3 resultados para Social science research
em Duke University
Resumo:
*Designated as an exemplary master's project for 2015-16*
This paper examines how contemporary literature contributes to the discussion of punitory justice. It uses close analysis of three contemporary novels, Margaret Atwood’s The Heart Goes Last, Hillary Jordan’s When She Woke, and Joyce Carol Oates’s Carthage, to deconstruct different conceptions of punitory justice. This analysis is framed and supported by relevant social science research on the concept of punitivity within criminal justice. Each section examines punitory justice at three levels: macro, where media messages and the predominant social conversation reside; meso, which involves penal policy and judicial process; and micro, which encompasses personal attitudes towards criminal justice. The first two chapters evaluate works by Atwood and Jordan, examining how their dystopian schemas of justice shed light on top-down and bottom-up processes of punitory justice in the real world. The third chapter uses a more realistic novel, Oates’s Carthage, to examine the ontological nature of punitory justice. It explores a variety of factors that give rise to and legitimize punitory justice, both at the personal level and within a broader cultural consensus. This chapter also discusses how both victim and perpetrator can come to stand in as metaphors to both represent and distract from broader social issues. As a whole, analysis of these three novels illuminate how current and common conceptualizations of justice have little to do with the actual act of transgression itself. Instead, justice emerges as a set of specific, conditioned responses to perceived threats, mediated by complex social, cultural, and emotive forces.
Resumo:
Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.
This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.
The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new
individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the
refreshment sample itself. As we illustrate, nonignorable unit nonresponse
can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse
in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.
The second method incorporates informative prior beliefs about
marginal probabilities into Bayesian latent class models for categorical data.
The basic idea is to append synthetic observations to the original data such that
(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.
We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.
The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.
Resumo:
Bycatch reduction technology (BRT) modifies fishing gear to increase selectivity and avoid capture of non-target species, or to facilitate their non-lethal release. As a solution to fisheries-related mortality of non-target species, BRT is an attractive option; effectively implemented, BRT presents a technical 'fix' that can reduce pressure for politically contentious and economically detrimental interventions, such as fisheries closures. While a number of factors might contribute to effective implementation, our review of BRT literature finds that research has focused on technical design and experimental performance of individual technologies. In contrast, and with a few notable exceptions, research on the human and institutional context of BRT, and more specifically on how fishers respond to BRT, is limited. This is not to say that fisher attitudes are ignored or overlooked, but that incentives for fisher uptake of BRT are usually assumed rather than assessed or demonstrated. Three assumptions about fisher incentives dominate: (1) economic incentives will generate acceptance of BRT; (2) enforcement will generate compliance with BRT; and (3) 'participation' by fishers will increase acceptance and compliance, and overall support for BRT. In this paper, we explore evidence for and against these assumptions and situate our analysis in the wider social science literature on fisheries. Our goal is to highlight the need and suggest focal areas for further research. © Inter-Research 2008.
