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From |
Richard Williams <richardwilliams.ndu@gmail.com> |

To |
statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 11 Jun 2013 08:47:37 -0500 |

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.

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? > * > * 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/ -- Andreas * * 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/

------------------------------------------- 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/

**Follow-Ups**:**Re: st: mlogit estimation p-value problem***From:*Richard Goldstein <richgold@ix.netcom.com>

**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>

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