xwith' command, mplus centers the latent variables composing the interaction, so therefore I should request the indirect effects at 0, 1 SD below and above 0 (but what is the SD then?)? Or should I request it at the mean of a composite scale of the moderator (mean of 3 indicators), 1SD above and below this?
So my question is, is there a way in Mplus to know what the mean and the SD of a latent variable is to know at which value of the moderator it is most reasonable to request the indirect effect?
The mean of a latent variable is zero unless otherwise printed (such as in multi-group settings). The SD is the square root of its estimated variance in the output. So do +- 1 SD for this latent moderator.
Hi, 1. In the following syntax, are -1, 0 and 1 adequate values to request? 2. Using these values with bootstrap, the p values of the main and interaction effects are .999. But they are sign. without bootstrap: why?
ANALYSIS: TYPE = RANDOM; BOOTSTRAP = 5000; ALGORITHM=INTEGRATION; Estimator is ML; INTEGRATION = 15; MODEL: X by xa* xb xc; X@1; DV1 by dv1a* dv1b dv1c; DV1@1; Med by meda* medb medc ; Med@1; Mod by moda* modb; Mod@1; X with Mod; DV1 with DV2; XxMod | X XWITH Mod; Med on X (xmed) Mod XxMod (int); DV2 on X (xdv2) Med (meddv2); DV1 on X (xdv1) Med (meddv1); MODEL CONSTRAINT: NEW (mh m0 ml CIN_LDV1, CIN_MDV1, CIN_HDV1 CIN_LDV2, CIN_MDV2, CIN_HDV2); ml = -1; m0 = 0; mh = 1; CIN_LDV1 = xmed * meddv1 + int*meddv1*ml; CIN_MDV1 = xmed * meddv1 + int*meddv1*m0; CIN_HDV1 = xmed * meddv1 + int*meddv1*mh; CIN_LDV2 = xmed * meddv2 + int*meddv2*ml; CIN_MDV2 = xmed * meddv2 + int*meddv2*m0; CIN_HDV2 = xmed * meddv2 + int*meddv2*mh; OUTPUT: TECH1 TECH8 STDYX CINTERVAL(BCBOOTSTRAP);
The latent variable means are zero as the default if not printed.
If you set the metric in your latent X and M by fixing the factor variance at 1, then in effect these latent variables are standardized. But it is not the case that Mplus standardizes them - instead your model specification results in tbis.