I am doing a binary logit analysis, in which I'm trying to fit a model to my data to explain why some towns adopt open space subdivision ordinances (OP), using a handful of discrete and continuous independent variables. One of the explanatory variables, median household income, is coming back with an estimated beta of 0, with a S.E. of 0, and a p value of (0.14), and the Odds Ratio is 1 (S.E. of 0). It is highly unlikely that there is absolutely no relation between household income and towns passing a growth management ordinance, so this seems pretty odd. The rest of my variables have normal looking estimates and errors. This is my first time asking a question on this site, so please forgive me if I leave out critical information - I'll explain more if needed. I tried to cut and paste my output below, but the formatting of the table disappeared, making it impossible to decipher.
Also, my descriptive statistics for household income look fine, with realistic minimum, maximum and mean numbers, and the original data doesn't show anything obviously wrong with the data itself, which makes me think I'm doing something wrong when estimating my model.
On a side note, my odds ratio results for percent change in housing from 1990-2000 (PCHU90_00) look really high, and well, odd =) I'm not sure what's going on there, either.
Any suggestions would be appreciated. Thank you!
Here's the model:
LOGIT MODEL OP = CONSTANT+COAST+LA_SQMI+PCHU00_10+PCHU70_80+PCHU80_90+PCHU90_00+CONSTEMP+ HHINCOME+LT+PCHPOP00_10+PCHPOP60_70+PCHPOP70_80+PCHPOP80_90+PCHPOP90_00+PCTDEM
And the results for the two variables I'm concerned about:
HHINCOME: 0 Beta (0 SE), z=1.46, p=0.14, 1.0 Odds Ratio (0 SE) PCHU90_00: 9.64 (3.89 SE), z=2.47, p=.01, 15310.11 Odds Ratio (59625.59 SE)
Model fit info seems okay -
Log-Likelihood of Constants only Model = LL(0):-83.14 2*[LL(N)-LL(0)] :37.13 df:15 p-value:0.00 McFadden's Rho-squared 0.22 Cox and Snell R-square 0.25 Naglekerke's R-square 0.35