“How Do I Bring Diversity?” Race and Class in the College Admissions Essay

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90581/1/j.1540-5893.2011.00429.x.pd

On the nonlocal viscosity kernel of mixtures

In this report we investigate the multiscale hydrodynamical response of a liquid as a function of mixture composition. This is done via a series of molecular dynamics simulations where the wave vector dependent viscosity kernel is computed for three mixtures each with 7-15 different compositions. We observe that the nonlocal viscosity kernel is dependent on composition for simple atomic mixtures for all the wave vectors studied here, however, for a model polymer melt mixture the kernel is independent of composition for large wave vectors. The deviation from ideal mixing is also studied. Here it is shown that a Lennard-Jones mixture follows the ideal mixing rule surprisingly well for a large range of wave vectors, whereas for both the Kob-Andersen mixture and the polymer melt large deviations are found. Furthermore, for the polymer melt the deviation is wave vector dependent such that there exists a critical length scale at which the ideal mixing goes from under-estimating to over-estimating the viscosity

Covariate Balance in Simple, Stratified and Clustered Comparative Studies

In randomized experiments, treatment and control groups should be roughly the same--balanced--in their distributions of pretreatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should there be a single, omnibus test? Could such a test be engineered to give easily computed $p$-values that are reliable in samples of moderate size, or would simulation be needed for reliable calibration? What new concerns are introduced by random assignment of clusters? Which tests of balance would be optimal? To address these questions, Fisher's randomization inference is applied to the question of balance. Its application suggests the reversal of published conclusions about two studies, one clinical and the other a field experiment in political participation.Comment: Published in at http://dx.doi.org/10.1214/08-STS254 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder

Omitted variable bias can affect treatment effect estimates obtained from observational data due to the lack of random assignment to treatment groups. Sensitivity analyses adjust these estimates to quantify the impact of potential omitted variables. This paper presents methods of sensitivity analysis to adjust interval estimates of treatment effect---both the point estimate and standard error---obtained using multiple linear regression. Central to our approach is what we term benchmarking, the use of data to establish reference points for speculation about omitted confounders. The method adapts to treatment effects that may differ by subgroup, to scenarios involving omission of multiple variables, and to combinations of covariance adjustment with propensity score stratification. We illustrate it using data from an influential study of health outcomes of patients admitted to critical care.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS315 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies

Clustered treatment assignment occurs when individuals are grouped into clusters prior to treatment and whole clusters, not individuals, are assigned to treatment or control. In randomized trials, clustered assignments may be required because the treatment must be applied to all children in a classroom, or to all patients at a clinic, or to all radio listeners in the same media market. The most common cluster randomized design pairs 2S clusters into S pairs based on similar pretreatment covariates, then picks one cluster in each pair at random for treatment, the other cluster being assigned to control. Typically, group randomization increases sampling variability and so is less efficient, less powerful, than randomization at the individual level, but it may be unavoidable when it is impractical to treat just a few people within each cluster. Related issues arise in nonrandomized, observational studies of treatment effects, but in this case one must examine the sensitivity of conclusions to bias from nonrandom selection of clusters for treatment. Although clustered assignment increases sampling variability in observational studies, as it does in randomized experiments, it also tends to decrease sensitivity to unmeasured biases, and as the number of cluster pairs increases the latter effect overtakes the former, dominating it when allowance is made for nontrivial biases in treatment assignment. Intuitively, a given magnitude of departure from random assignment can do more harm if it acts on individual students than if it is restricted to act on whole classes, because the bias is unable to pick the strongest individual students for treatment, and this is especially true if a serious effort is made to pair clusters that appeared similar prior to treatment. We examine this issue using an asymptotic measure, the design sensitivity, some inequalities that exploit convexity, simulation, and an application concerned with the flooding of villages in Bangladesh

Amphibians in agricultural landscapes: the habitat value of crop areas, linear plantings and remnant woodland patches

Mitigating the negative impacts of agriculture on amphibians requires knowledge of how different land uses affect species distribution and community composition. In the case of frogs, there is currently insufficient information on their use of terrestrial habitats in cropping landscapes to inform conservation planning. We examined how four different farmland types (linear plantings, cereal crops, grazing paddocks and woody mulch) and crop harvesting influenced amphibian abundance, richness, body condition and movement. We found the abundance of frogs was significantly higher in linear plantings compared to grazing paddocks and adjacent patches of remnant woodland vegetation. However, species richness and abundance of three individual species did not vary significantly between farmland types. For the most common frog Uperoleia laevigata, body condition was higher at the edges of the woody debris treatment (coupled with higher abundance) and lower in farmland with debris and linear plantings. The body condition of Limnodynastes tasmaniensis and L. interioris was not influenced by farmland type. Frog abundance and condition was largely unaffected by crop harvesting. However, frogs were less common after harvesting at the edges of farmland and within remnant patches. Movement patterns did not suggest mass movement out of crops after harvest, where almost half of all individuals recaptured remained within the farmland. These results suggest that some generalist frog species may have an affinity for habitats within agricultural paddocks, particularly when key habitat features like plantings are present. However, we found overall frog richness was low and did not differ between remnant patches, edges and farmland which may be an indication of habitat degradation within terrestrial habitats across the landscape. Although protection of remnant native vegetation is important, conservation strategies for the protection of amphibians will be ineffective if they do not consider the variety of land uses and the relationships of different species and their microhabitats within and outside of patches.Funding was provided by the Australian Government Research Training Program, Central Tablelands Local Land Services, NSW Environmental Trust and the Lake Cowal Foundation

An alternative to grids and glasses: Quaquaversal pre-initial conditions for N-body simulations

N-body simulations sample their initial conditions on an initial particle distribution, which for cosmological simulations is usually a glass or grid, whilst a Poisson distribution is used for galaxy models, spherical collapse etc. These pre-initial conditions have inherent correlations, noise due to discreteness and preferential alignments, whilst the glass distribution is poorly defined and computationally expensive to construct. We present a novel particle distribution which can be useful as a pre-initial condition for N-body simulations, using a simple construction based on a ``quaquaversal'' tiling of space. This distribution has little preferred orientation (i.e. is statistically isotropic), has a rapidly vanishing large scale power-spectrum (P(k) ~ k^4), and is trivial to create. It should be particularly useful for warm dark matter and cold collapse simulations.Comment: 8 pages, 6 figures, extended discussion of level of isotropy, matches version accepted in Ap

Ion size effects at ionic exclusion from dielectric interfaces and slit nanopores

A previously developed field-theoretic model [R.D. Coalson et al., J. Chem. Phys. 102, 4584 (1995)] that treats core collisions and Coulomb interactions on the same footing is investigated in order to understand ion size effects on the partition of neutral and charged particles at planar interfaces and the ionic selectivity of slit nanopores. We introduce a variational scheme that can go beyond the mean-field (MF) regime and couple in a consistent way pore modified core interactions, steric effects, electrostatic solvation and image-charge forces, and surface charge induced electrostatic potential. We show that in the dilute limit, the MF and the variational theories agree well with MC simulation results, in contrast to a recent RPA method. The partition of charged Yukawa particles at a neutral dielectric interface (e.g air-water or protein-water interface) is investigated. It is shown that as a result of the competition between core collisions that push the ions towards the surface, and repulsive solvation and image forces that exclude them from the interface, a concentration peak of finite size ions sets in close to the dielectric interface. We also characterize the role played by the ion size on the ionic selectivity of neutral slit nanopores. We show that the complex interplay between electrostatic forces, excluded volume effects induced by core collisions and steric effects leads to an unexpected reversal in the ionic selectivity of the pore with varying pore size: while large pores exhibits a higher conductivity for large ions, narrow pores exclude large ions more efficiently than small ones