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From |
"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Help with mlogit questions; IIA; choosing outcome variables; collapsing outcomes, multinomial logistic regression |

Date |
Thu, 14 May 2009 14:06:55 -0400 |

Tomas M wrote: >>QUESTION: Suppose that the mlogtests give results that suggest that the alternatives (i.e. outcomes) CAN be combined (i.e. gives a high p-value for evidence for the null, as stated above), does this mean that if we choose to combine the alternatives, we can do so in the model? (if we choose to combine them based on our knowledge of the data, etc.). Or does this mean that we SHOULD combine the alternatives?<< The answer to "Should these be combined?" seems to depend on "What point are you trying to make?" Merging serves two purposes, one statistical (cut df) and the other model parsimony. The mlogit model is quite complex so if you can make your point with fewer categories that may be wise. However, it's hard to answer this without a lot more context. I would be very wary of merging categories in a way that isn't sensible regardless of what the statistical tests say. >>OTHER QUESTION: Suppose I have the following outcome variables: blue collar, white collar, professional, no job. How would I go about deciding whether to include "No job" as an outcome in my models? Suppose that having "No Job" included in my models, versus having it excluded, makes no difference to the estimated coefficients. What justifications should I use to decide whether to include "No Job" or exclude it? Is this solely a decision based on my own knowledge of the data, and whether or not it would be of interest to the readers?<< Not including a category may alter the population you're studying so you should be really careful. Dropping no job makes the sample conditional on being employed, which is a different population. >>Does the assumption of independence of irrelevant alternatives (IIA) have anything to do with this justification? I.e., if all of my outcomes satisfy this assumption (after running the Hausman and/or Small-Hsiao tests), and they are distinct and dissimilar, then it does not matter if I include "No Job" or not (and thus my choice for including or excluding it rests solely on my decisions as an analyst)?<< With collapsing categories? Yes, one way to make things closer to IIA and thereby avoid the use of more complex models such as mixed logit is to get rid of categories that are highly similar. See Red Bus/Blue Bus Problem. JV * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Help with mlogit questions; IIA; choosing outcome variables; collapsing outcomes, multinomial logistic regression***From:*Tomas M <anon556656@live.ca>

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