My binary logistic regression analysis (method=ENTER) model has all beta values are statistically non-significant.
Should I include these predictors in the final binary logistic regression equation?
In general if a test for the regression coefficient being statistically significantly different from 0 cannot be rejected it suggests that the variable has little influence on the outcome and hence should not be included. However, it could be that the sample size is small and hence the variance of the estimate of the regression coefficient is too large to exclude the possiblity that it is zero. So the question then becomes how much bigger than 0 do I need the coefficient to be for me to want to include it in the model. Then figure out how large a sample size you need to detect a difference from 0 of at least that magnitude. If the sample size is large and all the estimates are close to 0 then exclude them and look for other predictors that might be better. But if the sample size is small and say a 95% confidence interval for a predictor is wide enough to include 0 and your significantly high slope parameter then consider increasing the sample size before you decide.
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