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 Michael Strambler posted on Tuesday, September 21, 2010 - 11:23 am
I am attempting to test a moderated mediation model where X->M->Y (all observed variables) is moderated by w, a binary variable (gender). I know how to examine this with a multiple group approach but am attempting the approach described in Preacher, Rucker & Hayes (2007) in Figure 2 model 5 where W influences paths a and b of the mediation chain.

With some help from Preacher & Hayes, I have modeled this as pasted below. However, all of the estimates SEs, ps, CIs are exactly the same for all of the paths with the exception of X->M and M->Y. This seems very odd to me. Can you point me to what I might be doing wrong? Thank you.

ANALYSIS: BOOTSTRAP = 1000;

MODEL:
M WITH MW;
M ON X (a1)
W XW (a3);
Y ON M (b1)
X W XW MW (b2);

MODEL CONSTRAINT:
NEW(eff1 eff2);
eff1=(a1+a3*1)*(b1+b2*1);
eff2=(a1+a3*2)*(b1+b2*2);
OUTPUT: CINTERVAL(bcbootstrap);
 Linda K. Muthen posted on Tuesday, September 21, 2010 - 2:18 pm
Can you send your input, data, output, and license number to support@statmodel.com.
 Linda K. Muthen posted on Tuesday, September 21, 2010 - 5:34 pm
I should have seen this earlier. The label b2 after x, w, ex, and mw holds all of those regression coefficients equal. It should be stated as:

Y ON M (b1)
X W XW
MW (b2);
 Michael Strambler posted on Tuesday, September 21, 2010 - 6:30 pm
Thank you. The results make much more sense now. Now it is only the M on W and M on MW results that are identical. Does it make sense that this would be the case? Might this be because the MW cross-product involves a binary variable (coded 1 & 2)?
 Linda K. Muthen posted on Tuesday, September 21, 2010 - 6:42 pm
I think you mean M on w and Xw. It is the same issue. The label needs to be on the next line.

M ON X (a1)
W
XW (a3);
 Michael Strambler posted on Tuesday, September 21, 2010 - 6:47 pm
Ah, yes. All looks well now. Thank you again for the help.
 Robert Wickham posted on Monday, November 01, 2010 - 8:52 am
Drs. Muthen,
I am attempting to fit a Moderated Mediation model with simple (single level) survey data. Specifically, I wish to examine the moderating influence of a dichotomous variable (Mod) effect coded (-1, +1) on the 'A', 'B', and 'Cprime' paths as seen in Edwards and Lambert (2007; Model H).
I realize that the multigroup function of Mplus may be used in this specific instance(e.g. a categorical moderator), however I would like to retain the more traditional 'cross product' approach for two reasons: 1) To replicate OLS estimates for all paths, 2) I am often interested in similar models with continuous moderators where multigroup analysis is not appropriate.
IV and Mod were centered prior to importing into Mplus, and product terms were created using a DEFINE statement.
I use the following code:

MODEL:
Med on Mod IV IVxMod; ! 'A' Path
Y on Mod IV IVxMod Med ModxMed; ! 'B' and 'Cprime' Paths

MODEL INDIRECT:
Y IND Med IV;

The resulting model has df = 1 and a significant ChiSq. Examination of RESIDUAL matrix suggests a notable covariance between Med and ModxMed.
Adding a covariance parameter results in a not-positive first-order derivative matrix.

I have run into this problem before in the past, and I have a hunch that it stems from the fact that Med is an endog variable.
Any advice?
Thanks
 Linda K. Muthen posted on Tuesday, November 02, 2010 - 10:21 am
If you obtain standard errors, the message most likely comes from the fact that the mean and the variance of your binary variable are not orthogonal. You can ignore the message if this is the case.
 Hong Deng posted on Thursday, May 10, 2012 - 8:10 pm
Dear Drs. Muthen,
Im trying to test a moderated mediation model with nested data. Data for all my variables were collected from individual and the level of interest is individual too. Basically it is a 1-1-1 mediation model (x-m-y)with a level 1 moderator (w). It should be an easy one if my data isnt nested in several clusters. Is it possible to test such a model in Mplus? How should I write my Syntax.
Thanks very much.
 Linda K. Muthen posted on Friday, May 11, 2012 - 10:20 am
Moderation can be handled by a multiple group analysis if the moderator is categorical or by creating an interaction term between the moderator and the variable being moderated. You can use TYPE=COMPLEX to take the non-independence of observations into account. Modify Example 3.11 and add CLUSTER to the VARIABLE command and TYPE=COMPLEX to the ANALYSIS command.
 Elizabeth Penela posted on Friday, May 25, 2012 - 2:48 pm
I am attempting to conduct a moderated mediation analysis where all variables are latent variables formed with continuous indicators. In my model, the independent variable (X) functions as a moderator of the b1 path (the path from the mediating variable to outcome). This model is illustrated in Preacher, Rucker & Hayes (2007) - Figure 2, Model 1.

I get an error message that says:
MODEL INDIRECT is not available for TYPE=RANDOM.

1. Is it possible to do moderated mediation with a latent interaction variable?

2. Any guidance as to how to generate syntax for this model would be greatly appreciated.

Best,
Elizabeth
 Bengt O. Muthen posted on Friday, May 25, 2012 - 3:03 pm
This type of model is discussed in Section 3 and Section 5 of

Muthn, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus.

which is on our web site under Papers, Mediational Modeling. As stated in Section 3, Model Indirect cannot be used for this type of model. Section 5 shows how to define the direct and indirect effects instead using Model Constraint.
 Paula Ruttle posted on Thursday, August 30, 2012 - 12:17 pm
Dear Drs. Muthen,
I am currently running a moderated mediation model (X -> A -> Y with the pathway from A to Y being moderated by gender) using the following syntax:

Y on X;
A on X(Med1);
Y on A(Med2);

Y on Gender A*Gender;

MODEL CONSTRAINT:
NEW(Indir1);
Indir1=Med1*Med2;

This model revealed significant moderated mediation; however, correlations suggested that moderation could also be expected on the pathway between X and A. When I checked for moderation of this pathway it also revealed significant moderated mediation. I've been told that presenting a model with both moderated pathways is inappropriate and that I need to check to see which model is best; however I am unsure of how to do this. Do you have any advice or syntax for such a problem?

Many thanks in advance,
Paula
 Bengt O. Muthen posted on Friday, August 31, 2012 - 2:49 pm
I would suggest a 2-group analysis with gender as the grouping variable. You can then easily test if the two paths are the same or different across gender. I can't see why a model with both paths differing across gender would be inappropriate.
 Luisa Rossi posted on Thursday, October 25, 2012 - 4:32 am
Dear Drs Muthen,

I have been running some mediation analyses and found that the x-->y relationship was mediated by m.

I would now like to see if the a and b paths of the indirect models are different depending on whether respondents score high or low on a number of personality measures.

So far, I have used multiple group comparisons with difftest to assess this.

I am now thinking there may be a better way to do it but I am not sure. Can you help?

Thanks,
Luisa
 Linda K. Muthen posted on Thursday, October 25, 2012 - 9:57 am
Why are you dissatisfied with your approach? Is it because your moderator is continuous and you are categorizing it? See Example 3.18 in the Version 7 Mplus User's Guide on the website for another approach to moderated mediation.
 Luisa Rossi posted on Thursday, October 25, 2012 - 10:08 am
Hi Linda,

Thanks for the tip! I will look at the example. I am unsure whether multiple group comparison is the most effective way to look at moderated mediation or whether reviewers may criticize it.

Thanks again!
 Linda K. Muthen posted on Thursday, October 25, 2012 - 11:47 am
There are two ways to look at moderation. One is multiple group and the other to create an interaction variable.
 Luisa Rossi posted on Tuesday, October 30, 2012 - 7:03 am
Hi Linda,

I work with survey data so I had to impute my dataset to account of missing data.

I noticed that difftest cannot be used with imputed data so I am thinking about using the interaction variable approach.

The example you recommended above seems to be based on path analysis (?) as it is able to use the define option after the usevariable one.

I am working with latent variables so I first need to define them - however, mplus is not happy for me to use 'define' within the 'model' option.

What can I do?
 Linda K. Muthen posted on Tuesday, October 30, 2012 - 1:35 pm
With latent variables, you should use the XWITH option for interactions. DEFINE is for observed variables. You don't include it in the MODEL command but above or below it.
 Luisa Rossi posted on Wednesday, October 31, 2012 - 5:15 am
Thanks Linda!

AS I am interested in the potential moderating role of w on path a and b, I adapted the model 5 example in Preacher, Rucker, and Hayes (2007) but I used the XWITH command to obtain the two interaction terms

mw | m XWITH w;
xw | x XWITH w;

and included these interaction terms in the model as they suggest:

MODEL:
y on m (b1)
x
w
mw (b2)
xw;
m on x (a1)
w
xw (a3);

I find that there is no evidence for a moderating role of either mw or xw (Ps>.05).

However, when I had run the multiple group comparisons using w as the grouping variable I had found that it moderated path a (not b)...

I am slightly confused on why this might be... can you help? are the 2 approaches not 100% comparable?
 Linda K. Muthen posted on Wednesday, October 31, 2012 - 1:05 pm
These two approaches should yield identical results. Please send the two outputs and your license number to support@statmodel.com.
 C. Lechner posted on Monday, January 14, 2013 - 7:58 am
Dear Drs. Muthn,

I am testing a moderated mediation model where the moderator is a latent variable; i.e., there is a latent interaction using the XWITH command involved.
Because this requires TYPE=RANDOM, the STANDARDIZED output is not available in these analyses. However, I would like to report R-square values for my outcomes.
--> Is there any way to obtain R-square values for these analyses?

Many thanks in advance!
Clemens
 Linda K. Muthen posted on Monday, January 14, 2013 - 10:35 am
See the FAQ Latent Variable Interactions on the website.
 C. Lechner posted on Thursday, January 17, 2013 - 7:38 am
Thanks, Linda. I have a follow-up question:

Using the formulas provided in the FAQ, I programmed myself an Excel spreadsheet that calculates R-square, change in R-square, and standardized path coefficients. It reproduces the numbers from your example on p. 6 in the FAQ sheet perfectly well.

However, I wonder how one would generalize the equations from the FAQ sheet to include covariates. In my model (otherwise identical to Fig.2 on p.7), all three latent variables are regressed on a set of covariates. -> How do I get the total variances of the latent variables in this case?

My approach was to sum all the product terms of the squared regression weights and the variance of the respective predictor, plus the residual variance of the latent variable (which I get in the output). E.g., for a latent variable eta2, regressed on eta1 and covariates x1 and x2, where b1 to b3 denote regression coefficients and zeta2 the residual variance of eta2, the total variance would be obtained by computing:
Var(eta2) = b1^2 * Var(x1) + b2^2 * Var(x2)+ b3^2 * Var(eta1) + Var(zeta2).

However, this seems to systematically underestimate r-square.
-> Have I overlooked anything?
-> Is there any way to directly get the total variance of a latent variable that is regressed on a set of covariates in the Mplus output?
 Linda K. Muthen posted on Thursday, January 17, 2013 - 8:52 am
You need the variances of the two factors in the interaction. I think you are using the residual variances. If the variances are not available in TECH4, you will need to compute them.
 C. Lechner posted on Thursday, January 17, 2013 - 9:42 am
TECH4 is unavailable for TYPE = RANDOM. I tried to compute the variances of all latent variables in the way described above. Referring to the notation in Figure 2 in the FAQ sheet, extended by two covariates x1 and x2 for each latent variable, I compute

Var(eta1) = b11^2 * Var(x1) + b12^2 * Var(x2)+ ^2 * Var(eta2) + Var(zeta1)

Var(eta2) = b21^2 * Var(x1) + b22^2 * Var(x2)+ Var(zeta2)

Var(eta3) = b31^2 * Var(x1) + b32^2 * Var(x2)+ 1^2 * Var(eta1) + 2^2 * Var(eta2) + 2*1*2*Cov(eta1,eta2)+3^2*Var(eta1*eta2) + Var(zeta3)

where eta1 is regressed on eta2 (coefficient ), eta3 is regressed on eta1 (coefficient 1), eta2 (coefficient 2), and their interaction (coefficient 3), all three latent are regressed on the covariates x1 and x2 (coefficients bij), zeta_i denote residual variances for the latent variables; and where Var(eta1*eta2) = Var(eta1)*Var(eta2)+[Cov(eta1,eta2)]^2 and Cov(eta1,eta2) = *Var(eta2).

-> Is this correct? There must be something missing. R-squares are substantially smaller than the ones Mplus computes for a model without interaction.
 Bengt O. Muthen posted on Thursday, January 17, 2013 - 5:29 pm
In your eta1 equation you have to express eta2 in terms of the x's so that eta1 is written as a function of these x's.

You also have to take into account that the x's are correlated and add a term like for the et1 equation:

2*Cov(x1, x2)*b11*b12.
 C. Lechner posted on Friday, January 18, 2013 - 1:04 am
Thanks you, Bengt I think the covariance term is what I had overlooked. I'll add it and see whether the numbers add up to something that makes sense then.

As this involves a lot of manual computation when more covariates are involved, I wonder whether there is any easy workaround? E.g., could one simply estimate the factor variances in a measurement-part only model (without the covariates and structural paths) and use those as input for the calculations of standardized parameters and r-square in the final model?
I'm afraid that would bias the variance estimates, wouldn't it?
 Bengt O. Muthen posted on Friday, January 18, 2013 - 2:53 pm
It might be too approximate.
 ywang posted on Friday, March 29, 2013 - 12:49 pm
Dear Drs. Muthen:

I have a question about how to decide the signficance of the moderated medaition. M is a mediator and W is a moderator (gender, 0 and 1).

For the following input:
ANALYSIS: BOOTSTRAP = 1000;

MODEL:
M ON X (a1)
W
XW (a2);

Y ON M (b1)
X W XW
MW (b2);

MODEL CONSTRAINT:
NEW(eff1 eff2);
eff1=a1*b1;
eff2=(a1+a2)*(b1+b2);

Should we test the indirect effect of a2*b1 and a1*b2 in order to conclude the signficant moderated mediation?

Thank you very much in advance!
 Bengt O. Muthen posted on Friday, March 29, 2013 - 4:33 pm
Seems like you have set it up right. One thing you want to test is the difference between eff1 and eff2. To test significance of the moderation in the mediation, don't you want to test that a2*(b1+b2) is significant? Both the difference and this last term can be given NEW names so you get z-tests.
 Leslie Roos posted on Friday, May 03, 2013 - 1:54 pm
Hello!

I wanted to ask a question about a post from the first author on this post regarding a model he created from your 2007 Conditional Indirect Effects Paper, working from "Model 5". In line with the model, I am confused about the role of eff1 and eff2.

Having performed previous mediations with the eff1 = a*b, this makes sense to me based on the M ON (X & XW) and Y on (M & MW) paths. I am confused about the eff2 role of multiplying each by 2 -- what would this be representing?

Thank you!
Leslie


MODEL CONSTRAINT:
NEW(eff1 eff2);
eff1=(a1+a3*1)*(b1+b2*1);
eff2=(a1+a3*2)*(b1+b2*2);
OUTPUT: CINTERVAL(bcbootstrap);
 Bengt O. Muthen posted on Friday, May 03, 2013 - 5:06 pm
One and two standard deviations above the mean of zero for the moderator.
 Krista Highland posted on Thursday, May 30, 2013 - 10:13 am
Hello,

I am attempting to complete a moderated mediation in which my predictor and mediator variables are latent and I have a categorical and latent outcome. The format follows Preachers Model 5 in which the moderator impacts both paths a and b. The moderator variable has 4 categories (Black, White, Hispanic, Other). Would it be best to utilize the multiple group function, comparing all 4 racial groups together? Though, I am unsure of how to complete group comparisons from there. Or would it be better to run models with several dummy codes representing the moderator?
 Bengt O. Muthen posted on Thursday, May 30, 2013 - 10:21 am
I would suggest doing multiple-group analysis, letting the a and b paths vary.
 Yisheng Peng posted on Wednesday, July 10, 2013 - 9:47 am
I found most other researchers would calculate the critical ratios of differences(CRD) by dividing the difference between two estimates by an estimate of the standard error of the difference (Arbuckle, 2003). A CRD greater than 1.96 indicates that there was a significant difference between the two parameter estimates at p < 0.05.However, they usually do this in the Amos software. However, in the Mplus result part, I cannot find the standard error of the difference.

I am wondering how can you do this through the results provided by Mplus?
 Yisheng Peng posted on Wednesday, July 10, 2013 - 9:54 am
My question is, I want to do multi-group analysis to identify whether the path coefficients differ significantly between east and west. We compared the first model, which allows the structural paths to vary across cultures, with the second model, which constrains the structural
paths across cultures to be equal to examine the cultural differences.
All the other paths (i.e., factor loadings, error variances and structure covariances) were constrained to be equal. However, I found the factor loading are still different in the Mplus result part. Are there other things that need to be constrained equally?

By the way, do you have any sample code for me to do such a multiple group comparison of the mediation model?

Thanks.
 Linda K. Muthen posted on Thursday, July 11, 2013 - 2:19 pm
You can use MODEL TEST to do the CRD test. See the user's guide.

See the Topic 1 course handout on the website under multiple group analysis to see the inputs for testing for measurement invarinace.
 JOEL WONG posted on Tuesday, October 08, 2013 - 5:28 pm
I am attempting to test a first and second stage moderated mediation model. I have one predictor, one outcome, one mediator, and one moderator (W). W is hypothesized to moderate the relationship between the predictor and the mediator and the relationship between the mediator and the outcome.

This model is described as model 58 in Hayes' PROCESS manual -- see http://mres.gmu.edu/pmwiki/uploads/Main/process.pdf

Does anyone know the Mplus syntax for this model? Preacher et al. (2007) provides the Mplus syntax for several moderated mediation models, but it doesn't include this model. Thank you.
 Bengt O. Muthen posted on Tuesday, October 08, 2013 - 5:46 pm
UG ex 3.18 describes the case of a moderator Z that moderates the influence of the predictor X on M and X on Y. That involves creating X*Z in Define. A plot of the effects and their confidence bands are obtained by LOOP. So that's the first part of your question.

The second part is the moderation of the M->Y relationship. This calls for creating M*Z and regressing Y on it. LOOP could be used here as well.
 Krista Highland posted on Thursday, October 10, 2013 - 7:11 am
Hello,

Thank you for your previous suggestion in running my moderated mediation with 2 latent predictors, continuous mediator, and binary outcome. I have 4 groups, and have tested the model using the indirect command and bootstrapping (with BC confidence intervals). To compare the effect size of the indirect effects across groups. Would it be acceptable to examine whether the confidence intervals between groups overlaps as a means of testing for significant differences? Or is there a better way to test whether the effect sizes are significantly different (e.g. running a difftest where I constrain paths a and b and compare them to a non-constrained model)?

Also, when I run the mediation for the whole sample, the mediation is significant. However, when I run the mediation using multigroup, the indirect effect is no longer significant in any of my four groups. Would it be safe to say that this could be due to sample size issues (as, I have already established measurement invariance in my predictors)
 Linda K. Muthen posted on Friday, October 11, 2013 - 11:04 am
You should use DIFFTEST.

Yes, lower power could be the reason for this.
 Laura Baams posted on Tuesday, October 29, 2013 - 9:02 am
Hi,

I am running a bootstrap multigroup mediation model with observed variables. Two predictors (x1, x2), two mediators (m1, m2) and one outcome (y).
There are 5 groups for the multigroup part.

I have compared model fit of a model in which I constrain all paths to be equal across groups, and a model in which they are not equal, and variations of this.
The model with the best fit, is the one where groups 1 and 2 are constrained to be equal, and group 3 and 4 are constrained to be equal.

I need to report the standardized estimates, but these are not equal for groups 1 and 2, or 3 and 4, while the unstandardized estimates are.

From other posts I understand that Mplus does not constrain standardized estimates, does this mean I cannot report standardized estimates in this case?

Is there a way I can still obtain standardized estimates (that are equal across groups 1 and 2; and 3 and 4)?

Thanks so much!
 Linda K. Muthen posted on Tuesday, October 29, 2013 - 10:12 am
The standardized coefficients are standardized using different standard deviations for each group. This is why the coefficients are different. It is not because they are not constrained to be equal in the analysis.
 Patrcia Costa posted on Friday, January 31, 2014 - 2:47 am
Dear Drs Muthn,

I am running a moderated mediation model based on Preacher, Rucker & Hayes (2007) model 3: the path from mediator m to y is moderated by w.

The output shows that all paths are significant at p =.000.

However, the fit indexes are as follows:


AIC 195.379
BIC 221.996
Sample-Size Adjusted BIC 171.948


Chi-Square Test of Model Fit

Value 12.001
Degrees of Freedom 2
P-Value 0.0025

RMSEA

Estimate 0.358
90 Percent C.I. 0.182 0.565
Probability RMSEA <= .05 0.004

CFI 0.958
TLI 0.852

Chi-Square Test of Model Fit for the Baseline Model

Value 243.003
Degrees of Freedom 7
P-Value 0.0000

SRMR 0.311


Can I consider that my model is significant, relying on the significance of the paths?

Thank you for your input,
Patricia
 Linda K. Muthen posted on Friday, January 31, 2014 - 9:35 am
Model fit is not assessed using the significance of the model parameters. The fit statistics indicate that leaving out the two paths represented by the two degrees of freedom create a lack of fit.
 Hannah Lee posted on Sunday, March 30, 2014 - 2:17 pm
Hello,
I am trying to run a moderated mediation model (Similar to Model 2 as Hayes and Preacher 2007 shows).

The IV, Moderater are latent constructs and I am unable to use the MODEL INDIRECT with the latent interactions. So I referred to Muthen (2011) sections 3 and 5 using BOOTSTRAP=1000; and OUTPUT: cinterval(bootstrap);

But I keep getting the error message that bootstrap cannot be used with TYPE=RANDOM. How can I get the indirect/total effects?
====================================
ANALYSIS: TYPE = RANDOM;
bootstrap = 1000;
MODEL: x1 BY CPI1-CPI3;
x2 BY CPC1-CPC3;
w BY CCC1-CCC3;
m BY NPA1-NPA5;
d BY Perf1- Perf6;
x1xw | x1 XWITH w;
x2xw | x2 XWITH w;
d ON m (b1) x1 x2 w x1xw x2xw c1 c2;
m ON x1 (a1) x2 (a2) w x1xw (a3) x2xw (a4) c1 c2;

MODEL CONSTRAINT:
new (ind wmodval);
wmodval = -1;
ind=(a1+a3*wmodval)*b1;
ind=(a2+a4*wmodval)*b1;
OUTPUT: SAMPSTAT STAND;
cinterval(bootstrap);

*** ERROR in ANALYSIS command
BOOTSTRAP is not allowed with TYPE=RANDOM.
 Bengt O. Muthen posted on Sunday, March 30, 2014 - 4:55 pm
Drop the bootstrap request. If your sample size is not small it is unlikely that bootstrap SEs would be very different from regular ML SEs.
 Hannah Lee posted on Sunday, March 30, 2014 - 7:07 pm
Thank you, Dr. Muthen. Would N=201 sample size be sufficient?

Some of the t-values for the indirect effects come out to be borderline significant. I was wondering if this may be improved if I were able to bootstrap.
 Linda K. Muthen posted on Monday, March 31, 2014 - 8:05 am
I would not worry unless the sample size is less than 100.

I would be conservative as far as significance goes given that you are not doing a single test but several tests.
 CW posted on Tuesday, April 22, 2014 - 10:53 am
I have used the following syntax to test a moderated mediation model:

y ON m (b);
m ON x (gamma1)
z
xz (gamma2);

However, I am now realizing that this did not include a test of y on x, which seems like the typical c path in a mediation model. Can you explain to me why did is not present in this syntax, and is it necessary?

Thank you!
 Linda K. Muthen posted on Wednesday, April 23, 2014 - 10:22 am
Whether you include a direct effect in your model is up to you. Your research hypothesis should determine this.
 Alvin posted on Thursday, May 01, 2014 - 10:31 pm
Hi there, I estimated a moderated mediation model (similar to that of Hayes and Preacher). All looks good except I did not get fit statistics in my output, even though I did it without bootstrap. Can you advise? many thanks
Here's the relevant bit of the syntax:
Analysis:
Type = random;
ALGORITHM=INTEGRATION;
Model:
pte by conflict family witness access child;
adapt by safety bonds justice roles exist;
stress by basic health dispa safe;
ptexad | pte xwith adapt;

whodas12 on pte (b1)
adapt
stress
ptexad;
stress on pte (a1)
adapt
ptexad (a3);
Model constraint:
new (ind wmodval);
wmodval = -1;
ind=(a1+a3*wmodval)*b1;

Information Criteria

Akaike (AIC) 9305.490
Bayesian (BIC) 9484.270
Sample-Size Adjusted BIC 9319.462
(n* = (n + 2) / 24)
 Linda K. Muthen posted on Friday, May 02, 2014 - 9:15 am
Chi-square and related fit statistics are not available when means, variances, and covariances ore not sufficient statistics for model estimation. This is the case for numerical integration.
 Alvin posted on Friday, May 02, 2014 - 7:48 pm
Thanks very much Dr Muthen for the quick reply. I could not do it without numerical integration however. Is there a way to get fit statistics without using numerical integration. If not, how do I know if my model fits? In terms of interpreting the outcome, is it correct to say that the putative moderator did not moderate the mediating relationship between the IVs and DV, as the interaction product (ptexad) was not significant?
 Linda K. Muthen posted on Sunday, May 04, 2014 - 4:42 pm
When there are no absolute fit statistics, nested models can be tested using -2 times the loglikelihood difference which is distributed as chi-square. Non-nested models with the same set of dependent variables can be compared using BIC.

If the interaction is not significant, there is no moderation.
 Alvin  posted on Wednesday, May 07, 2014 - 1:27 am
Thanks. Another question is the output does not give standardized coefficients as this was not allowed under Type=random analysis .. Is there a way to obtain standardized estimates as I'm interested in the effect sizes
 Linda K. Muthen posted on Wednesday, May 07, 2014 - 11:02 am
Because the variance of y varies as a function of x with TYPE=RANDOM, we do not give standardized estimates.
 Alvin  posted on Wednesday, May 07, 2014 - 5:44 pm
Thanks Dr Muthen - it probably doesn't make sense then to standardize the estimates of the effect size, does it? Also, I notice boostrap can not be used to construct CIs in this case with a random-intercept-random-slope model. Why is that?
 Alvin  posted on Wednesday, May 07, 2014 - 6:06 pm
A further question Dr Muthen is, I notice in model 5 by Preacher et al, the value of w at which the conditional indirect effect of x is estimated is set to -1. Is this an accepted practice? The authors refer to the Johnson-Neyman (J-n) technique where a region of significance or a range of values of w is used in lieu of using arbitrary conditional values of w. Is this method used in analysis of moderated mediation models?
 Linda K. Muthen posted on Thursday, May 08, 2014 - 2:11 pm
The variance of y varies as a function of x for any random effect. In this case, standardization is not done.

If you add ALGORITHM=INTEGRATION; to the ANALYSIS command, I think you can use BOOTSTRAP.

-1 is probably one standard deviation below the mean. This is a common choice. See Example 3.18 where you can get a plot that shows a region of significance.
 Alvin  posted on Thursday, May 15, 2014 - 11:41 pm
Thanks Dr Muthen. I wanted to look at the significance level of a particular set of indirect paths (beta1 x beta2) in the mediation part of the moderated mediation model, as this path (beta2) dropped out (not significant) after building the moderators into the model. Of interest is whether this could be due to power issues (as I have a small sample size). In random-effect analysis however Mplus does not allow model-indirect command. Is there an alternative way to request estimates of indirect/direct effects in this case?
 Linda K. Muthen posted on Friday, May 16, 2014 - 10:00 am
See Slides 80-85 of the Topic 7 course handout on the website.
 Eric Thibodeau posted on Wednesday, June 25, 2014 - 12:41 pm
I have an environmental observed variable of maltreatment, one observed impulsivity mediator, and a latent antisocial variable as my outcome. I'm interested in using genotype as a moderator. The problem is my moderator (genotype) is categorical and my maltreatment variable is categorical (no maltreatment, emotional abuse, physical abuse). I understand that the common practice in SEM is to use multiple group comparisons.

This is my problem and confusion. Recent recommendations by GxE experts call for all covariates to be included as interaction terms in your model. For example, I will have race and age as covariates in my SEM (full partial). However, it's now recommended that you should also add "race x environment" and "age x environment" as covariate terms in addition to having them be "main effect" terms. The Age x environment term is easy to add as a covariate because age is continuous. Race is not so easy, because it's categorical. Thus I'm not sure how to "control" for these other moderation effects that include categorical variables. Do I just do a massive multiple group comparison?

I've considered dummy coding the categorical moderator variables, so that I can create interaction variables and add everything in one SEM rather than do many group comparisons. That way I can appropriately add covariates where I want (full partial). Is that bad practice? Any advice?
 Bengt O. Muthen posted on Wednesday, June 25, 2014 - 5:33 pm
Seems like (1) dummy coding categorical variables and creating interactions is a relatively simple way to go. (2) Multiple-group modeling is also fine, but you would either have to combine that with interaction terms or do groups as maltreatment x genotype (so 6 groups). Approach (1) allows only different regression effects while approach (2) also allows different (residual) variances for DVs.

You may want to ask this type of general modeling question on SEMNET.
 Alvin  posted on Monday, July 28, 2014 - 4:54 pm
Dear Dr Muthen, following a mediated moderation model, I ended up with unstandardized estimates. I was wondering since standardization isn't available in random-effect models, are there alternative ways for standardizing the estimated coefficients manually? I've previously obtained unstandardized estimates from a similar model but without interaction terms, and they look similar to the one with interaction effects. Does it make sense to obtain standardized effects in this case? this is a fair question that I anticipate reviewers may ask. Many thanks
 Bengt O. Muthen posted on Monday, July 28, 2014 - 5:26 pm
What are your Model command statements that give rise to a random effect model?
 Alvin  posted on Monday, July 28, 2014 - 5:34 pm
Here's the relevant bit of the syntax. The model was estimated using algorithm-integration. Many thanks

Model:
pte by conflict family witness access child;
adapt by safety bonds justice roles exist;
stress by basic health dispa safe;

ptexad | pte xwith adapt;
stxad | stress xwith adapt;

whodas on pte (b1)
adapt
stress (c1)
ptexad
stxad (b2);

stress on pte (a1)
adapt
ptexad (a3) stxad with stress;

whodas on dsmany;

Model constraint:
!plot(indirect);
!loop(mod,-1,2,0.1);
new (ind wmodval);
wmodval = -1;
ind=(a1+a3*wmodval)*(b1+b2*wmodval);

new(ac);
ac = a1*c1;
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