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Daniel posted on Wednesday, November 09, 2005 - 7:53 am
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I ran a SEM with a combination of categorical and continuous indicator variables. I tested for several indirect effects with and without bootstrapping. Without bootstrapping, two out of three of my specific indirect effects were significant, p < .05, and the other wasn't close. With bootstrapping, however, one of the key paths was significant, p < .05, and the other was significant, p < .10. Which result is more valid, if such a thing exists, when modeling with continuous and categorical indicator variables, bootstrapping or not bootstrapping? |
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What is your sample size? |
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Daniel posted on Tuesday, December 06, 2005 - 8:41 am
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Sorry it took sssso long to get back, but this analysis was put on hold for a while. The sample size is 868. |
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Hmm. I think you should send your input, data, output, and license number at support@statmodel.com so we can see exactly what you are finding. |
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Daniel posted on Tuesday, December 06, 2005 - 12:11 pm
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Ok, it's on the way |
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JOEL WONG posted on Friday, January 29, 2016 - 5:50 am
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I am trying to understand when I can infer that two indirect effects in multigroup SEM (using bootstrapping) are significant different from each other. Assuming I have measurement invariance, if the Mplus output shows that the 95% confidence intervals of the indirect effect for group A (e.g., .01 - .05) doesn't overlap with the 95% confidence intervals for group B (e.g., (.06 - .10), is that sufficient evidence that the two indirect effects are significantly different? Or do I need to use some other method, e.g., the Wald test to demonstrate that the two indirect effects are significantly different? |
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You can create the indirect effects in MODEL CONSTRAINT and create a new parameter that is their difference. You will obtain a z-test and p-value for this. |
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JOEL WONG posted on Saturday, January 30, 2016 - 4:31 am
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Thank you, Linda. The MODEL CONSTRAINT is a good idea. I guess what I want to know is, can we make an inference on significant differences between 2 indirect effects based solely on the Mplus output showing that the 95% confidence intervals for group A (e.g., .01 - .05) doesn't overlap with the 95% confidence intervals for group B (e.g., (.06 - .10)? |
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No, only if those indirect effect estimates are independent as judged by TECH3. Instead, do what Linda suggested. |
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I am interested in this topic as I am comparing indirect effects, which involve multiple mediators, across three racial/ethnic groups. I already had results of bias-corrected 95% CI for each group using BOOTSTRAP in Mplus. According to Mplus User Guide (v. 7), TECH3 cannot be used in conjunction with the BOOTSTRAP option of the ANAYSIS command. I suppose I can test indirect effects using MODEL INDIRECT and request TECH3 output. In terms of requesting TECH3 output, should I do this separately for each racial/ethnic group? Or, should I include all three groups together in one Mplus syntax file? Also, what should I look for in the TECH3 output (estimated covariance/correlation matrices for the parameter estimates) to determine if indirect effect estimates are independent across three racial/ethnic groups or not? Thanks, in advance, for responding to my questions. |
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Why not do a multiple-group analysis in which you don't need TECH3, only Model Indirect. |
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Thanks for the quick response! Yes, I have been looking into multiple group comparisons on specific indirect effects across three groups. Your previous response mentioned TECH3 output, that's how I got curious about how to find out whether indirect effect estimates across groups are independent or not. |
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They are independent if there are no parameters that are held equal across groups. |
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Got it. Thanks!! |
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