@yli33 wrote:
Hi Paige,
Yes, I am employing survey-based binary logistic regression models for the categorical variables in my dataset. Nevertheless, I have not observed any extraneous categories within the either the independent or dependent outcome variable although there are some missing values within both variables.
Regarding my previous questions, I would just like to know whether there may be some alternative explanations for those significant point estimates and relatively larger point estimates for a specific category of the dependent variable but not for the other category. While I have also confirmed the relative sample sizes for each independent/dependent variable category, I was only able to conclude that wider confidence intervals correspond relatively smaller sample sizes since some weighted percentages were greater than 10%.
Best,
Lisa
It really helps to show the code as a minimum.
One potential cause in addition to @PaigeMiller's comments is the sample design information you provide in the survey proc.
Clusters for example may have unexpected behavior if some of your clusters have 0 or 100 percent prevalence (no variability within the cluster) and only a few cluster providing the variability have relatively large differences between them.
You may want to look at the data with surveymeans / surveyfreq requesting the CV statistic. Largish values for this statistic, rule of thumb >.5, tell you that you may have suspect reliability for those sample cells.
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