I am working on my thesis in which i have couple of independent variables that are categorical in nature and the depndent variable is dichotomus. Initially I run univariate analysis and added the variables with significant p-values (p<0.25) in my full model. I have three confusions. Firstly, I am looking for confounding variables by using formula "(crude beta-cofficient - adjusted beta-cofficient)/ crude beta-cofficient x 100" as per rule if the percentage of any variable is >10% than I have considered that as confounder. I wanted to know that from initial model i have deducted one variable with insignificant p-value to form adjusted model. Now how will i know if the variable that i deducted from initial model was confounder or not? Secondly, I wanted to know if the percentage comes in negative like (-17.84%) than will it be considered as confounder or not? I also wanted to know that confounders should be removed from model? or should be kept in model? Lastly, I wanted to know that I am running likelihood ratio test to identify if the value is falling in critical region or not. So if the value doesnot fall in critical region than what does it show? what should I do in this case? In my final reduced model all p-values are significant but still the value identified via likelihood ratio test is not falling in critical region. So what does that show?
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