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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 * * 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: Ordinal independent variables in probit regression***From:*Nyasha Tirivayi <ntirivayi@gmail.com>

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