Dear forum,

I am having trouble with identifying outliers using the post estimation-command in a logit regression.

I'll explain my model briefly:

I am working on my bachelor thesis about how citizens' prior attitudes impact their ability to asses performance information correctly.

I am using logistic regression to estimate the respondents assessment (1 = correct assessment; 0 = wrong assessment). The prior attitude of the respondents are measured with a dummy variable (1 = positive attitude towards public service provision; 0 = negative attitude towards public service provision).

It is a survey-experiment with four different treatments. So the if-command at the end of the logit-command is used to specify which of the four experimental groups the effect is for.

I want to check for influential observations using the dbeta-command.

Here is my Stata-command.

logit correct_assessment public_private if treatment == 1 & group == 1
predict tempdbeta1, dbeta
sort tempdbeta1
list correct_assessment tempdbeta1 if tempdbeta1>0.2 & tempdbeta1 !=.

What happens is that Stata considers every observation in the model influential (with a dbeta-value > 0.2).

I can not figure out why this is happening. I have considered that it might be due to the fact that I am using a dummy as both the dependent and independent variable. Furthermore, I have wondered if it has something to do with my experimental design.

I hope that somebody can help.

All the best,
Mads

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