# Increase of odds ratio in a non-significant predictor

I ran a hierarchical logistic regression with 2 blocks/predictors:

I found out that block 1 is not a significant predictor, but block 2 is. Still: the odds ratio for block 1 is MUCH higher (2.90) than for block 2 (1.31) and increases when the second block is added. How can it be so high but still not significant? Could that be caused by an underlying effect? Or could that simply be, because my sample seize is too small, so the values are not reliable?

• I'm unclear exactly what you did. Was it: Model 1 logit(Y)~ Block 1, model2: logit(Y) ~ Block 1 + Block 2? And what are the blocks? Single variables? Of what sort? Feb 9, 2014 at 11:22
• @PeterFlom Exactly, that was the model. Each block contains one quantitative variable. variable 1 = standarized residuals of linear regression for each individual. those residuals resulted from predicting pre-test-values out of post-test-values (this was done to get a value for the individual learning potential), variable 2 = lobal score of a working skill assessmentstandarized residuals of linear regression for each individual. those residuals resulted from predicting pre-test-values out of post-test-values (this was done to get a value for the individual learning potential)
– Lara
Feb 9, 2014 at 12:27
• It is very unusual to use residuals from one model as variables in another. What are you trying to do? Feb 9, 2014 at 12:49
• @PeterFlom I was trying to define the change score in a learning potential test from time 1 to time 2, based on Weingartz, Wiedl & Watzke (2008). There is says:"In contrast, and using statistical parameters of CTT, measures based on residuals of linear regression use the differences between the actual and predicted posttest score to reflect individual learning potential under consideration of the regression effect of the sample. Assuming that error values are perfectly random, the residuals can be taken as an estimate of the differential treatment effect." Or did I get that wrong?
– Lara
Feb 9, 2014 at 13:00
• When I talked about that with my professor he said I could use those residuals as a continous variable in the logistic regression.
– Lara
Feb 9, 2014 at 13:01