I am an Associate Professor in the Department of Educational, School, and Counseling Psychology (emphasis: Statistics, Measurement, & Evaluation in Education) Program in the College of Education and Human Development at the University of Missouri-Columbia. I am an applied quantitative methodologist and currently teach courses related to program (impact) evaluation, multilevel modeling, regression, and data management. My research interests focus on the use of applied quantitative methods for policy analysis (e.g., bullying, school violence, literacy growth), school climate, large scale data analysis, and the development and validation of empirically supported measures and scales. My methodological interest focus on the analysis of clustered data (or dealing with nonindependent data structures) and the design and analysis of experiments. Click here for a short one-pager.
PhD in Research, Statistics, and Evaluation, 2009
University of Virginia
MA in Instructional Technology and Media, 1997
Teachers College, Columbia University
See CV for complete list
Various ongoing grants funded by the National Institute of Justice, Department of Education (i3), and Institute of Education Sciences.
The Missouri Prevention Science Institute (MPSI) brings community members and researchers together to help schools and families apply techniques that promote social and academic success. Through community outreach, the institute’s staff provides parent training and teacher consultation services.
Our team of faculty and graduate students conducts research on effective methods and policies for youth violence prevention and school safety.
I teach the following courses at the University of Missouri:
The goal of the course is to provide students with the necessary skills needed to review/critique, analyze, interpret, and write-up studies involving nested (clustered) data using multilevel modeling (MLM). Clustered data (e.g., students within schools, patients within clinics) occur quite naturally in the social sciences and being able to understand and conduct their own analyses using nested data is an important skill. Alternatives are discussed as well.
This course is designed to provide students with both a theoretical and applied understanding of useful multivariate statistical procedures (e.g., factor analysis, principal components analysis, discriminant function analysis, cluster analysis, MANOVA) in education sciences.
Evaluating the quantifiable impact of social programs is a key task that policy makers, governments, and program funders perform. In education and the social sciences, a fundamental question asked is ‘How do we know our policy or program works?’
This course is designed to provide students the fundamental and necessary quantitative methods in educational research.
Good data management is a prerequisite for successful research, needed for reproducibility of results, and essential when collaborating with others.