Hmm, need to know a bit more about the circumstances. What are the numbers involved (i.e. exposed vs unexposed, with outcome vs without outcome). Usually an odds ratio like this would be the result of a statistical artifact resulting from very low numbers.

it is a finding in logistc regression, there are 9 DV and 1 IDV with 3 categories, total number is 5000 and 10%, 14%and 76% in each categories respectively,

The easy answer is no. You should check to see if you have collinear variables, that your variables are all correctly coded, and that your independent variables aren't small. Also, it's unclear from your response whether your IV or your DV (the outcome) has three responses. If it's your DV, then you can't use logistic regression - you either need to make it dichotomous or you need to use Ordered logistic regression. What Logistic will do (in some statistical packages) if you have more than two categories in your outcome is to code the zero as 0, and the numbers above 0 as 1 (i.e. it will dichotomize the variable for you). In your case, this could mean that only 10% of your sample is the comparison group and the other 90% are coded as 1, which could be problematic.

Your statistican may also be using a polycholric technique -- esp if you are using a mixed model with dichotamous and scalar variables. The purists would say this should not happen, but sometimes when you censor the data to make it dichotamous you lose so much information that your statistical model is invalid.

You need to talk to a statisican -- if the results are from that you need to know what the unit of chance is (ie if the BPRS goes up 10 points your risk of seeing a suicide attempt doubles (as a made up example). If you have these results TALK TO A STATISTICIAN NOW. You need him or her to check your sums, anyway.

There might be some problem in the data.

Either there is a problem with data, or data analysis.

Mohamed,

Your sample size is large enough so you would not expect the adjusted odds ratio of that magnitude, I think that the error might have been due to coding your variables. Did you try univariate analysis? It might give you possible plausible explanations for your an odd AOR

Thanks,

Ben