AMOS Bootstrapping - Mean Chi Square/df?


New Member
Hi, I am working on a project modelling a variety of psychological outcomes for AIDS-affected youth in South Africa.

Inevitably, some of our variables are not normally distributed, so we have used the bootstrapping procedure available in AMOS to estimate paremeters in the model.

Everything seems to be working fine, but I have a question regarding evaluating overall model fit. A number of texts say that chi square is extremely sensitive to sample size, and that chi square divided by degrees of freedom may be more appropriate (and this should be less than 3 - e.g. Blunch, 2008).

The bootstrapping procedure outputs a distribution of chi square statistics, as well as the mean chi square statistic for all the bootstrapped samples.

My questions: 1) is the mean chi square from the bootstrap procedure analogous to chi sq in the original ML estimnation, and 2) is it appropriate to divide the mean chi square statistic from the bootstrap procedure by degrees of freedom?

Unfortunately, the bollen-stine test is significant - but I have read that this test is also sensitive to sample size. All of the other fit statistics are either adequate or good

Any thoughts on this would be very gratefully appreciated
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