Longitudinal Data Analysis   A  2-Day Course on Regression Methods for Analyzing Panel Data Panel data offer major opportunities and serious pitfalls   The most common type of longitudinal data is panel data, consisting of measurements of predictor and response variables at two or more points in time for many individuals.  Such data have two major attractions: the ability to control for unobservables, and the determination of causal ordering.   However, there is also a major difficulty with panel data: repeated observations are typically correlated and this invalidates the usual assumption that observations are independent.  There are four widely available methods for dealing with dependence:  robust standard errors, generalized estimating equations, random effects models and fixed effects models.  This couse examines each of these methods in some detail, with an eye to discerning their relative advantages and disadvantages.  Different methods are considered for quantitative outcomes, categorical outcomes, and count data outcomes.     This course is based in part on Paul Allison’s Fixed Effects Regression Methods for Longitudinal Data Using SAS, published by the SAS Institute in 2005.    Who should attend?   If you need to analyze longitudinal data and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. And it is also helpful to have some familiarity with logistic regression. But you do not need to know matrix algebra, calculus, or likelihood theory.    Materials   Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.   Registration and Lodging   The fee of $650 includes all course materials and a continental breakfast each day.  To ensure your participation, click here to register. Cancellations received two weeks before the course begins are fully refundable (minus a $50 processing fee if you paid by credit card).    Participants must make their own arrangements for lodging and meals. Special reduced rates have been arranged at a nearby hotel.   Computing   This course will use SAS for the many empirical examples, but lecture notes using Stata are also available to course participants.  No computers will be provided on site and there will be no supervised exercises.  However, you are welcome to bring your own laptop and perform the distributed exercises on your own time.     Course outline Opportunities and challenges of panel data. Data requirements Control for unobservables Determining causal order Problem of dependence Software considerations Linear models Robust standard errors Random effects models Fixed effects models Hybrid models Logistic regression models Robust standard errors Subject-specific vs. population averaged methods Random effects models Fixed effects models Hybrid models Count data models Poisson models Negative binomial models Fixed and random effects Linear structural equation models Fixed and random effects in the SEM context Models for reciprocal causation with lagged effects