Stats Camp

Stats Camp is an advanced statistics camp for graduate students, post-docs, staff and faculty that takes places in the summer every year June. It is a non-profit organization that was founded by Todd D. Little in 2003 when a dozen faculty and graduate students attended Little's one week workshop SEM Foundations and Extended Applications course at the SpringHill Suites in Lawrence, Kansas. Stats camp was started to realize researchers' need for access to advanced statistical procedures driving current day research. Stats Camp is currently affiliated with Texas Tech University.
Course Structure
At the Stats Camp, a variety of courses are offered by different renowned researchers. These courses include.
#Introduction to Social Network Analysis using R and Rsiena: This course is designed primarily for researchers who are interested in conducting social network research, particularly those who are embarking upon it for the first time. With a focus on actor-oriented and tie-oriented characteristics of and changes within complete social networks, the course will survey a variety of approaches to analyzing network data at single or multiple points in time.
#Mixture Distribution Model: This course is an intensive short course in the fundamentals of latent class analysis and finite mixture modeling. Finite mixture models are a type of latent variable model that express the overall distribution of one or more variables as a mixture of a finite number of component distributions.
#Multilevel Modeling: This course focuses on the theory and practice of methods for analyzing hierarchically organized data. Topics include random effects, centering, multi-parameter tests, plotting and probing within-level and cross-level interactions, multilevel modeling for longitudinal data, and other applications of multilevel analysis.
#Data Mining & Big Data Analytics: This is an intensive short course on the principles and practice of data mining and big data analytics. Topics include data structures, exploring and transforming data, classification & regression trees vs. decision trees, random forests, boosting, and model performance and evaluation. Text mining and qualitative data analysis will also be touched upon. Extensions of decision trees for cost-benefit analysis will be demonstrated.
#Bayesian Data Analysis: This course covers principles of Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc.
#Mediation and Moderation: This course addresses methods to test why two variables are related (mediation) and when two variables are related (moderation). The course will cover classic and contemporary approaches to estimating moderation and mediation effects; topics include path analysis, indirect and direct effects, testing intervening variable effects, probing and plotting interactions, and combining moderation and mediation.
#Psychometrics: Topics covered in this stats camp course include measurement and statistical concepts, scaling, validity, reliability, factor analysis, item and test bias and the role each plays in test fairness, introduction to item response theory, introduction to generalizability theory,survey of advanced topics in psychometrics, developing norms and conducting test score equating.
#SEM with Mplus: This course is a workshop on using Mplus v7.
#Longitudinal SEM: This course, sponsored by IMMAP, is an advanced intensive short course in the analysis of longitudinal data using SEM.
#Advanced SEM: This course provides instruction on advanced topics in the world of structural equation modeling.
#Meta-Analysis: This course teaches the skills necessary to conduct and write publishable meta-analytic reviews, including methods of searching the empirical literature, coding effect sizes, and analyzing effect sizes across multiple studies.
#Modern Biostatistics: This course provides instruction on advanced topics in the world of Biostatistics. Students are also provided instruction on cutting edge topics and techniques used in this field.
#Program Evaluation and Cost-Benefit Analysis: This is an intensive short course on the principles and practices of program evaluation and cost-effectiveness and cost-benefit analysis. Topics include program theory, needs assessment, monitoring progress and measuring outcomes, cost-effectiveness vs. cost-benefit analysis, time and discounting, sensitivity analysis and risk analysis, and SEM and decision tree extensions of cost-benefit analysis.

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