# Thread: Zero Inflated LOGISTIC Regression?

1. ## Zero Inflated LOGISTIC Regression?

Hello all,

I recently gave a presentation describing my use of logistic regression (YES/NO response variable) and someone suggested that because there was such a high percentage of 0's (NO) that logistic regression wasn't the best analysis to use. However, when I asked if they thought Propensity Score Matching was what they were thinking of in terms of a better test they didn't really seem to imply that's what they were thinking - plus I don't think Propensity Score Matching is really quite I want.

Is there any type of regression technique similar to logistic regression but used specifically when the NO response greatly outnumbers the YES response? I'm just not sure what this person was thinking of and they never really told me (and I don't know their name or anything to e-mail them)

2. ## Re: Zero Inflated LOGISTIC Regression?

I think the speaker may have been talking about zero inflated Poisson or zero inflated negative binomial regression. Propensity scores are very different. They are more of a way of attempting to make non random assigned data approximate random assignment. Basically you use the predictors to create "balanced" groups.

3. ## Re: Zero Inflated LOGISTIC Regression?

Yes, I was also thinking that maybe the person asking the question was thinking about Zero-Inflated Poisson - which is not at all what I am trying to do (I'm not trying to model any type of count). Maybe they just didn't know that Poisson and Logistic regression are two completely different techniques used in different situations.

Also, since Propensity Score matching does use logistic regression when trying to match members I thought perhaps that's what they were thinking - but again, I'm not trying to make any type of comparison so there is no need to try and create balanced groups (since I am specifically interested in finding how the YES and NO groups are actually different).

I guess I could ask this then: is there some type of alternative to a logistic regression when one of the responses has very low numbers. For example, let's say that only 50 out of 1,000 responded YES and 995 responded NO....would there be a more suitable technique than logistic regression to try and determine which characteristics are good predictors of whether or not a person will respond YES or NO?

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