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RE: st: Plotting interactions
From
<[email protected]>
To
<[email protected]>
Subject
RE: st: Plotting interactions
Date
Mon, 30 Sep 2013 11:50:34 +0000
This is a classic problem: In their survey of top political science journals, Brambor et al. (2006) found that about 31% of articles using interaction terms did not include all constitutive terms in their models. The provided .do files not only simplify the plotting of interactions, but also help avoid such common mistakes.
Best,
Kostas
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of John Antonakis
Sent: maandag 30 september 2013 13:43
To: [email protected]
Subject: Re: st: Plotting interactions
Right......not including the main effects is tantamount to having an omitted variable--the interaction will certainly correlate with its constituents.
See:
Evans, M. G. 1991. The problem of analyzing multiplicative composites.
American Psychologist, 46(1): 6-15.
Best,
J.
__________________________________________
John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management
Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor:
The Leadership Quarterly
Organizational Research Methods
__________________________________________
On 30.09.2013 13:34, David Hoaglin wrote:
> Hi, Amal.
>
> I won't speak to the subsequent programming, but it is unusual for the
> predictors in a regression model to include the interaction of two
> variables and not include the "main effect" of either of those
> variables. Would the results make better sense if your model included
> the main effects of ethnicity_bi2 and smoke2? You can include those
> and the two-variable interaction by using the ## operator instead of
> #. If you simply want to combine those two variables in a 6-category
> predictor (and have the first category, ethnicity_bi2 = 1 and smoke2 =
> 2, as part of the constant term), then that is what your current model
> does.
>
> David Hoaglin
>
> On Mon, Sep 30, 2013 at 4:41 AM, Amal Khanolkar <[email protected]> wrote:
>> Hi All,
>>
>> I'm trying to plot interactions post regression using the following syntax:
>>
>> The model:
>>
>> . regress bwtgestage_sd i.ethnicity_bi2#i.smoke2 sex ib2.magecat
>> i.parity ib2.education i.famsit_new ib2.MBMI5 gestage_wk if
>> multibirth==1, vce(robust)
>>
>> Linear regression Number of obs = 1144571
>> F( 22,1144548) = 4543.46
>> Prob > F = 0.0000
>> R-squared = 0.0820
>> Root MSE = .94207
>>
>> --------------------------------------------------------------------------------------
>> | Robust
>> bwtgestage_sd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
>> ---------------------+-----------------------------------------------
>> ---------------------+-----------------
>> ethnicity_bi2#smoke2 |
>> 1 3 | -.3777867 .0023854 -158.37 0.000 -.3824621 -.3731114
>> 2 2 | -.1160349 .0063877 -18.17 0.000 -.1285545 -.1035153
>> 2 3 | -.4195231 .0107743 -38.94 0.000 -.4406403 -.3984059
>> 3 2 | -.4354954 .0044767 -97.28 0.000 -.4442696 -.4267213
>> 3 3 | -.577776 .0153539 -37.63 0.000 -.6078691 -.547683
>> |
>> sex | .0127302 .0017618 7.23 0.000 .0092772 .0161832
>> |
>> magecat |
>> 1 | .0813647 .0062532 13.01 0.000 .0691086 .0936207
>> 3 | -.0483047 .0024836 -19.45 0.000 -.0531725 -.0434369
>> 4 | -.0756843 .0028012 -27.02 0.000 -.0811745 -.070194
>> 5 | -.1036756 .0037371 -27.74 0.000 -.1110003 -.096351
>> 6 | -.1385867 .0075963 -18.24 0.000 -.1534752 -.1236982
>> |
>> parity |
>> 2 | .308966 .0020534 150.47 0.000 .3049415 .3129905
>> 3 | .4244317 .0026556 159.83 0.000 .4192269 .4296365
>> |
>> education |
>> 1 | -.044489 .0029772 -14.94 0.000 -.0503241 -.0386539
>> 3 | .0334391 .0024809 13.48 0.000 .0285766 .0383016
>> 4 | .0482377 .0025474 18.94 0.000 .0432449 .0532304
>> |
>> famsit_new |
>> 3 | -.0321022 .0055319 -5.80 0.000 -.0429445 -.0212598
>> 4 | -.023603 .0077085 -3.06 0.002 -.0387113 -.0084947
>> |
>> MBMI5 |
>> 1 | -.2915349 .0039634 -73.56 0.000 -.2993029 -.2837669
>> 3 | .249358 .0023809 104.73 0.000 .2446914 .2540245
>> 4 | .3691747 .0042171 87.54 0.000 .3609092 .3774401
>> |
>> gestage_wktemp | -.0000848 .0005237 -0.16 0.871 -.0011111 .0009416
>> _cons | -.0348456 .0209644 -1.66 0.096 -.0759352 .006244
>>
>>
>> I then use the following :
>>
>> qui foreach x of var magecat {
>> sum `x', d
>> replace `x' = r(p50)
>> }
>> predict p
>> predict se, stdp
>> tw (line p ethnicity_bi2 if smoke2==2, sort) (line p ethnicity_bi2 if
>> smoke2==3, sort)
>>
>>
>> I would like to know if the line above 'qui foreach x of var magecat' actually does indicate all categories of all variables magecat onwards as specified in the model including the continuous variable gestaga_wk?
>>
>> I don't seem to get a graph I was expecting - or at least I can't make sense of it. Have I specified the graph correctly or is there a better way plot interactions from a regression model?
>>
>> Thanks!
>> Amal
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