In the ELS silo, what to do with--and/or how to best interpret--"zeros" frequently persist as common challenges. In the world of standard count data, possible approaches include Zero-inflated Poisson Regression and Zero-inflated Negative Binomial Regression. Ordered logit analyses, however, require something slightly different.
To illustrate, a standard example is social science research (and explicated in this brief yet helpful video) includes, e.g., a four-siloed response to the question "How many cigarettes did you smoke each day, on average (0, 1-7, 8-12, >12)?" The possible ambiguity of the "0" response is that it may include responses from people who smoked less than one cigarette per day, on average, as well as those do not smoke cigarettes at all. And if the research question is designed to get a smokers' daily volume, the "0" response from non-smoker respondents is qualitatively different than a "0" response from smokers.
To address potential distortions to the "0" response introduced by non-smokers in this hypothetical, Stata's ziologit command fits a model for ordinal outcomes with an expected "excess" of "0" responses.
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