Simon, if you had a categorical variable with two categories, such as male and female, and you created two dummies (¡®ismale¡¯, ¡®isfemale¡¯) and then used both as independent variables, the regression would fail
because of perfect multi-collinearity. You have to use all but one dummy variable. Could that be what happened here?
I hope you are doing well. When I used UCINET to test a node-level hypothesis, something went wrong. My dependent variable is centrality, and my independent variables
are some dummy?variables and some continuous variables. When I include all dummy variables, all Beta, T, and c.Sig disappear, whereas all p.Sig are 1. When I reduce one dummy variable, it becomes normal. Could anyone explain how this happens? Thanks.
