Re: st: mlogit estimation p-value problem

[Richard Goldstein <richgold@ix.netcom.com>]

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
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