
Message/Author 

Daniel posted on Wednesday, November 09, 2005  7:53 am



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? 


What is your sample size? 

Daniel posted on Tuesday, December 06, 2005  8:41 am



Sorry it took sssso long to get back, but this analysis was put on hold for a while. The sample size is 868. 


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. 

Daniel posted on Tuesday, December 06, 2005  12:11 pm



Ok, it's on the way 

JOEL WONG posted on Friday, January 29, 2016  5:50 am



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? 


You can create the indirect effects in MODEL CONSTRAINT and create a new parameter that is their difference. You will obtain a ztest and pvalue for this. 

JOEL WONG posted on Saturday, January 30, 2016  4:31 am



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)? 


No, only if those indirect effect estimates are independent as judged by TECH3. Instead, do what Linda suggested. 

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