Suchergebnisse

Cover -- Half Title -- Dedication -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgements -- 1 Theological Trouble -- 2 Gay is Good -- 3 Exodus -- 4 Erotic Theology -- 5 AIDS and the Failure of Gay and Lesbian Theology -- 6 From Here to Queer -- 7 Queer Theology -- 8 Christianity is a Queer Thing -- Bibliography -- Index

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods–or developing methods related to matching–do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

AbstractAlthough randomized experiments are lauded for their high internal validity, they have been criticized for the limited external validity of their results. This chapter describes research strategies for investigating how much nonrepresentative site selection may limit external validity and bias impact findings. The magnitude of external validity bias is potentially much larger than what is thought of as an acceptable level of internal validity bias. The chapter argues that external validity bias should always be investigated by the best available means and addressed directly when presenting evaluation results. These observations flag the importance of making external validity a priority in evaluation planning.

Randomized trials play an important role in estimating the effect of a policy or social work program in a given population. While most trial designs benefit from strong internal validity, they often lack external validity, or generalizability, to the target population of interest. In other words, one can obtain an unbiased estimate of the study sample average treatment effect from a randomized trial; however, this estimate may not equal the target population average treatment effect if the study sample is not fully representative of the target population. This article provides an overview of existing strategies to assess and improve upon the generalizability of randomized trials, both through statistical methods and study design, as well as recommendations on how to implement these ideas in social work research.

Although experiments are viewed as the gold standard for evaluation, some of their benefits may be lost when, as is common, outcomes are not defined for some sample members. In evaluations of marriage interventions, for example, a key outcome—relationship quality—is undefined when a couple splits up. This article shows how treatment-control differences in mean outcomes can be misleading when outcomes are not defined for everyone and discusses ways to identify the seriousness of the problem. Potential solutions to the problem are described, including approaches that rely on simple treatment-control differences-in-means as well as more complex modeling approaches.

To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders, and numerous studies aim to estimate their effects. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements and variation in timing to estimate policy effects, including in the COVID-19 context. While these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions. "Target trial emulation" emphasizes the need to carefully design a non-experimental study in terms of inclusion and exclusion criteria, covariates, exposure definition, and outcome measurement — and the timing of those variables. We argue that policy evaluations using group-level longitudinal ("panel") data need to take a similar careful approach to study design, which we refer to as "policy trial emulation." This is especially important when intervention timing varies across jurisdictions; the main idea is to construct target trials separately for each "treatment cohort" (states that implement the policy at the same time) and then aggregate. We present a stylized analysis of the impact of state-level stay-at-home orders on total coronavirus cases. We argue that estimates from panel methods — with the right data and careful modeling and diagnostics — can help add to our understanding of many policies, though doing so is often challenging.

This book traces the experience of digital economic transformation in seven developing countries, providing insights for policymakers and practitioners in similar situations as well as lessons for outsiders trying to support government reform efforts more broadly. In one country, the prime minister pushes for the liberalization of digital finance as a central pillar of the country's national strategy, while the central bank almost makes it a criminal offence. In another, the digital minister tries to scupper the very process to support digital transformation that the president has asked them to co-lead. This book gives a ringside seat on seven developing countries' tumultuous early steps on the path to a reform of the economy and the government using technology. Written by a group of academics and practitioners from Oxford at the heart of the process, but foregrounding the voices of the policymakers and participants, this book documents and critically assesses efforts to assist a set of governments to kick-start digital transformation. In doing so, it offers lessons for policymakers in other countries. But beyond that, it is an exposition of the process of policymaking more generally in the 2020s, and offers a broader insight as to how outsiders can play a sensible role in other reform processes in developing and emerging countries.