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.

Spring 2019 Courses:

  • Math 343: Statistics Practicum
  • Math 392: Mathematical Statistics

Fall 2018 Courses:

  • Math 141: Introduction to Probability and Statistics
    • Students can access the RStudio Server here.
    • In the course, we use DataCamp.com a browser-based interactive platform for learning data science.


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).


  • McConville, K. S. and C. Schumacher*. (Submitted). Lasso Logistic Regression Estimator.
  • McConville, K. S., and E., Remy*. (Submitted) Are Volcanic Eruptions Increasing?: An Example of Teaching Data Wrangling and Visualization.
  • McConville, K. S. and D. Toth. (Accepted in the Scandinavian Journal of Statistics). Automated Selection of Post-Strata using a Model-Assisted Regression Tree Estimator.
  • 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.