I'm using logistic regression to analyse a dataset on managerial decision making. Managers have made decisions to fire 2 employees out of a group of 8 from a fictional company used in the research setup. This data is coded binominally (0=chosen to retain, 1 = fired). To find out what variables have an impact on the fictional employees getting fired, I gathered data on their opinions using 7-point likert scale questions (i.e. academic background is important, 1-7), and ran binominal logistic regressions using SPSS with a number of these kind of variables on the decision of firing a certain employee or not.

The problem I'm having is that in the complete model, some variables are not even close to significant (with values in the sig. column of more than 0,500). However, when I run logistic regressions with just one variable at a time, in some cases they are significant, with values in the sig. column of <0,05. The omnibus chi-square is sometimes not significant for these models, other times it is, I'm not sure how important this is for the relevance of the variable.

I've tried looking at correlations between variables, but in some cases a variable that doesn't correlate with any of the other variables will become significant if tested individually.

Now, I'm not sure how to find out or interpret which of my variables are of influence in the decision whether or not to fire a certain member, and which aren't.

Also, I don't know how to interpret the unstandardized B-values and the Exp(B) values, because testing a variable individually results in a different B-value from those found when I include all the variables in the model. This means I'm not sure what to report on the actual effect (Exp(B)) of these variables on the decision.

Thank you for any and all help!