Comment: The Essential Role of Pair Matching

Comment on "The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation" [arXiv:0910.3752]Comment: Published in at http://dx.doi.org/10.1214/09-STS274A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Why we (usually) don't have to worry about multiple comparisons

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal Of Research On Educational Effectiveness on 04/03/2012, available online: https://doi.org/10.1080/19345747.2011.618213Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern

Why we (usually) don't have to worry about multiple comparisons

Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the $p$-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern

Reducing Bias in Treatment Effect Estimation in Observational Studies Suffering from Missing Data

Matching based on estimated propensity scores (that is, the estimated conditional probability of being treated) has become an increasingly popular technique for causal inference over the past decade. By balancing observed covariates, propensity score methods reduce the risk of confounding causal processes. Estimation of propensity scores in the complete data case is generally straightforward since it uses standard methods (e.g. logistic regression or discriminant analysis) and relies on diagnostics that are relatively easy to calculate and interpret. Most studies, however, have missing data. This paper illustrates a principled approach to handling missing data when estimating propensity scores makes use of multiple imputation (MI). Placing the problem within the framework of the Rubin Causal Model makes the assumptions explicit by illustrating the interaction between the treatment assignment mechanism and the missing data mechanism. Several approaches for estimating propensity scores with incomplete data using MI are presented. Results demonstrating improved efficacy compared with existing methodology are discussed. These advantages include greater bias reduction and increased facility in model choice

Los Angeles During the Civil War

No abstract provide

Nihilism and Dystopian Morality in the Marquis de Sade’s \u3ci\u3eJustine\u3c/i\u3e

What does not conform draws attention. For this reason the work of the Marquis de Sade has long held the world’s attention by excluding any sense of being bound by convention. His characters follow no religion, no moral code, and no law but their own pleasure—especially when it involves violence. The characters of Sade’s twisted literary world, and Sade himself as a historical figure, seem to have a blatant disregard for all propriety, whether seen through its original 18th century context or that of today. This contempt for the conventional, or indeed everything, has led certain scholars to suggest that Sade’s writing is openly nihilist. Though biographical evidence on the author might support this conclusion, Sade’s novel Justine, however, provides repeated instances where the characters make bold universal statements about the human heart—the kind of statements typical of a moralist writer

My Failed Fake News Experiment

This paper investigates fake news and the part we all play in believing and spreading it. To explore the pervasiveness of fake news, I engaged an electronic literary method, netprov, to create a fake news production with its own website and social media profiles. Even though my creative work failed to find an audience, I nevertheless gained insights that led to practical advice on how anyone can avoid succumbing to the influence of fake news. For example, when viewing a potential fake news site, readers should ask if its content is simply confirming their beliefs or even encouraging them to interact (like, comment, share) in a way that bypasses their logical reasoning. My failed experiment shines a light on the festering issue that is fake news and calls upon everyone to critique and analyze this new media phenomenon

Midwest Elementary School Principals and the Use of Social Media

The purpose of this mixed-methods study was to examine the use of social media among elementary school principals in Minnesota. It was part of a dissertation study conducted at Bethel University (Hill, 2016). The survey collected data from elementary school principals across the state of Minnesota gathering demographic information on age, years of service, presence of a district social media policy, gender, school location, school size, and school poverty level as defined by its free and reduced lunch rate. As a result of the study, it was found that a negative relationship exists between the number of years of service from an elementary school principal and their use of social media. Age, gender, school location, school size, and poverty level have no relationship with a principal’s use of social media tools. A qualitative analysis was run on one open-ended question on the survey to determine its themes. Facebook was found to be the most popular tool for sharing information with friends and family while Twitter was cited as the most popular social media tool for professional development