Longitudinal Data Analysis Using Structural Equation Modeling - 6 & 7 October 2016

The Platform for Psychological Science at UL (PPS-UL) & Statistical Horizons present a 2-Day Seminar Taught by Paul Allison, Ph.D.:
"Longitudinal Data Analysis Using Structural Equation Modeling"

This seminar is designed for those who want to analyze longitudinal data with three or more time points, and whose primary interest is in the effect of predictors that vary over time. You should have a solid understanding of basic principles of statistical inference, including such concepts as bias, sampling distributions, standard errors, confidence intervals, and hypothesis testing. You should also have a good working knowledge of the principles and practice of linear regression.

It is desirable, but not essential, to have previous training in either longitudinal data analysis, structural equation modeling, or both. If you’ve taken Professor Allison’s courses on either of these topics, you should be well prepared. You should also be an experienced user of at least one of the following statistical packages: SAS, Stata, Mplus, or R.

For more information or to register, please click on the link below


The course fee is €895 which covers all course materials.  Special Registration Fee for UL staff/students: UL staff: €130 / UL student: €90

UL staff/students should contact Dr. Deirdre O'Shea for more information on how to register.



Paul Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is widely recognized as an extraordinarily effective teacher of statistical methods who can reach students with highly diverse backgrounds and expertise.
After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.
Much of his early research focused on career patterns of academic scientists. At present, his principal methodological research is on the analysis of longitudinal data, especially with determining the causes and consequences of events, and on methods for handling missing data.
A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”

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