Uniqueness and Metaepistemology

We defend Uniqueness, the claim that given a body of total evidence, there is a uniquely rational doxastic state that it is rational for one to be in. Epistemic rationality doesn't give you any leeway in forming your beliefs. To this end, we bring in two metaepistemological pictures about the roles played by rational evaluations. Rational evaluative terms serve to guide our practices of deference to the opinions of others, and also to help us formulate contingency plans about what to believe in various situations. We argue that Uniqueness vindicates these two roles for rational evaluations, while Permissivism clashes with them

Peer Evaluation of Video Lab Reports in a Blended Introductory Physics Course

The Georgia Tech blended introductory calculus-based mechanics course emphasizes scientific communication as one of its learning goals, and to that end, we gave our students a series of four peer-evaluation assignments intended to develop their abilities to present and evaluate scientific arguments. Within these assignments, we also assessed students' evaluation abilities by comparing their evaluations to a set of expert evaluations. We summarize our development efforts and describe the changes we observed in student evaluation behavior.Comment: 4 pages, 1 table, 2 figures, submitted to Summer 2014 PERC Proceeding

The Initial State of Students Taking an Introductory Physics MOOC

As part of a larger research project into massively open online courses (MOOCs), we have investigated student background, as well as student participation in a physics MOOC with a laboratory component. Students completed a demographic survey and the Force and Motion Conceptual Evaluation at the beginning of the course. While the course is still actively running, we have tracked student participation over the first five weeks of the eleven-week course.Comment: Accepted to PERC Proceedings 201

Application of family-based tests of association for rare variants to pathways

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard gene-based test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (\u3e200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches

Evaluation of the Power and Type 1 Error of Recently Proposed Family-based Tests of Assocations for Rare Variants

Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p \u3c1 × 10−4) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives

Application of Family-based Tests of Association for Rare Variants to Pathways

Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard “gene-based” test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (\u3e200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches

General Approaches for Combining Multiple Rare Variant Associate Tests Provide Improved Power Across a Wider Range of Genetic Architecture

In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by providing a general method that works for combining an arbitrarily large number of any gene‐based rare variant test—a flexibility typically not available in other combined testing methods. We provide a theoretical framework for evaluating our combined test to provide direct insights into the relationship between test‐test correlation, test power and the combined test power relative to individual testing approaches and other combined testing approaches. We demonstrate that our flexible combined testing method can provide improved power and robustness against a wide range of genetic architectures. We further demonstrate the performance of our combined test on simulated genotypes, as well as on a dataset of real genotypes with simulated phenotypes. We support the increased use of flexible combined tests in practice to maximize robustness of rare‐variant testing strategies against a wide‐range of genetic architectures

Tapping Environmental History to Recreate America’s Colonial Hydrology

To properly remediate, improve, or predict how hydrological systems behave, it is vital to establish their histories. However, modern-style records, assembled from instrumental data and remote sensing platforms, hardly exist back more than a few decades. As centuries of data is preferable given multidecadal fluxes of both meteorology/climatology and demographics, building such a history requires resources traditionally considered only useful in the social sciences and humanities. In this Feature, Pastore et al. discuss how they have undertaken the synthesis of historical records and modern techniques to understand the hydrology of the Northeastern U.S. from Colonial times to modern day. Such approaches could aid studies in other regions that may require heavier reliance on qualitative narratives. Further, a better insight as to how historical changes unfolded could provide a “past is prologue” methodology to increase the accuracy of predictive environmental models