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| Daniel posted on Friday, June 18, 2004 - 5:16 am
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| I am running several mediation models in which my dependent variable is ordered categorical. I am using the bootstrap method to estimate standard errors for the indirect effects, with the bootstrap analysis command. I asked for confidence intervals and am given the appropriate intervals. Can I use these intervals along with the effects to estimate odds ratios, or is this incorrect if my mediator is continuous? |
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| bmuthen posted on Friday, June 18, 2004 - 8:42 am
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| Which estimator are you using, WLSMV or ML? |
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| Daniel posted on Friday, June 18, 2004 - 9:02 am
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| I was using the default estimator. I believe it is WLSMV since I am modeling with categorical dependent variables, although I may be wrong. |
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| bmuthen posted on Friday, June 18, 2004 - 9:20 am
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| The default WLSMV works with probit regressions so the estimates are not directly in odds ratio metric. The indirect effects are with respect to a continuous y* variable behind the dependent observed categorical variable, where y* is the response propensity. I think this idea has been discussed in David MacKinnon's work. |
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| Daniel posted on Saturday, June 19, 2004 - 9:00 am
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| I have a question regarding the indirect effect. If the two arcs (paths) in the specific indirect affect (a to b [path a'] and b to c [path b']) are each significant (i.e., a' and b'), shouldn't the total indirect effect [a' * b')also be significant? Or is it possible for a' and b' to be significant without the specific indirect effect (a' * b') being significant? |
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| bmuthen posted on Saturday, June 19, 2004 - 11:59 am
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| Seems like this is possible because the indirect effect is a product of the two estimates and the SE of this product is a function not only of each of the two SEs, but also the covariance between the two estimates - which might be positive and therefore make the denominator of the test larger. |
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| Daniel posted on Monday, June 21, 2004 - 8:04 am
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| Ok, if I have the case where each path is significant, but the total indirect effect is not significant, what could I conclude about mediation? |
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| bmuthen posted on Monday, June 21, 2004 - 4:53 pm
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| I would say there is no significant mediation. |
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| Daniel posted on Tuesday, June 22, 2004 - 6:12 am
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| Thanks. My population is 913 for the study, and I am modeling mediation in an associative process model between two LGM, each with two random effects (trend and intercept), and about 5 covariates. The observed measures are ordered categorical. What would you suggest I set the bootstrap to (i.e., bootstrap=?) in the analysis command? |
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| There is no rule for this. You should experiment. Start with 250. Then try 500. Compare the standard errors to see if there is much difference. |
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| Daniel posted on Tuesday, June 22, 2004 - 10:49 am
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| I ran the bootstrap at 250, 300, 350, 400, and 450, and it ran fine each time, with each increase resulting in a proportional increase in run time. However, as soon as I run the bootsrap at 500, it runs for hours without end. Last night I tried to run it with 1000, and left the program running all night, after leaving work at about 4 PM. I returned to work the next morning, and it was still running. Why do you believe I cannot get a solution with values greater than or equal to 500? Does it have something to do with the associative processes or categorical outcome variables? |
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| Why don't you send the 400 run output, the 500 run input, and the data to support@statmodel.com so I can take a look at it. |
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| Using a WLSMV estimator, why would a chi-square test not be calculated using Dr. MacKinnon's bias-corrected bootstrap method of estimating SE and confidence intervals in a path analysis with multiple mediational pathways? |
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| There is no reason. We have so far only implemented bootstrap for standard errors. |
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Thank you very much for your quick response.
Would it appropriate then to report the chi-square goodness of fit test calculated when not using bootstrap function as long as the WLSMV estimator is used? |
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| Yes but you should make it clear that although the standard errors are bootstrapped, the chi-square is not. |
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I am currently running a mediational structural equation model dealing with domestic violence. The observed measures are primarily indices derived from self-report scales which have ranges as broad as 0 to 177 (i.e. The item endorsements are ordinal values, each of which represents a frequency range for specific behaviors (0 = 0-10, 1 = 10-20, etc.). These items are subsequently summed to produce the indices of interest for the current model.). I decided to model the data as continuous censored, but received the following error message:
INPUT READING TERMINATED NORMALLY
*** FATAL ERROR
Internal Error Code: GH1006.
An internal error has occurred. Please contact us about the error,
providing both the input and data files if possible.
I am forwarding the requested information to you.
In the meantime however, I am trying to resolve two questions regarding the model:
1) Regarding overall fit indices, I am contemplating the use of the MLR estimator, treating the data as continuous.
a) Given non-normal data and censoring
from below (at sero) to what degree
might this yield misleading
results?
b) Does applying a Bollen-Stine
bootstrap procedure provide a means
to address this more effectively?
2) Regarding the standard errors of the
parameter estrimates in the model I
would prefer to use a bootstrap
procedure since the this will provide
me with confidence intervals for the
indirect effects.
a) Do you detect anything problematic
with using the MLR approach for the
overall model fit indices, followed
by reporting confidence intervals
for the parameter estimates derived
from a bootstrap procedure?
Any guidance you might offer would be greatly valued. |
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| bmuthen posted on Monday, February 20, 2006 - 6:52 pm
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1.
a. With a high degree of censoring (say > 25-50%), the SEs and chi-square based fit indices may be off. The basic problem is that the linear model assumed is wrong with strong censoring, so non-normality robustness in SEs and chi-square doesn't help. Overall fit indices are perhaps less important than getting the right parameter estimates and checking fit by 2*LL for nested, neighbouring models.
b. I don't think so.
2. That's fine.
a. Not in principle. |
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Thank you so much for you prompt answer. If I might clarify this: Indeed I do have proportions of censored observations that are above 25%.
The error message I reported evidently occurs in version 3.01 but has since been corrected in version 3.14. If I run the analysis using the updated (v. 3.14) program, specifying which variables are censored I would obtain appropriate log-likelihoods from which -2*LL could be used for tests of nested models.
Am I correct in saying that the log-likelihoods obtained without the censor specification would be misleading?
Having obtained the -2*LL, I can use the Baron and Kenny (1981) approach to evaluating mediation. However, would there be a problem with removing the censor specification and bootstrapping the parameter estimates so that I can use the MacKinnnon (2004) approach to obtaining indirect effects and confidence intervals?
Finally, is there some reference you would recommend where I might find a primer on bootstrapping specifically regarding how to choose among the different bootstrap confidence intervals?
Many thanks. |
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| bmuthen posted on Tuesday, February 21, 2006 - 3:23 pm
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Yes, you are correct in that the loglik would be misleading if not taking censoring into account such as when using the censored approach.
You should use the same model for parameter estimation and testing as for the bootstrapping.
Efron, B. & Tibshirani, R.J. (1993). An introduction to the bootstrap. New York: Chapman and Hall.
MacKinnon, D.P., Lockwood, C.M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99-128. |
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Hello,
I am back on the mediation analysis trail. This time, unlike most of the data I analyze, my sample size is not that large (n=376) and my ultimate outcome (smoking) is ordered categorical with four levels. I am running a SEM with measured variables only (no factors). Is it better to calculate standard errors with bootstrapping or Delta method in this case, due to the relatively small sample size? |
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| I would use the default standard errors for the estimator you choose. I don't think that you would benefit from bootstrapping. |
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| Thanks |
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| Hello Linda and Bengt. I am asked by a reviewer to estimate the size of an effect in my model. I actually sent you this data before. My finding is that the significant indirect effect with 95% confidence interval is .054(.008,.101). You mentioned that this is a small effect. How should I word this in the results/discussion section to indicate the strength of this effect? I'd appreciate any clues if you have them. By the way, this was calculated with the delta method. |
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| To know how small it is, wouldn't you want to evaluate it in terms of the SD of the independent and dependent variables, so using a standardized value? |
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Ok, I see. Thank you very much.
DR |
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| Yi-fu Chen posted on Friday, July 21, 2006 - 7:13 am
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Hi, Dr. Muthen,
I am working on a model to test mediation effects. I have two predictors, four mediators and two outcomes. The outcomes are all continuous. I've tried to use MODEL INDIRECT with BOOTSTRAP to estimate the standard errors of the indirect effect.
The question I have is that:
When I ran a recursive model in which outcome1 predicted outcome 2, the output of model indirect showed the standard errors of indirect effects for predictors via each mediator.
However, when I estimated the recipical relationship between the two outcomes, the output showed only the total indirect effect for each predictors, but no printouts for the contribution of each mediator.
I don't know if what I got is right for Mplus when recipical model are estimated.
Is there any way that I can get more detail indirect effect information for this kind of model?
I am using MPLUS 3.0.
Thanks! |
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| I don't think this is possible. See the Bollen SEM book to check. |
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Dear Mplus-team, I have read in an article by MacKinnon and colleaques that there are different ways to calculate SE for indirect effects, using the delta method (e.g. Freedman & Schatzkin, 1992, or Olkin & Finn, 1995). I would be interested in which one is implemented in Mplus?
MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G. & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7 (1), 83-104. |
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| We calculate standard errors for indirect effects using both the Delta method and bootstrap as described in the MacKinnon et al. aricle. I am not aware that there are different Delta methods. |
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| Claire Hofer posted on Thursday, December 07, 2006 - 8:02 am
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Could you tell me about the differences in the bootstrap method between mplus version 3 and version 4? I am getting very different results: my model run in version 4 with the Bollen Stine bootstrap method matches closely what I get in the regular model using ml or mlr estimation but when I run the model in version 3 with the bootstrap method there, I get completely different results. We do have missing data. Could you tell me a little bit about why the results might be so different?
Thank you. |
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| I don't know of any reason offhand there would be a difference. If you send your input, data, output, and license number to support@statmodel.com, I can take a look at it. |
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Dear Drs. Muthen,
We are running a mediation model with three exogenous variables - (1 continuous and two indicators for race/ethnicity), two mediating variables (one continuous and one dichotomous) and one outcome variable (dichotomous). For different paths we are calculating the mediation proportion, defined as the indirect effect divided by the total effect (indirect + direct effect). We would like to be able to calculate confidence intervals for mediation proportion by using the estimates from each individual bootstrapped dataset. The question is: Can M-Plus output into a separate dataset the individual bootstrapped estimates of the direct and indirect effects for a given model? |
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| Mplus does not saved indirect effects and does not saved results from each bootstrap replication. |
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| Emily Blood posted on Tuesday, October 09, 2007 - 6:15 pm
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Within the MC facility, is there a way to output indirect effect values and standard errors of indirect effects for each MC replication? I am currently outputting the parameters from each MC replication, but am not able to output the indirect effects and their standard errors from each replication, only the mean and se of all indirect effects from all MC replications. Is this possible in Mplus?
Thanks. |
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| No, results from MODEL INDIRECT are not saved. The only way to obtain them would be to save all of the data sets and analyze them one at a time. |
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| Eric posted on Monday, June 16, 2008 - 10:20 pm
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| I am using cinterval(bcbootstrap) to get confidence intervals for indirect effects in a path analysis model with 4 mediators. Though I get confidence intervals for the specific indirect effects, the confidence intervals for the rest of the path estimates are all zeros. Does this mean that I should not trust the CIs for the specific indirect effects? |
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| It sounds like you are using an old version of the program. I think there may have been a problem some time ago. I suggest using Version 5.1. |
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| Eric posted on Tuesday, June 17, 2008 - 9:56 am
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| Is it possible to get more decimal places for the confidence intervals when using cinterval(bcbootstrap). One of my confidence intervals ranges from 0.000 to 0.050 and I would like to be able to say that the effect is significant. I have tried using the savedata command, but I am not sure what to ask for, since the results option does not seem to include the confidence intervals. Thanks for your help. |
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| Confidence intervals are not saved. You can rescale your variables by dividing them by a constant using the DEFINE command. |
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Dear Drs. Muthen,
We're running a mediational SEM model, in which we have an X, a Y and two mediators, Ma and Mb. When we're running two seperate models, both Ma and Mb fully mediate the X-->Y relationship (using bootstrapped standard errors). But when we model both mediators in the same SEM model, the Ma mediator is no longer significantly related to Y. All other paths are significant, including the X-->Ma. We also ran an regression analysis and found that Ma predicts unique variance in Y after controlling for both X and Mb.
1. What can we conclude about Ma as a mediator of X-->Y?
2. Could it be that the finding that only Mb (and not Ma) mediates the X-->Y when tested in the same model, is a statistical artifact? And if that is the case, how does that happen?
3. Alternatively, if it is not an artifact, what can we conclude that Mb is a more important mediator than Ma when compared in the same model? How should one then report that Ma functioned as a mediator when tested alone and when tested in a regression model and was found to predict unique variance in Y.
I thank you in advance and for a great discussion board. |
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| If Ma and Mb are highly correlated, there may not be anything left in y to predict beyond what one of the mediators predicts. |
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Thak you so much for your prompt reply.
That's probably right, that the high correlation btw Ma and Mb messes this up. My question is still, though, what can we conclude about Mb as a mediator? Is it an artifact, that is, could it just as easily have been Ma that ended up with the significant path or neither?
If Ma and Mb are so highly correlated that the Ma --> Y path becomes non-sig., it doesn't explain why the Mb --> Y is significant? Nor why we found that the unique contribution of Ma was sig. after controlling for X and Mb in a regression model. Or does it? |
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| This topic - without the mediation angle - is discussed in the linear regression literature under the heading multicollinearity. You may want to take a look at that. I don't think it is possible to conclude about the joint role of Ma and Mb in such a situation, only that each entered separately is a mediator. You may also want to consult the new mediation book by David MacKinnon to see if he has some wisdom on this topic. |
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I have question regarding MPlus output. I used bootstraping to test a mediation effect. On the output for Model Indirect command, I have columns for "Estimates S.E. Est./S.E. StdYX StdYX SE StdYX/SE."
Can you please explain me what StdYX, StdYX SE, and StdYX/SE refers to?
Which one is the test of indirect effect?
Thanks.
Metin |
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| The test is the ratio of the estimate to the standard error of the estimate. Please see Chapter 17 for a description of the columns of the Mplus output and information about the various standardizations. |
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Hello,
can I use FIML to test mediation (with bootstrapping) or to model interaction (both for latent variables)?
And in the case of using multiple imputation how do I treat the fit values and indirect and direct coefficients? Can I just use the (so)-called rubin formular (which would be like a mean)?
Thanks for your help, Miriam |
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I would like to explain my post above a little more: maybe it is not so clear what I am trying to ask please exuse that:
I am trying to model mediation (with bootstrapping) and moderation (with interaction) but I have missings which I would like to impute: Now I have 5 datasets and Iam doing my analysis with all those data sets, because I cannot read in the 5 data sets at once, because beforementioned modelings won't allow that. But I don't know how to handle the coefficients or fit values of those 5 analysis could you me give me an advice how to handle this? (Is that done with the rubin formular?)
Further I read fiml is an appropriate way to handle missing data: but as far as I read here it's more used in multilevel or group analysis. So that was an idea that this could have been a way for my issue.
Thanks for your help, Miriam |
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| You can use the IMPUTATION option of the DATA command to analyze a set of imputed datasets. Correct parameters estimates and standard errors are calculated. Fit statistics are provided. |
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| Maybe I did something wrong. But I had problems useing this command for the modeling interaction or mediation (bootstrapping). |
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Maybe I did something wrong, but I had problems using the command IMPUTATION. This is the error I get
*** ERROR
MODEL INDIRECT is not allowed with TYPE=IMPUTATION.
The same error shows up when I try to model interaction.
So thats why I am modelling it with each of the five data sets and would like to ask if the fit values can be integrated by calcutating the mean of them? |
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| You cannot use MODEL INDIRECT with TYPE=IMPUTATION but you should be able to use XWITH. I would use MODEL CONSTRAINT with TYPE=IMPUTATION to define the indirect effects. Although the parameter estimates are simply an average across imputed data sets, the standard errors and chi-square are not and cannot be computed by hand. If you have further problems along this line, please send them along with your license number to support@statmodel.com. |
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| Thank you so much for your help. I will first try to model it with the commands you recommended. If this will not work out - I will come back to you later and send you my data. |
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