Country Roads, Take Me Away: Coal Mining and Migration in West Virginia, 1971-2010

West Virginia’s population peaked in 1950. Parallel to this, employment in the coal mining industry peaked in 1948. Popular discourse links these two trends together. It is suggested that the decline of the coal mining industry, which was previously a stable source of employment, has led West Virginians to leave the state searching for better job opportunities elsewhere. This thesis uses first-difference regression models to analyze the relationship between lagged year-to-year changes in coal-mining employment and year-to-year changes in net-migration to and from West Virginia. A positive and statistically significant relationship is found between 1-year lagged changes in coal mining employment and changes in net-migration. Specifically, the average effect is that as coal employment increases (decreases) by 100 people in year t-1, then net-migration increases (decreases) by 59 people in year t. The relationship remains when using coal mining employment as a percentage of total employment in West Virginia. The average effect is that as coal mining employment as a percentage of total employment increases (decreases) by one percentage point in year t-1, there is an increase (decrease) in net-migration of approximately 4,438 people in year t. Evidence is also presented that net-migration is positive if per capita personal income in West Virginia grows faster than that of neighboring states. The implications of these results for understanding the decline of coal mining and population decline are discussed

Commencement 5/20/1973

1973 Macalester College Commencement, on 5/20/1973. Distinguished Citizen alumni citations presented by Nancy Slaughter, honorary degrees awarded by Chairman of the Board of Trustees John Bristol, and commencement address by Senator Walter Mondale. Reel #231, run time 43:4

Bayesian Latent Variable Methods for Longitudinal Processes with Applications to Fetal Growth

We consider methods for joint models of exposure and response in epidemiologic studies. In particular, we show how latent variable methods provide a structure for obtaining inference about multistate growth processes and multiple longitudinal and cross-sectional outcomes. Each model utilizes underlying, subject-specific latent variables to account for the correlation that arises from taking multiple observations on the same sampling unit. We also consider latent variable mixture models in order to more flexibly model the latent variable distributions and identify latent classes of subjects who are of particular scientific importance. We apply our methods to applications in reproductive health, obtaining interesting new insights while developing and applying statistical methodology. We first consider the problem of estimating a multistate growth process with unknown initiation time to determine individual early fetal growth. Using cross-sectional data, we identify fetuses that have a latent tendency to grow relatively quickly and slowly and show that slow growth early in pregnancy is associated with an increased risk of future pregnancy loss. These results are important to researchers who use early ultrasounds to date pregnancies under the assumption that there is no measurable variability in early fetal growth. Paper two is concerned with jointly modeling the unusual, asymmetric distributions of birth weight and gestational age. Using latent variable mixture models, we identify a latent class of subjects who are more likely to deliver early and have low weight. We also allow observed covariates to be associated with latent class membership. Our approach provides researchers a new method for examining low birth weight and pre-term birth. In paper three, we aggregate multiple ultrasound measurements on fetal size and blood restriction using latent variables that follow mixture distributions to identify a latent class of subjects who are growth restricted during pregnancy. We then consider a joint model that examines the associations between covariates, early growth restriction, and outcomes measured at birth. Our methods are able to identify a latent class of subjects who have increased blood flow restriction and below average intrauterine size during the second trimester who are more likely to be growth restricted at birth

The Readability of BSCS Textbooks

Since they appeared, the BSCS materials have been widely acclaimed by science educators, and justly so. They represent a contribution toward the movement away from the traditional teaching patterns and toward the modern approach to teaching of science, that is as inquiry

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

Evaluation of ARCAM Deposited Ti-6Al-4V

A wide range of Metal Additive Manufacturing (MAM) technologies are becoming available. One of the challenges in using new technologies for aerospace systems is demonstrating that the process and system has the ability to manufacture components that meet the high quality requirements on a statistically significant basis. The widest-used system for small to medium sized components is the ARCAM system manufactured in Gothenburg, Sweden. This system features a 4kW electron-beam gun, and has a chamber volume of 250mm long x 250mm wide x 250mm to 400mm tall. This paper will describe the basis for the quality and consistency requirements, the experimental and evaluation procedures used for the evaluation, and an analysis of the results for Ti-6Al-4V

Shared Mental Models, Familiarity, and Coordination: A Multi-Method Study of Distributed Software Teams

Coordination is important in large-scale software development because of the many people involved and the complex dependencies present in software tasks. Even small improvements in productivity can lead to substantial cost savings and competitive advantage. But despite great technological advances in software engineering and collaboration tools in recent years, coordination in software development projects continues to be problematic. Traditional theories suggest that team members coordinate by programming their tasks and by communicating with each other, but more recent research also suggests that they coordinate through work familiarity, and team cognition mechanisms like shared mental models. This paper reports on the results of a multi-method research investigation of how shared mental models, work familiarity and geographic dispersion affect coordination in software teams. This research is based on three studies conducted at a large telecommunications company: face-to-face interviews, survey, and archival studies. Results show that shared mental models have a positive effect on team coordination and that prior familiarity with the same software parts and projects reduces software development time. Results also indicate that geographic dispersion increases software development time and that the effect of work familiarity is stronger for geographically distributed teams than for colocated teams

Shared Mental Models and Coordination in Large-Scale, Distributed Software Development

Despite substantial improvements in the last few years in software engineering and collaboration tools, coordination in large-scale software development continues to be problematic. This coordination is important because of the complex interdependencies that exist among software tasks, in that small productivity improvements can lead to substantial cost-savings and competitiveness. Traditional theories suggest that collaborators coordinate by organizing tasks and communicating, but recent research suggests that they also coordinate via implicit mechanisms like shared mental models. However, most of the shared mental model research literature focuses on real-time tasks, and there is very little empirical evidence on how these models affect coordination in more asynchronous and geographically distributed collaboration. Furthermore, none of this evidence is based on large-scale software development organizations. The present research is a field study at a large telecommunications company. It employs qualitative, quantitative, and survey research methods to investigate the effect of shared mental models on coordination in large-scale software development, and to better understand how geographic distance affects coordination