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Transitioning a Survey to Self-Administration using Adaptive, Responsive, and Tailored (ART) Design Principles and Data Visualization

Methodology. Sage Publications Wolf, Joye, Smith and Fu. Thousand Oaks. CA, 255–268. Duprey, M., J. Murphy, P. Biemer, and R. Chew. 2017. “Veni, Vidi, Vici: Interactive Data Visualizations for Adaptive Total Design.” Presented at the 5th Workshop on Adaptive and Responsive Survey Design. Ann Arbor, MI. Eddy, W.F. and Marton, K., Editors. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys . Washington D.C.: The National Academies Press. Edgar, J., J. Murphy, and M. Keating. 2016

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Megatrend and Intervention Impact Analyzer for Jobs: A Visualization Method for Labor Market Intelligence

Abstract

This article presents a visual method for representing the complex labor market internal structure from the perspective of similar occupations based on shared skills; and a prototype tool for interacting with the visualization, together with an extended description of graph construction and the necessary data processing for linking multiple heterogeneous data sources. Since the labor market is not an isolated phenomenon and is constantly impacted by external trends and interventions, the presented method is designed to enable adding extra layers of external information. For instance, what is the impact of a megatrend or an intervention on the labor market? Which parts of the labor market are the most vulnerable to an approaching megatrend or planned intervention? A case study analyzing the labor market together with the megatrend of job automation and computerization is presented. The source code of the prototype is released as open source for repeatability.

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Book Review

References Murrell, P. 2011. R Graphics , 2 nd ed. Boca Raton, FL: Chapman & Hall/CRC Press. Sarkar, D. 2008. Lattice: Multivariate Data Visualization with R . New York: Springer-Verlag. Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis , 2 nd ed. New York: Springer-Verlag.

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Linear Regression Diagnostics in Cluster Samples

References Atkinson, A.C., and M. Riani. 2000. Robust Diagnostic Regression Analysis. New York: Springer-Verlag. Atkinson, A.C., and M. Riani. 2004. “The Forward Search and Data Visualization.” Computational Statistics 19: 29-54. Bates, D., M. Maechler, B. Bolker and S. Walker. 2014. “lme4: Linear Mixed-Effects Models Using Eiqen and S4. R package version 1.1-7.” Available at: http://CRAN.R-project.org/package= lme4 (accessed February 2, 2015). Belsley, D.A., R. E. Kuh, and R. Welsch. 1980. Regression Diagnostics: Identifying Influential Data and

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A Bayesian model to compare vinification procedures

: Springer-Verlag. Ruppert D., Wand M., Carroll R. (2003): Semiparametric Regression. Cambridge: Cambridge University Press. Sacchi K.L., Bisson L.F., Adams D.O. (2005): A review of the effect of winemaking techniques on phenolic extraction in red wines. American Journal of Enology and Viticulture 56(3): 197-206. Sarkar D. (2008): Lattice: multivariate data visualization with R. New York: Springer. ISBN 978-0-387-75968-5. <http://lmdvr.r-forge.r-project.org> Soleas G.J., Tomlinson G., Goldberg D.M. (1998

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