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Hi, 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? Thank you, Tamara |
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You can use TYPE=MIXTURE RANDOM and the KNOWNCLASS option for multiple group analysis using XWITH. When all classes are known, it is exactly the same as using the GROUPING option. |
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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? Thank you for your help! |
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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. |
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Hi Bengt, thanks for your quick answer. I still don't understand why. Does this mean that the 'xwith' command standardizes x and moderator? Output prints a variance of 1 for x and moderator. But what is the command to get the means of latent variables? I see these for the individual items/indicators but not for the latent variables. Thanks again! |
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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); |
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First Eib post: 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. |
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Second Eib post: The input looks correct. Send the output for the bootstrap run to support@statmodel.com along with your license number. |
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Dear Drs Muthen, I am estimating a mediation model in which the mediator is a growth model (so intercept and slope are the mediators), X is binary (treatment) and Y is continuous. I would like to test for an interaction in the "b path", ie whether the association between my growth factors and my outcome is moderated by a Z variable. Is using XWITH the correct approach to do that in Mplus? Can you point me toward an example, if there are any? Thank you |
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Yes, use XWITH for a factor f and an observed variable y: int | f XWITH y; |
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Thank you Dr Muthen. I have 2 follow up questions: When testing for the interaction between my growth factors and the observed continuous variable y: 1) am I supposed to enter the interactions for both growth factors at the same time ? That is: int1 | intercept XWITH y; int2 | slope XWITH y; outcome ON y int1 int2; 2) is it normal that the interaction between slope and y changes significantly according to whether the intercept growth factor is centred to the first or last time point of my growth model? i s | v1@0 v2@.12 v3@.30; versus i s | v1@-.30 v2@-.18 v3@.0; Thanks again |
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1) That's ok. 2) Yes, the meaning of "i" changes. |
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Bibi Zhang posted on Sunday, July 08, 2018 - 10:13 am
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Hi, I have questions regarding moderated mediation using latent variables. I need type=random to declare interaction variables, but model indirect effect is not available for type=random. How can I get indirect effect while retaining the moderator? Here is my inp: analysis: type=random; algorithm=integration; model: IV by IV1-5; Me by Me1-4; Mo by Mo1-6; DV by DV1-6; Me on IV Mo; IVxMo | IV xwith Mo; Me on IVxMo; DV on Mo IV; model indirect: DV via Me; output: stdyx mod; Thank you! |
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You can place parameter labels in the Model command and use them in the Model Constraint command to express the effects you want. |
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Hi Dr. Muthen, I have a question: When conducting a moderated-mediation analysis: Do you need to first have a significant indirect effect before you proceed with testing your conditional indirect effect at different levels of the moderator? Thanks, Sara |
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No, that is not necessary - see for instance our discussion of that on page 112 of our RMA book. |
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