Teaching

I enjoy helping my students develop their abilities to think statistically, to understand variability, and to draw sound conclusions from data. The statistical software package R/RStudio plays a prominent role in most of my classes with the students learning a reproducible workflow through creating R Markdown reports.

Fall 2020 Courses:

  • Math 141: Introduction to Probability and Statistics
    • Students can access the RStudio Server here.

Spring 2020 Courses:

  • Math 343: Statistics Practicum
  • Math 241: Data Science
    • Course Slides can be found here

Research

In many of my research projects, I develop new estimators for complex survey data using modern modeling techniques such as penalized splines, elastic net regression, and regression trees. For several years, I have partnered with the Forest Inventory and Analysis Program to estimate forestry quantities by combining complex sample data with remotely sensed data. For 2016 – 2017, I was an ASA/NSF/BLS Research Fellow and explored statistical learning techniques for estimation and imputation at the Bureau of Labor Statistics.

Current Projects:

  • Land cover/land use change estimation in Georgia.
    • Work is part of the grant “Advances in model-assisted and small area estimation strategies for forest inventories.” funded by U.S. Department of Agriculture Forest Service, Rocky Mountain Research Station.
  • Domain estimation and calibration using regression trees.
    • Collaboration with Daniell Toth (BLS).
  • Mentoring undergraduate research in statistics: Reaping the benefits and overcoming the barriers.
    • Collaboration with Vittorio Addona (Macalester College), Joe Nolan (Northern Kentucky University), Dennis Pearl (Pennsylvania State University), and Nathan Tintle (Dordt College).

Publications and Technical Reports:

  • Basil, M. R. K., Huque, S., McConville, K. S., Moisen, G. G., and T. S. Frescino. (In Press) Creating Homogeneous Landfire Vegetation Classes for Forest Inventory Applications in the Interior West. Gen. Tech. Rep. US. Department of Agriculture, Forest Service, Southern Research Station.
  • Rintoul, M. A., Maebius, S, Alvarado, E, Lloyd-Damnjanovic, A., Toyohara, M., McConville, K. S., Moisen, G. G., and T. S. Frescino. (In Press) An Alternative Post-Stratification Scheme toDecrease Variance of Forest Attributes in the Interior West. Gen. Tech. Rep. US. Department of Agriculture, Forest Service, Southern Research Station.
  • McConville, K. S., Moisen, G. G., and T. S. Frescino. (2020) A Tutorial on Model-Assisted Estimation with Application to Forest Inventory. Forests, 11(2), 244, https://doi.org/10.3390/f11020244
  • McConville, K. S. and D. Toth. (2019) Automated Selection of Post-Strata using a Model-Assisted Regression Tree Estimator. Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12356
  • McConville, K. S., Stokes, L., and M., Gray. (2018). Accumulating Evidence of the Impact of Voter ID Laws: Student Engagement in the Political Process. Statistics and Public Policy, https://doi.org/10.1080/2330443X.2017.1407721.
  • McConville, K. S., Breidt, F. J., Lee, T. C. M., and G. Moisen (2017). Model-Assisted Survey Regression Estimation with the Lasso. Journal of Survey Statistics and Methodology 5, 131-158.
  • McConville, K. S. and F. J. Breidt (2013). Survey Design Asymptotics of the Model-Assisted Penalised Spline Regression Estimator. Journal of Nonparametric Statistics 25, 745-763.
  • Ayala, J., Corbin, P., McConville, K., Colonius, F., Kliemann, W., and J. Peters (2006). Morse Decompostion, Attractors, and Chain Recurrence. Proyecciones Journal of Mathematics 25, 79-109.

It is raining stats and dogs.

The Reed Forestry Data Science Lab has been hard at work for a month now! The lab, supported by the US Forest Service Forest Inventory and Analysis Program, Reed College, and Swarthmore College, has 6 projects going this summer. These projects include: Creating interactive web dashboards of important forest estimates using FIESTA Exploring alternative variance estimators for data collected under a spatially systematic sampling design Producing forest inventory teaching materials Improving and expanding pdxTrees, an R data package of Portland park trees Increasing the functionality of mase, an R package of modern survey estimators Determining the utility of the generalized regression estimator for estimating forest attributes in the Interior West I asked each lab member to provide a picture of themselves with a tree and a description.

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I am teaching Math 241: Data Science this spring. As part of the course, the students are writing blog posts which can be found at reed.edu/math/241. The first batch are up and showcase some of the awesome data packages they have made.

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Over the course of 10 weeks, I had the pleasure of working with 6 awesome student researchers. We worked on SEVEN different projects related to data questions poised by the US Forest Service Forest Inventory and Analysis Program (FIA). This work was a joint collaboration between FIA, Reed College and Swarthmore College and therefore it involved 3 FIA folks: Gretchen Moisen, Research Scientist Tracey Frescino, Forester Chris Toney, Forester Four Reedies:

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I had the wonderful (and terrifying) experience of being interviewed by THE Significance magazine editor, Brian Tarran, about my work estimating the impacts of voter ID laws. Significance teamed up with the always amazing Stats + Stories to provide coverage on JSM 2019. You can listen to the interview here.

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