hey guys, I've developed a logit model to be applied to three different sets of cross-sectional data(the same model specification with differenet samples). What I'm trying to uncover is whether there are changes in the substantive effect of a given independent variable (IV) on the dependent variable (DV) controlling for other explanations at different times and across time. the first two models are logit models and third model is exlogistic model because the IV "interdependence" always associates with the same outcome when interdependence=!1
My questions are1)How do I assess increased / decreased size in the association between the IV and DV? (2) Can I simply look at the different magnitudes of the coefficients across the logit and exlogistic models ?(3) why the the standard error of the IV "interdependence" is N/A
the first model
Iteration 0: log likelihood = -36.563979
Iteration 1: log likelihood = -35.084441
Iteration 2: log likelihood = -35.001635
Iteration 3: log likelihood = -35.001431
Iteration 4: log likelihood = -35.001431
Logistic regression Number of obs = 99
LR chi2(4) = 3.13
Prob > chi2 = 0.5371
Log likelihood = -35.001431 Pseudo R2 = 0.0427
collaboration Odds Ratio Std. Err. z P>z [95% Conf. Interval]
interdependence1 2.21211 1.580962 1.11 0.267 .5450864 8.977344
novelty .9071175 .4687204 -0.19 0.850 .3294851 2.497419
incentive 1.200299 .4840858 0.45 0.651 .5444981 2.645957
incident1 .9977701 .0023237 -0.96 0.338 .993226 1.002335
_cons .178827 .2427856 -1.27 0.205 .0124967 2.55901
Note: _cons estimates baseline odds.
the second model
. logit collaboration interdependence1 novelty incentive incident1 if period==2,or
Iteration 0: log likelihood = -79.159074
Iteration 1: log likelihood = -52.106663
Iteration 2: log likelihood = -47.194014
Iteration 3: log likelihood = -46.644318
Iteration 4: log likelihood = -46.630959
Iteration 5: log likelihood = -46.630915
Iteration 6: log likelihood = -46.630915
Logistic regression Number of obs = 156
LR chi2(4) = 65.06
Prob > chi2 = 0.0000
Log likelihood = -46.630915 Pseudo R2 = 0.4109
collaboration Odds Ratio Std. Err. z P>z [95% Conf. Interval]
interdependence1 70.25622 73.49304 4.06 0.000 9.042052 545.8868
novelty 1.77801 .5997315 1.71 0.088 .917952 3.443885
incentive 1.497619 .5009724 1.21 0.227 .7774339 2.884954
incident1 1.002408 .006256 0.39 0.700 .9902215 1.014745
_cons .0020589 .003575 -3.56 0.000 .0000685 .0618914
Note: _cons estimates baseline odds.
the third model
observation 63: enumerations = 1411544
note: CMLE estimate for interdepen~1 is +inf; computing MUE
Exact logistic regression Number of obs = 63
Model score = 23.69152
Pr >= score = 0.0000
collaborat~n Odds Ratio Suff. 2*Pr(Suff.) [95% Conf. Interval]
interdepen~1 32.16168* 21 0.0000 4.767067 +Inf
novelty .8263797 30 0.7101 .3716871 1.627337
incentive 3.686022 35 0.0270 1.118811 24.93683
incident1 .9940054 4419 0.2823 .9857038 1.003811
(*) median unbiased estimates (MUE)
. estat se
collaborat~n Odds Ratio Std. Err.
interdepen~1 32.16168* N/A
novelty .8263797 .2806791
incentive 3.686022 2.326282
incident1 .9940054 .0046159
(*) median unbiased estimates(MUE)