Iowa Survey of Public Attitudes: An Optimal Approach

爱荷华州公众态度调查：最佳方法

• 批准号: 0825588
• 负责人: Kevin Leicht
• 金额: $ 78.71万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0825588&HistoricalAwards=false
• 关键词: Iowa; Survey; Public; Attitudes; Optimal

SES-0825588Kevin T. Leicht, Naresh KumarThe University of IowaThe 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.

SES-0825588 Kevin T.爱荷华大学爱荷华州的社会科学研究中心（SSRC）将于2010年完成一项试点研究，以测试使用空间分散抽样设计收集调查数据的有效性。 该设计将捕捉社会经济条件的最大差异，并将每个调查对象周围的邻里和物理环境（使用其精确位置）置于情境中。参与者将在2010年抽取一个完整的家庭样本，并将抽样地区和受访者与传统的综合社会调查中选定的受访者进行系统比较。2010年，PI将在EPA的一个地区进行个人层面的实地调查，目的是将其整个数据收集方法和结果与这些地区的传统GSS进行比较。虽然GSS提供了前所未有的机会来了解被调查个人的态度和情况，但这些与人们生活和工作的更大的文化，经济，政治和环境背景之间的联系充其量是间接的。这种方法利用了多层次地理数据的快速量化及其与建筑环境，有毒物质暴露以及人类和自然地理的其他特征的信息的广泛整合，这些特征越来越多地用于社会和行为科学以及公共卫生理论。PI将构建一个居住区的空间抽样框架，然后抽取一个可以捕捉人口分布和SES最大变化的样本。他们将利用地理信息系统、遥感和地点分析来建立一个抽样框架，并将全球安全系统的各个答卷者置于具体的背景之中。与传统的抽样设计相比，这种方法具有以下几个优点：（a）捕捉人口规模和社会经济地位的最大空间离散;（B）避免空间自相关;（c）将抽样调查对象联系起来（通过地理编码）到许多其他的上下文信息，包括一个地方的SES特征，土地利用和土地覆盖类型，以及潜在的有毒排放源;（d）确保社会经济地位和族裔群体的空间覆盖和充分代表性;及（e）为访问员提供资料，以便更好地评估受访者与其生活环境的关系。这项研究提出了一种机制，用于进行和整合综合社会调查在收集全国性资料方面的传统优势-通过与美国成年人的空间样本进行面对面访谈，使用地理信息数据管理系统，将GSS受访者定位在地理空间的多个层面，从而获得代表性调查数据。该方法建立在地理数据的快速量化及其与建筑环境，有毒物质暴露以及越来越多地用于理论化社会科学研究的人类和自然地理的其他特征的信息的广泛整合的基础上。2010年的试点调查可以对一个地理区域的最佳空间抽样方法与更传统的抽样方法的结果进行系统比较，并可以在国家一级对两种全面抽样框架进行比较。 更广泛的影响：世界正变得越来越相互关联，将个人情况与更广泛的社会结构联系起来的能力是社会科学事业的核心。 通过全球社会科学系统建立这些联系的能力将为在美国和（最终）在世界各地建立社会科学基础设施提供前所未有的机会，使研究人员和决策者能够获得空间上相互联系的、人口统计上健全的数据集。将大大加强对未来几代社会科学学生和从业人员进行地理信息系统和层次分析方法培训的能力，社会科学和社会政策界将获得前所未有的详细程度的高质量数据。这种最佳空间抽样方法的试点测试是实现国家调查数据收集革命性变革的重要第一步，将最新技术与传统的合理调查研究技术相结合。