Kim is a workshop instructor for The Analysis Factor and owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.
She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.
Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.
While she enjoys working with all clients, Kim particularly enjoys working with those who feel they have a less-than-perfect relationship with statistics.
Her goal is to spread an appreciation of statistics across many fields of study, starting by making it understandable to those who interact with it.
The Craft of Statistical Analysis Webinars
All of these were taught by Kim and access to all recordings are available at no charge.
Software Used: R
Software Used: SPSS, SAS, R, Stata
Kim’s Blog Posts
- Member Training: Interactions in Poisson and Logistic Regression – Part 2
- Six Common Types of Statistical Contrasts
- What is a Randomized Complete Block Design?
- What is the Mann-Whitney U Test?
- The Difference Between the Bernoulli and Binomial Distributions
- What is a Completely Randomized Design?
- The Difference between Chi Square Tests of Independence and Homogeneity
- Three Principles of Experimental Designs
- Member Training: Classic Experimental Designs
- R-Squared for Mixed Effects Models
- Regression Diagnostics in Generalized Linear Mixed Models
- The Difference Between Link Functions and Data Transformations
- Understanding Random Effects in Mixed Models
- What is the Purpose of a Generalized Linear Mixed Model?
- The Advantages of RStudio
- What Really Makes R So Hard to Learn?