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From |
Maarten Buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: cascading dummies |

Date |
Tue, 2 Oct 2012 09:59:17 +0200 |

On Tue, Oct 2, 2012 at 1:18 AM, Shikha Sinha wrote: > You and also STB mentioned that coeff in regression using ordinal > dummies can be constructed by subtracting the coeff from the > regression using the standard dummies. Is this true for non-linear > regression as well (Logit, probit)? <snip> > Does the odds ratios of 1.397527 mean that an increase in the dummy > from 3 to 4 results in 39% increase in literacy? I would do this type of analysis with -contrasts- command. Below is an example that deals with the comparison of -cascade- and -constrast-, the interpretation, and its use in a -logit- model. *--------------------- begin example ---------------------- //========================== prepare data sysuse nlsw88, clear gen byte ed = cond(grade < 12, 1, /// cond(grade == 12, 2, /// cond(grade < 16, 3, /// cond(grade < . , 4, .)))) label define ed 1 "less than high school" /// 2 "high school" /// 3 "some college" /// 4 "college" label value ed ed // =============== interpret "normal" coefficients // estimate model reg wage i.ed // This gives the mean wages for each category margins ed // The constant is the mean wage of less than high school: di _b[_cons] // The coefficient of 2.ed is the difference between // less than high school and high school, that is // the constant + 2.ed = the mean wage of 2.ed: di _b[_cons] + _b[2.ed] //=============== -cascade- is superceded by -constrast- // -cascade- cascade ed if ed < ., gen(foo) reg wage foo* // -contrast- reg wage i.ed contrast ar.ed // =================== interpretation of these contasts // get the mean wages within each eductional category margins ed, post // first contrast is difference in mean wage between // less than highschool and highschool: di _b[2.ed] - _b[1.ed] // second contrast is difference in mean wage between // highschool and some college: di _b[3.ed] - _b[2.ed] // third contrast is difference in mean wage between // some college and college: di _b[4.ed] - _b[3.ed] // I do these computations to show that we do not need // to do them, the results are already given in the // output of -contrast- // ====================== non-linear model gen byte good_job = occupation < 3 if occupation < . logit good_job i.ed, or // get the contrasts contrast ar.ed, eform // get the odds margins i.ed, expression(exp(xb())) post // first contrast is the ratio of the odds for less // than high school and highschool di _b[2.ed]/_b[1.ed] // second contrast is the ratio of the odds for // high school and some college di _b[3.ed]/_b[2.ed] // third contrast is the ratio of the odds for // some college and college di _b[4.ed]/_b[3.ed] *---------------------- end example ----------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * 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: cascading dummies***From:*Shikha Sinha <[email protected]>

**Re: st: cascading dummies***From:*Richard Goldstein <[email protected]>

**Re: st: cascading dummies***From:*Shikha Sinha <[email protected]>

**Re: st: cascading dummies***From:*Nick Cox <[email protected]>

**Re: st: cascading dummies***From:*Shikha Sinha <[email protected]>

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