Practically, I tried to modelled are variable into multivariate analysis using enter method where I simply put every factors into one model. Oddly enough, the odds ratio does not seem to be correct as it appears to be this way (exhibit I).
There are some variables whose EXP-B seems to be wrong. I use complex sample and all analysis are conducted in a weighted manner using complex sample analysis.
No, it would not be possible to have an odds ratio of 1,040E+12 (or 1.04 x 10^12). An odds ratio represents the ratio of the odds of an event occurring in one group compared to the odds of the event occurring in another group.
An odds ratio of 1 indicates that there is no association or difference in the odds between the two groups being compared. An odds ratio greater than 1 indicates a higher odds of the event occurring in one group compared to the other, while an odds ratio less than 1 indicates a lower odds in one group compared to the other.
However, an odds ratio of 1,040E+12 would imply an extremely large difference in the odds between the groups, suggesting an extremely strong association. Such a large odds ratio is highly unlikely in most research scenarios and would raise questions about the study design, sample size, or the data itself. It is important to critically evaluate and interpret odds ratios in the context of the study and consider the precision and confidence intervals associated with the estimate.
This is often a sign of partial or complete separation (though collinearity can also be an issue; or both).
e.g., see https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logistic-regression-and-what-are-some-strategies-to-deal-with-the-issue/#:~:text=What%20is%20complete%20separation%3F,and%20X2%20are%20predictor%20variables.
Take sample from your data , check auto correlation and PACF.
If the auto correlation decreases with the lag then check for outliers.
Remove that from data then find. You will get correct result.
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