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st: Re: Ordinal independent variables in probit regression


From   "Joseph Coveney" <stajc2@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: Ordinal independent variables in probit regression
Date   Thu, 10 Apr 2014 09:15:02 +0900

Nyasha Tirivayi wrote:

How best can I use a likert scale/ordered predictor in a probit
regression? The variable has five response categories from Strongly
disagree to Agree (neutral is the third response option).

Should I include the variable as it is, where one category becomes the
reference? Or should I consider the variable to be continuous? Or
should I instead use the tab command to create dummies for all five
response options, and include the ones I am interested in ( e.g.
strongly agree and agree responsea)?

--------------------------------------------------------------------------------

There are numerous ways to include it as a predictor.  You could use factor
variables and then use -contrast- after fitting the model.  You could put the
scores in linearly (as a continuous predictor).  But it seems that you've
already hit upon the way that makes most sense from the standpoint of how best
to address the question of scientific interest:  create three indicator
variables, one for strongly agree, one for agree, and one for all of the other
scores--something like that below.  (You can accomplish the same thing using a
factor variable and then constructing particular contrasts of interest after
fitting the model.)

    generate byte strongly_agree = likertlike_score == "Strongly Agree"
    generate byte agree = likertlike_score == "Agree"
    generate byte others = !strongly_agree & !agree
   probit response c.(strongly_agree agree others)

Joseph Coveney

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