Longitudinal Data Analysis Using Structural Equation Modeling
A 2-Day Seminar taught by Paul D. Allison, Ph.D.
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 new seminar he takes up where those courses leave off, with methods for analyzing panel data using software for structural equation modeling (SEM).
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 determine the direction of causal relationships.
Fixed effects methods that control for unobserved confounders are now well known and widely used. Cross-lagged panel models have long been used to study the direction of causality. But trying to combine these two approaches poses serious problems.
To deal with these difficulties, econometricians have developed the Arellano-Bond methods for estimating dynamic panel data models. However, Arellano-Bond is known to be statistically inefficient and can also be severely biased in important situations.
Professor Allison has recently shown that dynamic panel models can easily be estimated by maximum likelihood with SEM software (ML-SEM). His recent work demonstrates that the performance of ML-SEM is superior to the econometric methods. Plus, ML-SEM offers better capabilities for handling missing data, evaluating model fit, relaxing constraints, and allowing for non-normal data.
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. Although the methodology is cutting edge, the emphasis is on how to actually do the analysis in order accomplish your objectives.
It’s also a hands-on course with lots of opportunities to practice the methods, including at least two hours of carefully-designed and supervised exercises using real data. You are strongly encouraged to bring your own data and try out the new methods.
Mplus will be the main software package used in this course. However, program code for three other packages will also be included in the slides: SAS, Stata, and the new lavaan package for R. The exercises can be done with any of these packages.
At the end of this seminar, you should be able to confidently apply the ML-SEM method for dynamic panel data to your own research projects. You will also have a thorough understanding of the rationale, assumptions, and interpretation of these methods.
CAUTION: This is not a survey course but rather an intensive study of a specific methodology—one that has enormous potential for improving causal inference from panel data. This seminar does NOT cover several popular methods that use SEM to analyze panel data, including:
- Latent growth curve models
- Latent trajectory models
- Common factor models
The methods covered in this course are NOT typically useful in analyzing data from randomized experiments. That’s because dynamic panel models are designed for situations in which the predictor variables of central interest vary over time. These methods are also not designed for prediction—rather, the goal is to maximize the ability to make valid causal inference. To use these methods, you need panel data with at least three time points, and the number of individuals should be substantially larger than the number of time points.
Who should attend?
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.
Location, Format, AND Materials
The seminar meets Thursday, June 22 and Friday, June 23 at the Courtyard Boston Downtown, 275 Tremont Street, Boston, MA 02116.
The class will meet from 9 to 5 each day with a 1-hour lunch break.
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 $995.00 includes all seminar materials. The early registration fee of $895 is available until May 22.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of rooms has been reserved at the Courtyard Boston Downtown, 275 Tremont Street, Boston, MA 02116 at a special rate of $269 per night. In order to guarantee rate and availability, make your reservations by calling Marriott Reservation 1-(800) 321-2211 or (617) 426-1400 no later than, Wednesday, May 31, 2017 and identify yourself as part of the Statistical Horizons Meeting group.
- Characteristics of panel data
- Objectives of panel modeling methods
- Random effects models
- Fixed effects models
- Cross-lagged panel models
- SEM modeling
- Dependence among repeated measures
- Lagged dependent variables
- Problems with reciprocal causation
- Difference scores for T=3
- Econometric solutions
- System estimator
- ML methods
- SEM for random effects
- SEM for fixed effects
- SEM with lagged endogenous and predetermined variables.
- SEM compared with Arellano-Bond
- SEM details
- Evaluating model fit.
- Relaxing constraints
- Higher-order lags
- Likelihood ratio tests
- Allowing coefficients to vary with time
- Missing data by FIML
- Random coefficients
- Higher-level clustering
- Alternative estimators
- Other latent variables
- Categorical outcomes
“Dr. Allison is a spectacular teacher! He obviously has years of experience with teaching because he organizes his slides well, knows the material inside and out, is very patient and responds to questions very well, and can judge the optimal pace of the class. I found the preparatory materials (recommended articles and slides) extremely helpful to review before the class began. I would highly recommend this class to other colleagues, especially those with a background in SEM.”
Mary M. Mitchel, Johns Hopkins University
“This class includes an emphasis on coding that I found extremely practical. I appreciate that Paul’s materials included coding examples from several software packages, while lectures focused on just two packages in the interest of time. Further, the provided materials will surely be a great resource after the class, they are clear and detailed.”
Sarah Spell, The Pew Charitable Trust
“This is a very informative, intense course on a novel approach that combines fixed effects model with cross-lagged model using the SEM approach. I truly appreciate that organized way that Dr. Alison provides information. It helps me to understand difficult concepts more easily.”
Hye Won Kwon, University of Iowa
“As a graduate student, this course was extremely helpful. I learned more in two days than two semesters of the same stat courses. Dr. Allison is very knowledgeable and presents complex material beautifully and clearly.”
Daniel Paulus, University of Houston
“I really appreciate the state-of-the-art insight shown by Paul Allison. Even as an only moderately experienced SEM user, I could follow well. The methods taught are very powerful for strategy/organization behavior studies.”
Sebastian Fourné, WHU – Otto Beisheim School of Management
“This course provided a better explanation of fixed versus random effects and showed how they are hidden within procedures like PROC GLM. It gives many examples of reciprocal causation for different number of time points, lending to a better appreciation of the limitation of having fewer waves of data. This is a good segue into growth curve modeling.”
Andy Kin On Wong, McMaster University & University Health Network
“This course provided an excellent discussion of how to apply different SEM models to panel data. It pulled from the latest publication on these topics and provided a framework to analyze my personal data. I was greatly appreciative of learning not just how to code with SEM, by the advantages and tools each software package offered.”
Elizabeth Hecht, North Carolina State University