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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Testing interaction terms |

Date |
Wed, 19 Jun 2013 09:36:38 -0400 |

note that var00 does not exist and I ignore it here; try: regress partner var11 var10 var01, hascons test (var10+var11)/2=var11 Rich On 6/19/13 9:27 AM, Verena Dill wrote: > What you wrote is exactly what I did in my regression (output below, > just for the matter of illustration I included the four categories; > var11: var1==1 & var2==1, var10: var1==1 & var2==0, var01: var1==0 & > var2==1, var00: var1==0 & var2==0; because of the below mentioned > structure of the data two of the categories are omitted). > ------------------------------------------------------------------------------ > > partner | Coef. Std. Err. z P>|z| [95% Conf. > Interval] > -------------+---------------------------------------------------------------- > > var11 | .7478253 .3528458 2.12 0.034 .0562602 > 1.43939 > var10 | .9636673 .3315029 2.91 0.004 .3139335 > 1.613401 > var01 | 0 (omitted) > var00 | 0 (omitted) > _cons | -.63364 .3095231 -2.05 0.041 -1.240294 > -.0269858 > > But my question is: how can I test if the coefficients _b[var11] and > _b[var10] are equal taking the "interaction"-nature of the variables > into account? Using only "test _b[var11]= _b[var10 ]" does not account > for that. Is there any other procedure I could use here (maybe similar > to contrast)? > > > > Am 19.06.2013 14:25, schrieb David Hoaglin: >> Verena, >> >> Because the data have no observations for var1==0& var2==0, it is not >> possible to express the combined effect of those variables (in the >> linear predictor) in the usual way, >> (effect of var1) + (effect of var2) + (interaction). >> One alternative approach is to treat the combination of var1 and var2 >> as a categorical variable with three categories: var1==0& var2==1, >> var1==1& var2==0, and var1==1& var2==1. >> >> David Hoaglin >> >> On Wed, Jun 19, 2013 at 8:06 AM, Verena Dill<dill@uni-trier.de> wrote: >>> I obtained the coefficients from a regression model and want to test >>> whether >>> or not the coefficients are significantly different from each other. >>> The problem now is that the two variables are related to each other like >>> interactions and only partly overlap. >>> >>> var1: variable 1 (dummy) >>> var2: variable 2 (dummy) >>> interaction: interaction of variable 1 and variable 2 >>> >>> tab var1 var2 >>> >>> var1 | var2 >>> | 0 1 | Total >>> -----------+----------------------+---------- >>> 0 | 0 122 | 122 >>> 1 | 322 256 | 578 >>> -----------+----------------------+---------- >>> Total | 322 378 | 700 >>> >>> Since no observations exist for var1==0& var2==0 I can only include the >>> interaction and one of the variables (just to mention that: From a >>> theoretical sense it makes sense to do so): "probit var1 interaction" >>> Now I want to test if I can reject the hypothesis that >>> _b[var1]=_b[interaction]. If I use the standard command "test" it >>> does not >>> account for the fact that these variables are related. >>> >>> Because of the nature of my variables I wanted to use the "contrast" >>> command >>> but this only works if I'd use something like this before: "probit >>> var1##var2" which is not solvable because of the above mentioned >>> fact that >>> var1==0& var2==0 does not exist in the data. >>> >>> Can anybody suggest another command that takes into account that the two >>> variables are interacted or has ideas on how to adjust the >>> "contrast"-command? >>> >>> Any help is greatly appreciated! >>> >>> Verena >> * >> * 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/ > * > * 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/ > * * 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: Testing interaction terms***From:*Verena Dill <dill@uni-trier.de>

**Re: st: Testing interaction terms***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Testing interaction terms***From:*Verena Dill <dill@uni-trier.de>

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