Kevin T. Leicht, Naresh Kumar The University of Iowa
The Social Science Research Center (SSRC) at the University of Iowa will complete a pilot study in 2010 to test the efficacy of using a spatially dispersed sampling design to collect survey data. The design will capture the maximum variance in socio-economic conditions and contextualize the neighborhood and physical environment surrounding each survey respondent (using their precise location). The PIs will draw a full household sample for the 2010 and systematically compare the sampled regions and respondents with those selected in the conventional General Social Survey (GSS). The PIs will field individual-level surveys in one EPA region in 2010 for the purposes of comparing their entire data collection method and results with those from the conventional GSS for these same regions.
While the GSS has provided unprecedented access to the attitudes and circumstances of surveyed individuals, the links between these and the larger cultural, economic, political, and environmental contexts where people live and work have been indirect at best. This approach takes advantage of the rapid quantification of multi-level geographic data and its extensive integration with information on built environments, toxic exposures, and other features of human and natural geography that are increasingly used in theories in social and behavioral science and public health. The PIs will construct a spatial sampling frame of inhabited areas and then draw a sample that can capture the maximum variation in population distribution and SES. They will utilize geographic information systems, remote sensing and locational analysis for constructing a sampling frame and contextualizing individual GSS respondents. This approach offers several advantages over the conventional sampling designs by (a) capturing the maximum spatial dispersion in population size and SES; (b) avoiding spatial autocorrelation; (c) linking sampled respondents (via geocodes) to many additional pieces of contextual information including SES characteristics of a place, land use and land-cover type, and potential sources of toxic emission; (d) ensuring spatial coverage and adequate representation across SES and ethnic groups; and (e) providing interviewers with information that allows for a better assessment of respondents' answers in relation to their lived context.
This study presents a mechanism for conducting and integrating the General Social Survey's traditional strengths in collecting nationally-representative survey data through face-to-face interviews with spatial samples of adults in the United States with a geographic information data management system that will locate GSS respondents in multiple layers of geographic space. The approach builds on the rapid quantification of geographic data and its extensive integration with information on built environments, toxic exposures, and other features of human and natural geography that are increasingly being used to theorize social science research. The pilot survey in 2010 allows for a systematic comparison of the results from an optimal spatial sampling approach with more conventional sampling methods for a geographic region and allow for a comparison of both full-sampling frames at the national level.
Broader Impacts: The world is becoming more interconnected, and the ability to link personal circumstances to broader social structure is central to the social scientific enterprise. The ability to develop these linkages through the GSS will provide an unprecedented opportunity to build social science infrastructure in the United States and (eventually) around the world by allowing researchers and policy makers to have access to spatially linked, demographically and statistically sound data sets. The ability to train future generations of social science students and practitioners in GIS and hierarchical analysis methods will be greatly enhanced, and high quality data will at a previously unheard-of level of detail will be available to the social science and social policy communities. The pilot test of this optimal spatial sampling approach is an important first step toward revolutionizing survey data collection for national surveys combining the latest technologies with conventionally sound survey research techniques.
Human beings are creatures of their environments. We live, work, form families, have children, and form our hopes, dreams, and aspirations in specific places. Yet social science has yet to develop comprehensive ways of understanding how these environments affect us. Policy makers, businesses, and the general public are increasingly interested in how aspects of our built and natural environments affect the quality of our lives. Yet, until recently, it was very difficult to develop a comprehensive understanding of the environments surrounding individual people and how those environments affect them physically and socially. Our project developed a specific and technical method for selecting locations for understanding the relationship between environments and individuals' behaviors, beliefs, motivations, and well-being. The method seeks to make sure that all environments are included in the analyses researchers do while minimizing, as much as possible, the costs of collecting, storing, and using these data. The methodology is also used to protect the confidentiality of research subjects while insuring that the data researchers collect is as accurate as it can possibly be. Our results suggest that the more comprehensive ability to take neighborhoods, communities, and personal interactions into account has the ability to revolutionize how we think of and explain changes in public health, public trust, crime and social disorder, child well-being, and educational outcomes. The ability to monitor and respond to this environmental information will give policy makers and the general public the tools to improve our overall quality of life, enhance our international competitiveness, and improve our security.
