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From |
Antonio Silva <antonio.silva.09@ucl.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: Interpreting margins results of a non-significant interaction |

Date |
Fri, 13 Jul 2012 13:15:38 +0100 |

I'm using the margins command to understand the effect of an interaction between two continous variables (perc_catholics and income_score) on the binary response using logistic regression. When running the logistic regression with other co-variates, both the interaction term and one of the variables of this term (income_score) are not significant, however when running the margins command I obtain a significant relationship for the majority of values of income_score. I'm trying to understand how if the overal interaction is not significant, there is nevertheless a significant interaction when looking at most of the values of income_score. I would appreciate if someone has ideas how this may happen. Output for the the regression and the margins is below //logistic regression with interaction term and co-variates logit return c.perc_catholics##c.income_score perc_catholics_3km crime_disorder_score wall_path_distance postboxes if catholic==1 ------------------------------------------------------------------------------ return | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- perc_catho~s | .0239559 .0097542 2.46 0.014 .0048381 .0430738 income_score | -.1066493 1.490582 -0.07 0.943 -3.028137 2.814838 | c. | perc_catho~s#| c. | income_score | -.0131006 .0176606 -0.74 0.458 -.0477146 .0215135 | perc_cat~3km | -.0130098 .0076235 -1.71 0.088 -.0279516 .0019321 crime_diso~e | -.0188915 .0114732 -1.65 0.100 -.0413786 .0035957 wall_path~ce | .5054887 .2408849 2.10 0.036 .033363 .9776144 postboxes | .3747876 .1275695 2.94 0.003 .1247561 .6248192 _cons | -.9355062 .749677 -1.25 0.212 -2.404846 .5338338 ------------------------------------------------------------------------------ //margins command allowing income_score to vary and keeping co-variates at their means. margins, dydx(perc_catholics) at (income_score=(0(0.1)1) perc_catholics_3km=(42.57777) crime_disorder_score=(33.82767) wall_path_distance=(1.041 > 307) postboxes=(2.966667 )) vsquish post Expression : Pr(return), predict() dy/dx w.r.t. : perc_catholics 1._at : income_score = 0 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 2._at : income_score = .1 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 3._at : income_score = .2 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 4._at : income_score = .3 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 5._at : income_score = .4 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 6._at : income_score = .5 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 7._at : income_score = .6 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 8._at : income_score = .7 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 9._at : income_score = .8 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 10._at : income_score = .9 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 11._at : income_score = 1 perc_cat~3km = 42.57777 crime_diso~e = 33.82767 wall_path~ce = 1.041307 postboxes = 2.966667 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- perc_catho~s | _at | 1 | .0046361 .0014882 3.12 0.002 .0017192 .0075529 2 | .0045107 .0012751 3.54 0.000 .0020115 .0070098 3 | .0043706 .0010694 4.09 0.000 .0022746 .0064666 4 | .0042144 .0008866 4.75 0.000 .0024767 .0059521 5 | .0040407 .0007597 5.32 0.000 .0025516 .0055297 6 | .0038483 .0007406 5.20 0.000 .0023968 .0052999 7 | .0036367 .0008592 4.23 0.000 .0019526 .0053207 8 | .0034054 .0010909 3.12 0.002 .0012672 .0055436 9 | .0031548 .0013964 2.26 0.024 .0004179 .0058917 10 | .0028859 .0017492 1.65 0.099 -.0005425 .0063142 11 | .0026002 .0021333 1.22 0.223 -.0015811 .0067815 ------------------------------------------------------------------------------ Antonio Silva -- Human Evolutionary Ecology Group Department of Anthropology University College London 14 Taviton Street London WC1H OBW * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Interpreting margins results of a non-significant interaction***From:*Christoph Engel <engel@coll.mpg.de>

**Re: st: Interpreting margins results of a non-significant interaction***From:*David Hoaglin <dchoaglin@gmail.com>

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