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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: continuous interactions |

Date |
Tue, 5 Oct 2010 08:21:34 +0100 (BST) |

--- On Mon, 4/10/10, Erum Ikramullah wrote: > Does anyone have experience running a continuous X > continuous interaction in a multinomial regression model? > I need some help interpreting the findings. Consider the example below: *----------------- begin example ------------------ sysuse auto, clear // create an indicator variable that is // 1 when an observation has valid values // on mpg, price, and rep78, and 0 otherwise gen byte touse = !missing(mpg, price, rep78) // center mpg sum mpg if touse, meanonly gen c_mpg = mpg - r(mean) // center price and change unit to 1000s of $ sum price if touse, meanonly gen c_price = (price - r(mean))/1000 // see the example FAQ recode rep78 1/2 = 3 gen byte baseline = 1 // add value labels to rep78 label define rep78 3 "Average" /// 4 "Good" /// 5 "Excellent" label value rep78 rep78 // the model mlogit rep78 c.c_mpg##c.c_price foreign baseline, rrr nocons *-------------------- end example -------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) When it comes to interactions it is always a good idea to make sure that the value 0 of each of your variables have a substantive meaning that could meaningfully occur in the data. In this case I did that by mean centering my variables. One way of interpreting -mlogit- model is to look at the odds ratios. This has certain advantages when it comes to interpreting interaction terms, as I discussed in Buis (2010). Assume that we are interested in the effect of mpg and how it changes when the price changes. I find it easiest to start with the baseline odds. The reference are average cars. In the Good equation we see that for domestic cars of average mpg and price we expect to find 0.36 good cars for every average car. For an average priced car this odds increases with a factor 1.097 (i.e. 9.7%) for every mile per gallon increase in mpg. This effect of mpg increases with a factor 1.058 (i.e. 5.8%) for every 1000$ increase in price. In the Excellent equation we see that for domestic cars with average price and mpg we expect to find 0.03 Excellent cars for every average car. For an averaged priced care this odds increases with a factor 1.22 (i.e. 22%) for every mile per gallon increase in mpg, and this effect decreases with a factor 0.97 (i.e. -3%) for every 1000$ increase in price. Hope this helps, Maarten M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in non-linear models", The Stata Journal, 10(2), pp. 305-308. -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**References**:**st: continuous interactions***From:*Erum Ikramullah <eikramullah@childtrends.org>

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