```Date: Mon, 8 Aug 2005 15:21:04 -0400 Reply-To: Marc Scoppettone Sender: "SPSSX(r) Discussion" From: Marc Scoppettone Subject: Re: multinomial vs. ordinal Comments: To: Winny Chi Hi Winnie, Ordinal regression is a special case of multinomial logistic regression. Both require a dependent variable that is categorical with more than two categories. However, the ordinal model also requires that the categories be ordinal. The ordinal regression is more commonly referred to as the proportional odds model. For example, suppose you have five categories. The multinomial logisitc model will have a base outcome (say, outcome 5), and then estimate the odds ratio of any other outcome relative to outcome 5. It makes not assumptions about the relation between, say outcome 1 and outcome 2 except the restriction that all of the probabilities add up to 1. However, the proportional odds model will estimate the odds of being in, say, outcome x or better versus the outcome of being in outcome y or worse. This goes for all x > y (in terms of ordinality). The proportional odds model assumes the same affect of an explanatory factor for all levels. However, the general multinomial logistic model will give one parameter estimate for each explanatory factor at each of (n- 1) levels of the dependent variable. Alan Agresti's "Introduction to Categorical Data Analysis" has a nice simple discussion (much much clearer than mine!!!) On Mon, 8 Aug 2005 12:12:18 -0700, Winny Chi wrote: >Dear List: > > > >Would anyone here kindly advise me the difference between multinomial and >ordinal regression analysis in terms of what type of DV and IV fit? It seems >to me that they both deal with categorical DV that has more than two >possible values. Any comments? > > > > > >Thanks. > > > > > >Winny > > > >yanfangc@usc.edu ```

Back to: Top of message | Previous page | Main SPSSX-L page