Yes, the whole survey has 400 observations but I'm only working with 3 main observations per time period plus confounders. ... View more

In general, can you explain to me why my simple survey a complex survey for SAS? I only have two groups of people with only 3 observations. ... View more

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?   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. ... View more

Thank you for this clarification and now I'm a bit confused. If PLS only works with continuous response variables (SAS help is about as clear as mud on this matter), then are these my only options for collinearity reduction amongst mixed variables?   1. Use PROC REG with the CLASS statement that contains my categorical variables, to perform VIF or GVIF analysis. Then eliminate those variables (as @Ksharp had suggested) with a VIF score > 5 (my threshold for collinearity for now), then continue with PROC LOGISTIC for ordinal regression with the new uncorrelated variables.   2. Use PROC PCA with the risk of ending up with a reduced set of uninterpretable components.   3. What do you suggest is best way to reduce multicollinearity amongst 35 confounders (categorical and continuous variables) to improve a logistic regression model of 1 ordinal outcome and 1 ordinal predictor?   BTW, all my data is from a 400 respondent survey, if that helps. Not sure sas survey specific functions help much here. ... View more

So ... is it safe to say those predictors with very small or minute coefficients do not contribute much to the model, hence they could be potential candidates for 'elimination'? ... View more

Ok, thanks for the clarification. So will PLS also generate regression coefficients that will map my 35 variables to the Y response in its model? ... View more

My original goal (which I hope I clearly stated and apologies if I didn't) was to reduce the number of highly correlated 35 mixed type variables to a lesser number that are not or much less correlated with each other. Won't the PROC CORR function with the /corrb option show correlation coefficients that could be used to target high correlation variables for elimination? Won't the PLS procedure you suggested do something similar? ... View more

Interesting and thank you ... this fits with the statement  @PaigeMiller said that you can define categorical variables in the CLASS statement of the PROC PLS procedure. So will the PROC logistic example you've shown accomplish the same goal of eventually producing a set of de-correlated variables for my model? ... View more

Thanks for the excellent referrals...I read the SAS help you alluded to regarding the CLASS statement that " Typical classification variables are  Treatment ,  Sex ,  Race ,  Group , and  Replication . If you use the CLASS statement, it must appear before the MODEL statement statement. Classification variables can be either character or numeric. By default, class levels are determined from the entire set of formatted values of the CLASS variables. "   Are they saying (fingers crossed) that I can define all variable types (binary, categorical and continuous) in this statement and all is golden? I see "Sex" as binary, "Race" and possibly "Treatment" as nominal. Not sure what type of variables Group and Replication might be.   Thanks for the PLS referral btw ... that PCA is not responsive to Y variables, just X independent ones only.  I also appreciate your patience with my newbie-ness. ... View more

Thank you for the fast response. Based on the article you posted and some other reading, yes, PLS is great de-correlator when used with highly collinear variables. But what I've read so far, all these variables are continuous only. Am I correct in this?   Corrected my original question to say I want a reduced set or number of mixed type variables that are de-correlated from each other. ... View more