Mapping the spatial pattern of the uncertain data in urban areas: The disadvantaged predict global nonresponse rate in the National Household Survey

Abstract

High levels of survey nonresponse potentially produce unreliable data due to the often indeterminable possibility of such data being subject to nonresponse bias. In this paper, spatial patterns of global nonresponse rate are analyzed in order to identify whether systemic bias exists across urban spaces with regard to survey nonresponse. Forward stepwise regression is used in combination with spatial regression analysis to build models enabling the prediction of global nonresponse rates in the voluntary 2011 National Household Survey based on explanatory employment, housing, income, and other variables within 11 Canadian cities. The modelling process underscores the inequity of global nonresponse rates; places with high unemployment, high rates of rental properties, a higher proportion of Aboriginal residents, and lower educational attainment have lower compliance with the voluntary survey. Such a pattern has the potential to dramatically influence the ability of government, non-governmental organizations, and other service providers to address the needs of residents of such urban areas.