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Reusing existing data sets of health information for public health or medical research has much to recommend it. Much data repurposing in medical or public health research or practice involves information that has been stripped of individual identifiers but some does not. In some cases, there may have been consent to the reuse but in other cases consent may be absent and people may be entirely unaware of how the data about them are being used. Data sets are also being combined and may contain information with very different sources, consent histories, and individual identifiers. Much of the ethical and policy discussion about the permissibility of data reuse has centered on two questions: for identifiable data, the scope of the original consent and whether the reuse is permissible in light of that scope, and for de-identified data, whether there are unacceptable risks that the data will be reidentified in a manner that is harmful to any data subjects. Prioritizing these questions rests on a picture of the ethics of data use as primarily about respecting the choices of the data subject. We contend that this picture is mistaken; data repurposing, especially when data sets are combined, raises novel questions about the impacts of research on groups and their implications for individuals regarded as falling within these groups. These impacts suggest that the controversies about de-identification or reconsent for reuse are to some extent beside the point. Serious ethical questions are also raised by the inferences that may be drawn about individuals from the research and resulting risks of stigmatization. These risks may arise even when individuals were not part of the original data set being repurposed. Data reuse, repurposing, and recombination may have damaging effects on others not included within the original data sets. These issues of justice for individuals who might be regarded as indirect subjects of research are not even raised by approaches that consider only the implications for or agreement of the original data subject. This chapter argues that health information should be available for reuse, information should be available for use, but in a way that does not yield unexpected surprises, produce direct harm to individuals, or violate warranted trust.

Single subject research has long been employed to evaluate intervention effectiveness with students with learning or behavioral disabilities. Typically, the results of single subject research are presented on graphic displays and analyzed by a method of visual inspection, in which analysts simultaneously consider such data elements as level change, slope change, and variability in baseline and treatment data. However, over the years several concerns regarding visual inspection have emerged, including relatively low inter-rater reliabilities. This chapter reviews the arguments in favor of visual inspection as an analytic tool, and also summarizes the arguments favoring statistical analysis of single case data. The use of randomization tests is recommended, and an example is provided of its use in research with students with learning and behavioral disorders.

This chapter analyzes the compliance of some category of Open Data in Politics with EU General Data Protection Regulation (GDPR) requirements. After clarifying the legal basis of this framework, with specific attention to the processing procedures that conform to the legitimate interests pursued by the data controller, including open data licenses or anonymization techniques, that can result in partial application of the GDPR, but there is no generic guarantee, and, as a consequence, an appropriate process of analysis and management of risks is required.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

Purpose: This chapter sets out to lay out and analyse the effectiveness of the General Data Protection Regulation (GDPR), a recently established European Union (EU) regulation, in the local insurance industry.

Methodology: This was done through a systematic literature review to determine what has already been done and then a survey as a primary research tool to gather information. The survey was aimed at clients and employees of insurance entities.

Findings: The general results are that effectiveness can be segmented into different factors and vary regarding the respondents’ confidence. Other findings include that the GDPR has increased costs, and its expectations are unclear. These findings suggest that although the GDPR was influential in the insurance market, some issues about this regulation still exist.

Conclusions: GDPR fulfils its purposes; however, the implementation process of this regulation can be facilitated if better guidelines are issued for entities to follow to understand its expectations better and follow the law and fulfil its purposes most efficiently.

Practical implications: These conclusions imply that the GDPR can be improved in the future. Overall, as a regulation, it is suitable for the different member states of the EU, including small states like Malta.

Measuring risk aversion is sensitive to assumptions about the wealth in subjects’ utility functions. Data from the same subjects in low- and high-stake lottery decisions allow estimating the wealth in a pre-specified one-parameter utility function simultaneously with risk aversion. This paper first shows how wealth estimates can be identified assuming constant relative risk aversion (CRRA). Using the data from a recent experiment by Holt and Laury (2002a), it is shown that most subjects’ behavior is consistent with CRRA at some wealth level. However, for realistic wealth levels most subjects’ behavior implies a decreasing relative risk aversion. An alternative explanation is that subjects do not fully integrate their wealth with income from the experiment. Within-subject data do not allow discriminating between the two hypotheses. Using between-subject data, maximum-likelihood estimates of a hybrid utility function indicate that aggregate behavior can be described by expected utility from income rather than expected utility from final wealth and partial relative risk aversion is increasing in the scale of payoffs.

The focus of this chapter is the intersection of social media, publication, data sharing, and research ethics. By now there is an extensive literature on the use of social media in research. There is also excellent work on challenges of postpublication sharing of social media, primarily focused on legal restrictions, technical infrastructure, and documentation. This chapter attempts to build upon and extend this work by using cases to deepen the analysis of ethical issues arising from publishing and sharing social media data. Publishing will refer to the presentation of data extracts, aggregations, or summaries, while sharing refers to the practice of making the underlying data available postpublication for others to use. It will look at the ethical questions that arise both for researchers (or others) sharing data, and those who are using data that has been made available by others, emphasizing the inherently relational nature of data sharing. The ethical challenges researchers face when considering sharing user-generated content collected from social media platforms are the focus of the cases. The chapter begins by summarizing the general principles of research ethics, then identifies the specific ethical challenges from sharing social media data and positions these challenges in the context of these general principles. These challenges are then analyzed in more detail with cases from research projects that drew upon several different genres of social media. The chapter concludes with some recommendations for practical guidance and considers the future of ethical practice in sharing social media data.