Do low survey response rates bias results? Evidence from Japan

BACKGROUND In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. OBJECTIVE The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. METHODS Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. RESULTS Evidence of response-rate bias is found for the univariate distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. CONCLUSIONS Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple univariate distribution than when examining the relationship between variables in a multivariate model. COMMENTS The results have two implications. First, demographers should not presume the presence or absence of low response-rate biasrather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible

VRSC 2021 Conference Proceedings

The biennial conference aims to catalyze ideas and innovation between academia, practice, NGOs and government agencies who work to address analysis, planning, valuation, design and management of visual resources. The aim of the 2021 Virtual Conference is to share ideas and discuss the issues associated with the assessment and protection of visual resources in an era of major landscape change - regionally, national and globally

Arts & Economic Prosperity 6: The Economic & Social Impact Study of Nonprofit Arts & Culture Organizations & Their Audiences

The newly released Arts & Economic Prosperity 6 (AEP6) is an economic and social impact study of the nation's nonprofit arts and culture industry. Building on its 30-year legacy as the largest and most inclusive study of its kind, AEP6 provides detailed findings on 373 regions from across all 50 states and Puerto Rico—ranging in population from 4,000 to 4 million—and representing rural, suburban, and large urban communities.With its largest cohort ever, AEP6 uses a rigorous methodology to document the economic contributions of the arts and culture industry, demonstrating locally as well as nationally, that arts and culture is a critical economic driver of vibrant communities. The arts and culture industry supports jobs, generates government tax revenue, strengthens the visitor economy and community vibrancy, and helps to preserve authentic cultural experiences.For the first time, AEP6 expands beyond the economic and financial data to learn about the nonprofit arts and culture sector's social impact on the overall well-being of communities and the importance of affirming spaces in BIPOC (Black, Indigenous, People of Color) and ALAANA (African, Latine, Asian, Arab, Native American) identifying communities.By measuring the industry's wide-ranging impact, public and private sector leaders can work together to secure arts and culture funding and arts-friendly policies that shape more vibrant and equitable communities

Ambient Intelligence with Wireless Grid Enabled Applications: A Case Study of the Launch and First Use Experience of WeJay Social Radio in Education

Wireless grid and ambient intelligent (AmI) environments are characterized as supportive of collaboration, interaction, and sharing. The conceptual framework advanced for this study incorporated the constructs of innovation, creativity and context awareness while offering emergence theory -- emergent properties, structures, patterns and behaviors -- to frame and investigate a wireless grid enabled social radio application which was theorized to be potentially transformative and disruptive. The unintended consequences and unexpected possibilities of wireless grid and smart environments were also addressed. Using a single case study, drawing upon multiple data collection methods, this research investigated the deployment and use experience of WeJay, an application incubated through the Wireless Grids Innovation Testbed (WiGiT), from the perspective of beta trial participants. Guided by the broad research question -- Do wireless grid enabled applications, such as WeJay social radio, add to the potential for new and transformative outcomes for people, information and technology when deployed in an academic setting? -- this empirical study sought to: a) learn more about the launch experience of this first pre-standards wireless grid enabled application among WiGiT members and selected Syracuse University students and faculty; b) understand how this application was interpreted for use; c) determine whether novel and unexpected uses emerged; d) investigate whether wireless grid enabled environments fostered innovation and creativity; and e) elicit whether a conceptual relationship was emerging between wireless grid and AmI environments, focusing on context-awareness and ambient learning. While this early stage of diffusion and first user sample was a key limitation of the study it was also the core strength. Although challenged by the state of readiness of WeJay, study findings supported the propositions that WeJay fosters innovation and creativity; that novel and unexpected uses were generated; and that the theorized relationship between wireless grid applications and embedded awareness does exist. Recommendations for enhanced tool readiness were made and embedded smartness was found to be both desirable and beneficial. This research makes a contribution as a bridge study for future research while having theoretical and methodological implications for research and practice. Social, emotion/affect, and human-centered computing (HCC) dimensions emerged as rich areas for further research

How Environmental Change Will Impact Mosquito-Borne Diseases

Mosquitos, the most lethal species throughout human history, are the most prevalent source of vector-borne diseases and therefore a major global health burden. Mosquito-borne disease incidence is expected to shift with environmental change. These changes can be predicted using species distribution models. With the wide variety of methods used for models, consensus for improving accuracy and comparability is needed. A comparative analysis of three recent modeling approaches revealed that integrating modeling techniques compensates for trade-offs associated with a singular approach. An area that represents a critical gap in our ability to predict mosquito behavior in response to changing climate factors, such as temperature, is evolutionary adaptive potential. Evolutionary studies for mosquitos have documented rapid evolutionary change in photoperiodic traits. Further research on evolutionary adaptive potential for mosquito thermal tolerances using longitudinal studies in conjunction with genomic approaches will allow for more realistic parameterization of mosquito biological processes. One of the primary factors driving disease patterns is urbanization. Urban areas are already highly impacted by climate-related health issues and offer a wide variety of potential aquatic habitats for breeding, thereby presenting vulnerable targets for mosquito populations. Mosquito-borne diseases have been historically underrepresented in urban health planning, and with projected increases in habitat suitability for temperate areas such as the U.S., promoting awareness of this issue constitutes a major health priority for the future. Integrating mosquito control policies into urban planning and design, such as concomitant strategies for elimination in green space development, will be highly beneficial in mitigating adverse health outcomes

SPATIAL ANALYSIS OF WHITE-TAILED DEER CAPTURE METHODS AND MOVEMENTS IN SUBURBAN MARYLAND

Regions of the United States continue to experience an increase in zoonotic diseases. White-tailed deer support tick populations and implicated the emergence of several tick-borne diseases. Urbanization has elicited a dramatic increase in white-tailed deer populations. Consequently, the rise in deer numbers close to suburban areas has placed the public at increased risk of contracting disease. This study is part of an USDA-supported tick control project in Howard County, Maryland. The objectives were to 1) evaluate capture methods and provide recommendations for suburban trapping programs; and 2) evaluate spatial and temporal movement patterns and resulting impacts on risk of exposure to ticks. We found trapping deer in urbanized parks, during cold and snowy weather likely increased success. Different patterns in movement and space use of residential land can have important implications for humans’ risk of exposure to disease, with females deer posing higher risk than males especially during winter months

Recognizing deviations from normalcy for brain tumor segmentation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 180-189).A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with the presence of pathology. The resulting fitness map may then be used by conventional image segmentation techniques for honing in on boundary delineation. Such an approach is applicable to structures that are too irregular, in both shape and texture, to permit construction of comprehensive training sets. We develop the method of diagonalized nearest neighbor pattern recognition, and we use it to demonstrate that recognizing deviations from normalcy requires a rich understanding of context. Therefore, we propose a framework for a Contextual Dependency Network (CDN) that incorporates context at multiple levels: voxel intensities, neighborhood coherence, intra-structure properties, inter-structure relationships, and user input. Information flows bi-directionally between the layers via multi-level Markov random fields or iterated Bayesian classification. A simple instantiation of the framework has been implemented to perform preliminary experiments on synthetic and MRI data.by David Thomas Gering.Ph.D