2016 Winter Stata Summer School:

Longitudinal Data Analysis Using Structural Equation Modeling

Taught by Paul Allison, Ph.D.
February 25-26, Hotel Birger Jarl Conference
Stockholm, Sweden 

Read reviews of this seminar 

For the past eight years, Professor Paul Allison has been teaching his acclaimed two-day seminar “Longitudinal Data Analysis Using Stata”. In this new seminar he takes up where that course leaves off, with methods for analyzing panel data using structural equation modeling.

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. Several real data sets are analyzed in great detail, testing out alternative methods and working toward an optimal solution. Both the –sem- and the –gsem- commands will be explored. In addition, Professor Allison will explain his new –xtdpdml- command which radically reduces the programming necessary to run the panel data models.

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.

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. Note: the methods covered in this course require panel data with at least three time points, and the number of individuals should be substantially larger than the number of time points.


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 Stata,


If you wish to do the optional exercises, you are advised to bring your laptop with Stata installed (versions 13 or 14). Power outlets and Wi-Fi will be provided.

If you plan to bring your own data, it’s best to bring it in both the wide form (one record per individual) and the long form (multiple records per individual). SEM estimation of dynamic panel models requires that the data be in the wide form. 



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. 


 Please go to the Metrika website for information on registration, and discounted hotel accommodations.


  • Characteristics of panel data
  • Objectives of panel modeling methods
  • Review
    • Random effects models
    • Fixed effects models
    • Cross-lagged panel models
    • SEM modeling
  • Problems
    • Dependence among repeated measures
    • Lagged dependent variables
    • Problems with reciprocal causation
  • Difference scores for T=3
  • Econometric solutions
  • 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
  • Linear and quadratic effects of time.
  • Missing data by FIML
  • Higher-level clustering
  • Alternative estimators
  • 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 course provided detailed instructions on the implementation of a modern approach to data analysis, with a discussion of common pitfalls and plenty of opportunity for questions.”
  Greg Pavela, The University of Alabama at Birmingham 

“This course completely changed my thinking about how to analyze panel data. SEM offers so much more flexibility than conventional methods and allows one to address, and, in some cases, overcome, the limitation of such methods.”
  Bill Carbonaro, University of Notre Dame 

“The course made me learn a lot more about SEM and to look at the possible problems I might encounter during the analysis. Professor Paul Allison is very clear on his lecture, and even with little back ground in SEM, I can say I learned a lot.”
  Ruben Ladwig  

“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