Thank you for this...I'll try to adapt it to my code. Actually, I tried a similar methods first, but then got stuck after the merging about how to extract the data I needed and I gave up on it after 2 days of frustration. Thanks...will give update soon. ... View more

Thank you, @Ksharp  ... I believe I understand your first solution, but not the second one. Would you mind explaining its logic? Thanks again. ... View more

Ok, with your help, I came up with the following code: ods select JS_Corr; /* Perform Spearman’s correlation for Job_Satisfaction and Respect at Work with potential confounders */ proc corr data=F0_Data fisher spearman; var Job_Satis Resp_Work; with _all_; ods output fisherspearmancorr = JS_Corr(where=(pvalue<.05)); /* Save the correlation table parameters with p<0.05 to JS_Corr */ run; ods pdf file="JobSatis_Resp@Work Confounders.pdf"; Title "Job Satisfaction - Respect at Work Potential Confounders"; Need to get rid of the X variables It seems to produce the desired output of finding variables that pair with both Job_Satis and Resp_Work an have a p <0.05, which is the desired outcome, except for these two issues:   1. How do I get rid of the variables coded as X ? They do not the desired requirement as those shown in the green boxes? A variable in the WithVar column has to be statistically significant with both Job_Satis (outcome) and Resp_Work (predictor), hence a confounding variable. Otherwise do not include in display table or JS_Corr array or anywhere.   2. How can I save the correct possible confounding variables (ordinals and nominals) in the green box to another array as distinct and not duplicated variables? I will be performing some analysis to reduce collinearity amongst these variables for a later ordinal logistic regression modelling. Either PCA (?) VIF (Polychoric ?) procedures are the candidates for now. @Rick_SAS , @Ksharp , @PaigeMiller    Seriously, thank you!!!   ... View more

My sincerest apologies ... I realize it can be very frustrating to the community I'm trying to ask for help. ... View more

Thanks ... my actual goal is to identify confounder variables in my dataset governing the relationship between Job Satisfaction (Y outcome, Ordinal, Likert 1 - 5) and WorkRespect (X Predictor, Ordinal, Likert 1 - 4). The characteristics of a confounder C are: 1) Y and C are statistically related (p <0.05) 2) X and C are statistically related (p <0.05) 3) C cannot be in the path between  X and Y, otherwise it becomes a mediator.    C (my dataset) is mostly ordinal and nominal, but binary and continuous variables exist.   I am trying identifying variables in my dataset that meet the first two criteria, hence the original goal for this post. Used SPSS, as I said earlier on a smaller set, then ported the files over into excel and did some painful excel math logic, which I wanted to avoid now, on this much larger dataset. BTW, I compared Spearman and Kendall results form the first two criteria and they were within 10% of each each. Both gave the same number of variables that meet criteria #1 and #2.   How to determine the 3rd criteria is currently mystery to me now, beyond just drawing Directed Acyclic Graphs (DAGs) and using "strategic common sense" as a colleague told me, to determine whether C truly comes before X or simply does not belong. Literature searches so far have not been that helpful.   Anyways, to preventing bloating this thread any further, I wanted to concentrate on SAS code that helps meet first two criteria. Hope this makes sense and my apologies for any confusion on my part. ... View more

Thanks @Rick_SAS . You guessed it.  I have a total of 100 variables (of all types) that most will not be significant, hence the need to sort them out by their p-values. I do not want to have a 100x100 matrix table that I would then manually sort out the statistically significant variables in Excel. Did it before on a 50x50 matrix from another dataset generated in SPSS (of which < 50 were significant), though I had hoped SAS would be more streamlined about this. ... View more

Thank you ... how can I adapt the code to handle all numeric and categorical variables? I have continuous, ordinal, binary and nominal variables I need to perform the aforementioned  correlation tests on and generate the two Spearman and Kendall tau-b correlation matrices. My  apologies, but I'm very new to SAS.   Thanks. ... View more

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