2017 Methods Workshop
Presented by the Learning Performance Research Center
Sponsored by the following WSU departments: Prevention Science, Psychology, and ELSSECP
May 11-12, 2017 – 9:00 a.m. to 5:00 p.m.
About the Workshop: Bayesian Statistical Modeling
Bayesian approaches to statistical modeling and inference are characterized by treating all entities (observed variables, model parameters, missing data, etc.) as random variables characterized by distributions. In a Bayesian analysis, all unknown entities are assigned prior distributions that represent our thinking prior to observing the data. Once values for the data are observed, Bayes’ theorem is employed to yield a posterior distribution, representing a synthesis of the prior information and the information in the data. This approach to modeling departs, both practically and philosophically, from traditional frequentist methods that constitute the majority of statistical training. Importantly, adopting a Bayesian approach allows an analyst to accomplish statistical and inferential goals that cannot be attained by, or pose considerable challenges to, conventional frequentist approaches. Recent computational advances now allow researchers access to this wider class of models. Bayesian statistical modeling and inference is an attractive alternative to frequentist approaches in that a Bayesian perspective offers a coherent approach to statistical modeling, including building and fitting models with complex features, interpreting results, making inferences, and representing uncertainty.
This workshop provides both a theoretical and practical introduction to Bayesian statistical modeling. No prior experience with Bayesian statistical modeling is required. An understanding of Bayesian statistical modeling will be developed by relating it to participants’ existing knowledge of traditional frequentist approaches. The philosophical underpinnings and departures from conventional frequentist interpretations of probability will be explained. This in turn will motivate the development of Bayesian statistical modeling. Examples will be accompanied by input and output using the WinBUGS software package. These input files, as well as input files for other software that could be used, will be provided so that throughout the workshop participants will be able to practice exercises using the software; participants are strongly encouraged to bring their own laptop PCs to perform these exercises. (Participants will be instructed on how to download the software and access the input files prior to the course.)
Presenter: Dr. Roy Levy
Dr. Roy Levy is an Associate Professor of Measurement and Statistical Analysis in the T. Denny Sanford School of Social and Family Dynamics at Arizona State University. He holds a B.A. in Philosophy, an M.A. in Measurement, Statistics and Evaluation, and a Ph.D. in Measurement, Statistics and Evaluation from the University of Maryland. His research and teaching interests include methodological investigations and applications in psychometrics and statistical modeling, focusing on item response theory, structural equation modeling, Bayesian networks, and Bayesian approaches to inference and modeling. He also work in areas of assessment design, focusing on evidentiary principles and applications in complex assessments. His research has appeared in such journals as Structural Equation Modeling: A Multidisciplinary Journal, British Journal of Mathematical and Statistical Psychology, Multivariate Behavioral Research, Psychological Methods, Applied Psychological Measurement, Journal of Educational and Behavioral Statistics, and Sociological Methods and Research. A larger volume of his writing appears in a co-written book (with Robert Mislevy) entitled Bayesian Psychometric Modeling.
Remote (AMS): $30
2016 – Todd D. Little, Texas Tech University – Measurement, design, and analysis issues in longitudinal modeling with a particular focus on the longitudinal CFA model as the basis for both panel and latent growth curve modeling.
2014 – Bethany Bray, Penn State University – An Introduction to Latent Class and Latent Profile Analysis
2013 – Greg Hancock, University of Maryland – A First Course in Structural Equation Modeling