2018 Methods Workshop
Presented by the Learning Performance Research Center
Additional sponsorship by Educational Psychology, Prevention Science, and Psychology
May 10-11, 2018 – 9:00 a.m. to 5:00 p.m.
About the Workshop: Latent Growth Curve Modeling
Longitudinal data are ubiquitous throughout the social and behavioral sciences and beyond, where researchers have questions about the nature of change over time as well as its determinants. This two-day seminar provides a thorough introduction to latent growth curve models, which facilitate an assessment of longitudinal change from within the structural equation modeling (SEM) framework. The seminar will start with a review of SEM with measured and latent variables, illustrating the use of Mplus for such models. Next, latent means models, which add a mean structure to typical covariance-based structural models, will be introduced and illustrated with Mplus. The seminar will then review more traditional longitudinal models within an SEM framework (repeated measure models, panel models, etc.) to finish laying the necessary foundations. The seminar will then move into a thorough coverage of traditional linear latent growth models, including but not limited to different time centering, uneven and varied time points, and time-independent covariates. Then topics will transition into more complex modeling variations, drawing from the following areas as time allows: nonlinear models, spline models, time-dependent covariates, growth models for treatments and interventions multidomain models, cohort-sequential models for planned missing data, second-order growth models latent-difference score models, growth models with categorical data, growth mixture models, power analysis in latent growth models.
Presenter: Dr. Gregory R. Hancock
Dr. Gregory R. Hancock is Professor, Distinguished Scholar-Teacher, and Director of the Measurement, Statistics and Evaluation Program in the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park, and Director of the Center for Integrated Latent Variable Research (CILVR). His research interests include structural equation modeling (SEM) and latent growth models, and the use of latent variables in (quasi)experimental design. His research has appeared in such journals as Psychometrika, Multivariate Behavioral Research, Structural Equation Modeling: A Multidisciplinary Journal, Psychological Methods, British Journal of Mathematical and Statistical Psychology, Journal of Educational and Behavioral Statistics, and Educational and Psychological Measurement. He also co-edited with Ralph O. Mueller the volumes Structural Equation Modeling: A Second Course (2006; 2013) and The Reviewer’s Guide to Quantitative Methods in the Social Sciences (2010), with Karen M. Samuelsen the volume Advances in Latent Variable Mixture Models (2008), and with Jeffrey R. Harring the volume Advances in Longitudinal Methods in the Social and Behavioral Sciences (2012). He is past chair of the SEM special interest group of the American Educational Research Association (three terms), serves on the editorial board of a number of journals including Psychological Methods, Multivariate Behavioral Research, and Structural Equation Modeling: A Multidisciplinary Journal, and has taught well over 100 methodological workshops in the United States, Canada, and abroad. He is a Fellow of the American Educational Research Association, the American Psychological Association, and the Association for Psychological Science, an elected member of the Society of Multivariate Experimental Psychology, and received the 2011 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring by the American Psychological Association. Dr. Hancock holds a Ph.D. from the University of Washington.
Remote (AMS): $30
2017 – Roy Levy, Arizona State University – Foundations of Bayesian Statistical Modeling
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