@David_M wrote:
My apologies for not specifying at the outset that I was dealing with survey data. I didn't think it mattered if my confounders were survey based or not.
Never mind. But please be clear from now on that complex survey data complicates everything concerning statistics. Every statistical method you employ have to be complex survey data-adapted ones, not the generic ones. In fact, I am not aware of any deterimental effect collinearity would have upon the parameter estimates of the regression coefficients in logistic regression, but I know that they are severly biased if you neglect the complex survey nature of your data. It would therefore be much better if you pointed out that your data originated from complex surveys in the first place the next time you raise a question on complex survey data analysis.
@David_M wrote:
Thank you for your suggestions. Yes, this is a matched paired survey from a longitudinal study of 400 healthcare workers whose characteristics I'm trying to analyze at two time points only, pre- and post-Covid. The characteristics are Job Satisfaction (JS), Intention to Leave the workplace (ITL) and Respect/Civility at Work (RW), which are in the form of Likert scales (1 - 4 and 1 - 5). I want to develop 2 models by performing a standard logistic regression between JS and RW and another between ITL and RW at both pre and post-Covid time points, so 4 models total but similar analytical methods for all 4 models. The results, conclusions and implications for each model would be different.
As previously mentioned, I've identified 30+ mixed type categorical and continuous confounders that I want to add to my models but I need to eliminate or drastically reduce any correlation between them, hence preventing unstable regression estimates, inflated standard errors, etc. These confounders will be treated as standard predictors in my models. All 4 models will have and end up with these same set of de-correlated confounders.
Are there no SAS procedures or methods suitable for this task?
Unfortunately, to the best of my knowledge, the answer is "yes", unless you learn the formulae and compile codes on your own in SAS.
By the way, complex survey data analysis is a field of statistics receiving not that much attention, so there are really few researchers working on how to conduct statistical diagnostics like detecting for collinearity and correcting them. In fact, as Taylor H. Lewis, author of the book Complex Survey Data Analysis with SAS | Taylor H. Lewis | Taylor & Francis noted, statistical diagnostics of models for complex survey data are "in its nascent stage". It is therefore highly likely that you encounter a particular problem in this field and discover that to date, there is no way of dealing with it.
