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While block randomized designs have become more common in place-based policing studies, there has been relatively little discussion of the assumptions employed and their implications for statistical analysis. Our paper seeks to illustrate these assumptions, and controversy regarding statistical approaches, in the context of one of the first block randomized studies in criminal justice—the Jersey City Drug Market Analysis Project (DMAP).
MethodsUsing DMAP data, we show that there are multiple approaches that can be used in analyzing block randomized designs, and that those approaches will yield differing estimates of statistical significance. We develop outcomes using both models with and without interaction, and utilizing both Type I and Type III sums-of-squares approaches. We also examine the impacts of using randomization inference, an approach for estimating p values not based on approximations using normal distribution theory, to adjust for possible small N biases in estimating standard errors.
ResultsThe assumptions used for identifying the analytic approach produce a comparatively wide range of p values for the main DMAP program impacts on hot spots. Nonetheless, the overall conclusions drawn from our re-analysis remain consistent with the original analyses, albeit with more caution. Results were similar to the original analyses under different specifications supporting the identification of diffusion of benefits effects to nearby areas.
ConclusionsThe major contribution of our article is to clarify statistical modeling in unbalanced block randomized studies. The introduction of blocking adds complexity to the models that are estimated, and care must be taken when including interaction effects in models, whether they are ANOVA models or regression models. Researchers need to recognize this complexity and provide transparent and alternative estimates of study outcomes.
相似文献The aim of this paper is to examine the influence of the right to information laws on sustainability transparency in European local governments. This goal is novel, in that previous studies have examined the effects of various factors on the dissemination of government information (demographic, socioeconomic, political and financial) but not the contribution of legal factors to online transparency on environmental, social and economic sustainability. Our research question is this: Do information laws contribute to transparency on sustainability? Using the Global Reporting Initiative guidelines and a statistical regression analysis, we studied the websites of 106 local governments in ten European countries. The results obtained show that when transparency laws clearly stipulate the rules applicable and the procedures established for appeals, exceptions, refusals and requests, this can favour transparency on environmental, social and economic sustainability. Our findings advance understanding of this field and reinforce the basis for legal reforms to enhance sustainability transparency.
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