<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Christopher, Ed J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non - response Bias and Trip Generation Models</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bias (Statistics)</style></keyword><keyword><style  face="normal" font="default" size="100%">Travel surveys</style></keyword><keyword><style  face="normal" font="default" size="100%">Trip generation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1993</style></year></dates><publisher><style face="normal" font="default" size="100%">Transportation Research Board</style></publisher><pages><style face="normal" font="default" size="100%">64-70</style></pages><isbn><style face="normal" font="default" size="100%">0309055598</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;There is serious concern over the fact that travel surveys often overrepresent smaller households with higher incomes and better education levels and, in general, that nonresponse is nonrandom. However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, estimates of parameters are unbiased regardless of the nature of the respondents, and the issues of how response rates and nonresponse bias are ameliorated. The more important task then is the complete specification of the model, without leaving out variables that have some effect on the variable to be predicted. The theoretical basis for this reasoning is given along with an example of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but the manner in which these and other more nonstandard methods have been systematically put together to assess bias in estimates shows that careful model building, not concern over bias in the data, becomes the key issue in developing trip generation and other models.&lt;/p&gt;
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