Spring Camp courses are highly relevant to current trends and practices in modern statistical analysis. Most courses offer practical “hands on” instruction to get students up to speed fast and maximize knowledge takeaway. All Stats Camp instructors are vetted and selected from the very best in the field. Most are recognized innovators in advanced quantitative methods. Learn More About Our Instructors
Stats Camp is an internationally recognized award winning educational event focused on advanced statistical training at the graduate and post-graduate level. Each year Weeklong Stats Camps and 3 day Mini Camps are held at various locations throughout the world.
Stats Camp started in 2003 to meet the critical need for on-going education in advanced statistical procedures that researchers, governments, institutions, and corporations employ. The demand for training remains substantial resulting in new courses and additional locations each year.
As a result of its educational value and world-class instructors, Stats Camp has the highest rate of returning students of any statistics training program in the nation (recidivism can be a good thing…).
Stats Camps and Mini Camps take place in some of the most beautiful, exciting and interesting locals in the world. Stats Campers get world class training along with a world class experience. Campers make friends and professional connections while sharing in the many fun activities that are part of every Stats Camp experience.
THURSDAY: 9 – 5, FRIDAY: 9 – 5 and SATURDAY: 9 – 5
March 9th – 11th
An introduction to cross-sectional “person-centered” data analysis. Topics include traditional latent class analysis (i.e., LCA with categorical indicators), multiple group LCA, measurement invariance testing, and modern stepwise methods for modeling predictors and outcomes of latent class membership. Hands-on practice with Mplus is provided.
Katherine Masyn Ph.D. – Associate Professor of Biostatistics at Georgia State University’s School of Public Health.
An introduction to multilevel modeling analysis. Topics include fixed and random effects, centering predictors, cross-level interactions, contextual effects, growth curve modeling, binary outcomes, and power and sample size. Hands-on practice with HLM software is provided.
Audrey Leroux Ph.D. – Assistant Professor of Research, Measurement, and Statistics in the Department of Educational Policy Studies at Georgia State University’s College of Education and Human Development.
This workshop will cover the theory and implementation of propensity score methods for removing selection bias in analyses to estimate the effects of programs or conditions with non-experimental data.
Walter L. Leite Ph.D. – Associate Professor in the Research and Evaluation Methodology of College of Education at the University of Florida.
This is an intensive short course on the principles of structural equation modeling. Topics include confirmatory factor analysis, multiple-group comparisons, factorial invariance as well as extended applications such as hierarchical models & multi-level SEM.
Todd D. Little Ph.D. – Professor of Educational Psychology and Leadership at Texas Tech University. Little is also the director of the Institute for Measurement, Methodology, Analysis, and Policy (IMMAP) at Texas Tech University.
Elizabeth Grandfield – Doctoral student in Quantitative Psychology at the University of Kansas.