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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: mlogit estimation p-value problem |

Date |
Tue, 11 Jun 2013 09:05:13 -0400 |

in addition to Richard's point, there are models for ordinal data that do not make the proportional odds assumption; Stata includes one (officially) called -slogit-; in addition, one could use either continuation ratio or adjacent category models (see, e.g., Ananth, CV and Kleinbaum, DG (1997), "Regression models for ordinal responses: a review of methods and applications", _International Journal of Epidemiology_, 26 (6): 1323-1333 or Fullerton, AS (2009), "A conceptual framework for ordered logistic mdoels," _Sociological Methods & Research_, 38 (2): 306-347 Rich On 6/11/13 9:47 AM, Richard Williams wrote: > At 06:54 AM 6/11/2013, Andreas Chouliaras wrote: >> Dear All, >> >> I will try to address David's questions: >> >> First of all, why am I not using an ordered logit? Well, I believe >> that in an ordered logit, the odds of getting a value for the count >> equal to 5, instead of 4, are equivalent to the odds of observing 3 >> instead of 2. With such a constraint the estimates will be less less >> efficient if the odds are not proportional. And I don't think I have a >> strong reason why the odds should be proportional in my study. > > You can always test whether the proportional odds assumption is met. If > some variables meet the assumption while others do not, a partial > proportional odds model could be estimated with the user-written > -gologit2-. mlogit models aren't very parsimonious so personally I'd > rather see if some sort of ordinal model is viable. > > >> I have a total of 8 groups (bcE, bcW, bcP, bcC, tcE, tcW, tcP, tcC). >> The total observations are 1877. The groups starting with b (bcE, bcW, >> bcP, bcC) are examined separately than the groups starting with t >> (tcE, tcW, tcP, tcC). More specifically, my primary interest is to see >> the interactions of bcP with the other groups starting from b (bcE, >> bcW, bcC), and the same for tcP for groups starting with "t" (tcE, >> tcW, tcC). Thus, I use bcP as an independent variable in 3 different >> cases: bcE as the dependent, bcW as the dependent, bcC as the >> dependent. Also, I use tcP as an independent variable in 3 cases: tcE >> as the dependent, tcW as the dependent, tcC as the dependent. >> >> Thus, I am primarily interested in the results of 6 multinomial logit >> models: >> >> A: For the "b" groups >> 1) mlogit bcE vE eRE iRE bE bcP >> 2) mlogit bcW vW eRW iRW bW bcP >> 3) mlogit bcC vC eRC iRC bC bcP >> >> B: For the "t" groups >> 4) mlogit tcE vE eRE iRE bE tcP >> 5) mlogit tcW vW eRW iRW bW tcP >> 6) mlogit tcC vC eRC iRC bC tcP >> >> For these 6 models, I believe there is >> a problem for the results of model 2, outcome 5. >> >> Now, regarding the observations of outcome 5: >> >> bcW has 4 observations for outcome 5, tcW has 3 observations for >> outcome 5. I am putting the numbers of observations for outcome 5 for >> the other groups >> >> bcE : 18 tcE : 11 >> bcP : 17 tcP : 12 >> bcC : 41 tcC : 31 >> >> So for the problematic case of model 2, the dependent variable (bcW) >> has 4 observations for outcome 5, while bcP has 17. Maybe the problem >> is that bcW has only 4 observations as you mentioned. But on the other >> hand tcW has only 3 observations for outcome 5 as well. >> >> Furthermore, when I drop the bcP from model 2, the coefficients are >> significant for 4 of the other 5 variables. >> >> How do you think I should deal with these issues? >> >> >> On Mon, Jun 10, 2013 at 12:14 PM, David Hoaglin <dchoaglin@gmail.com> >> wrote: >> > Dear Andreas, >> > >> > It is difficult to give good suggestions without seeing your Stata >> > commands and output. >> > >> > I am puzzled by your analysis. If the dependent variable is actually >> > a count (which can take values of 0 through 5), that would make the >> > six outcome categories ordered. A multinomial logistic regression >> > treats the outcome categories as unordered. You could consider an >> > ordinal logistic regression, but that would not use the equal spacing >> > of the count. >> > >> > You did not mention the number of groups or the total number of >> > observations. Perhaps the outcome of 5 has too few observations. >> > >> > David Hoaglin >> > >> > On Mon, Jun 10, 2013 at 4:42 AM, Andreas Chouliaras >> <adhoul@gmail.com> wrote: >> >> Dear all, >> >> >> >> I estimate an mlogit for a discrete dependent variable that takes the >> >> values 0 to 5 (count variable). I have different groups thus I have >> >> different count variables for each group. At a later point I want to >> >> see whether there is some relationship between the dependent variables >> >> of the different groups, and I use the count variable of one group as >> >> an independent variable for another group. But this causes the >> >> following problem: for outcome 5, all coefficients are insignificant. >> >> If I remove the count variable, most of the coefficients are >> >> significant, so I guess there must be something wrong with the >> >> inclusion of the count variable. My initial guess was >> >> multicollinearity, but using the "collin" command I don't get any very >> >> high VIFs. Any idea what might be the reason? >> >> -- >> Andreas > ------------------------------------------- > Richard Williams, Notre Dame Dept of Sociology > OFFICE: (574)631-6668, (574)631-6463 > HOME: (574)289-5227 > EMAIL: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: mlogit estimation p-value problem***From:*Andreas Chouliaras <adhoul@gmail.com>

**Re: st: mlogit estimation p-value problem***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: mlogit estimation p-value problem***From:*Andreas Chouliaras <adhoul@gmail.com>

**Re: st: mlogit estimation p-value problem***From:*Richard Williams <richardwilliams.ndu@gmail.com>

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