We extend Evans and King (Forthcoming, 2021) to nonlinear transformations, using proportions and weighted averages as our running examples.
When word processors were first introduced into the workplace, they turned scholars into typists. But they also improved our work: Turnaround time for new drafts dropped from days to seconds. Rewriting became easier and more common, and our papers, educational efforts, and research output improved. I discuss the advantages of and mechanisms for doing the same with do-it-yourself video recordings of research talks and class lectures, so that they may become a fully respected channel for scholarly output and education, alongside books and articles. I consider innovations in video design to optimize education and communication, along with technology to make this change possible.
Purpose: The COVID-19 death-rate in Italy continues to climb, surpassing that in every other country. We implement one of the first nationally representative surveys about this unprecedented public health crisis and use it to evaluate the Italian government’ public health efforts and citizen responses.
Findings: (1) Public health messaging is being heard. Except for slightly lower compliance among young adults, all subgroups we studied understand how to keep themselves and others safe from the SARS-Cov-2 virus. Remarkably, even those who do not trust the government, or think the government has been untruthful about the crisis believe the messaging and claim to be acting in accordance. (2) The quarantine is beginning to have serious negative effects on the population’s mental health.
Policy Recommendations: Communications focus should move from explaining to citizens that they should stay at home to what they can do there. We need interventions that make staying at home and following public health protocols more desirable. These interventions could include virtual social interactions, such as online social reading activities, classes, exercise routines, etc. — all designed to reduce the boredom of long term social isolation and to increase the attractiveness of following public health recommendations. Interventions like these will grow in importance as the crisis wears on around the world, and staying inside wears on people.
Replication data for this study in dataverse
In this report, I evaluate evidence described and conclusions drawn in several Exhibits in this case offered by the Plaintiffs. I conclude that the evidence is insufficient to support conclusions about election fraud. Throughout, the authors break the chain of evidence repeatedly – from the 2020 election, to the data analyzed, to the quantitative results presented, to the conclusions drawn – and as such cannot be relied on. In addition, the Exhibits make many crucial assumptions without justification, discussion, or even recognition – each of which can lead to substantial bias, and which was unrecognized and uncorrected. The data analytic and statistical procedures used in the Exhibits for data providence, data analysis, replication information, and statistical analysis all violate professional standards and should be disregarded.
The Court's ruling in this case concluded "Not only have Plaintiffs failed to provide the Court with factual support for their extraordinary claims, but they have wholly failed to establish that they have standing for the Court to consider them. Allegations that find favor in the public sphere of gossip and innuendo cannot be a substitute for earnest pleadings and procedure in federal court. They most certainly cannot be the basis for upending Arizona’s 2020 General Election. The Court is left with no alternative but to dismiss this matter in its entirety."
[Thanks to Soubhik Barari for research assistance.]
We provide an overview of PSI ("a Private data Sharing Interface"), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy. (See software here and our OpenDP.org project which builds on this paper.)
Universities require faculty and students planning research involving human subjects to pass formal certification tests and then submit research plans for prior approval. Those who diligently take the tests may better understand certain important legal requirements but, at the same time, are often misled into thinking they can apply these rules to their own work which, in fact, they are not permitted to do. They will also be missing many other legal requirements not mentioned in their training but which govern their behaviors. Finally, the training leaves them likely to completely misunderstand the essentially political situation they find themselves in. The resulting risks to their universities, collaborators, and careers may be catastrophic, in addition to contributing to the more common ordinary frustrations of researchers with the system. To avoid these problems, faculty and students conducting research about and for the public need to understand that they are public figures, to whom different rules apply, ones that political scientists have long studied. University administrators (and faculty in their part-time roles as administrators) need to reorient their perspectives as well. University research compliance bureaucracies have grown, in well-meaning but sometimes unproductive ways that are not required by federal laws or guidelines. We offer advice to faculty and students for how to deal with the system as it exists now, and suggestions for changes in university research compliance bureaucracies, that should benefit faculty, students, staff, university budgets, and our research subjects.
Matching is an increasingly popular method of causal inference in observational data, but following methodological best practices has proven difficult for applied researchers. We address this problem by providing a simple graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable ``Mahalanobis Distance Matching'' (MDM), the commonly used ``Propensity Score Matching'' (PSM), and a newer approach called ``Coarsened Exact Matching'' (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in many real applications and in data simulated by the processes that fit PSM theory. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can often degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach and easy-to-follow procedures, focus can be on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it.
Please see our follow up paper on this topic: Why Propensity Scores Should Not Be Used for Matching.
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