Date(s) - 10/25/2019 - 10/26/2019
9:00 am - 5:00 pm
For the past ten years, Professor Paul Allison has been teaching his acclaimed two-day seminars on Longitudinal Data Analysis Using SAS and Longitudinal Data Analysis Using Stata. In this seminar he takes up where those courses leave off, with methods for analyzing panel data using software for structural equation modeling (SEM).
Statistical Horizons is hosting a two day seminar taught by Dr. Paul Allison on Longitudinal Data Analysis Using Structural Equation Modeling. The course will be taught on October 25 at 9 and end on October 26.
Professor Allison has recently shown that dynamic panel models can easily be estimated by maximum likelihood with SEM software (ML-SEM). This seminar takes a deep dive into the ML-SEM method for estimating dynamic panel models, exploring the ins and outs of assumptions, model specification, software programming, model evaluation and interpretation of results. We’ll work through several real data sets in great detail, testing out alternative methods and working toward an optimal solution. This is an applied course with a minimal number of formulas and a maximal number of examples.
Panel data have two big attractions for making causal inferences with non-experimental data:
- The ability to control for unobserved, time-invariant confounders.
- The ability to investigate the direction of causal relationships.
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.
The course cost $995 to attend, which includes all the necessary materials. To attend this course please CLICK HERE.