May 22 to May 26, 2017
Chapel Hill, North Carolina
Instructors: Dan Bauer and Patrick Curran
Software Demonstrations: Mplus and Stata
Longitudinal Structural Equation Modeling is a five-day workshop focused on the application and interpretation of structural equation models fitted to repeated measures data. The analysis of longitudinal data (i.e., the repeated measurement of the same cases over time) has become fundamental in most areas of social and behavioral science research. There are many structural equation models available for analyzing repeated measures data, ranging from two time-point autoregressive models to complex multivariate latent curve models spanning multiple time periods. The goal of this workshop is to present a variety of longitudinal SEMs ranging from traditional methods to recent advances in latent curve and latent change score modeling. Topics include longitudinal factor analysis, autoregressive cross-lagged models, latent change score models, latent curve models, and observed and latent multiple group growth models (a.k.a. mixture models). This course offers a comprehensive examination of the SEM as a foundation for the estimation of a variety of models for testing hypotheses about stability and change in repeated measures data, as described below.
Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences. Importantly, we recommend that you have prior experience with structural equation models (SEMs). We will provide a general review of the SEM, but we assume that all participants have a working knowledge of this modeling framework. Participants without prior SEM training are encouraged to consider our five-day workshop in Structural Equation Modeling that is scheduled prior to Longitudinal Structural Equation Modeling, although any introduction to SEM would be sufficient.
Software demonstrations will be provided in separate Mplus and Stata break-out groups at the end of each day (you choose which to attend). While it is helpful to have some familiarity with Mplus or Stata, this is not necessary. The lectures which constitute the majority of the workshop are software-independent.
Note: This workshop is a significant expansion of our previously offered three-day workshop focused on latent curve modeling. Prior participants in our LCM class may still wish to attend to obtain a broader overview of longitudinal SEM, but should be aware that there is overlap in the portion of the current workshop that focuses on LCMs.
Our motivating goal is to provide an intense yet enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in longitudinal structural equation modeling. We strive to strike an equal balance between core concepts of the underlying statistical models along with the practical application and interpretation of longitudinal SEMs fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of the longitudinal SEM and to be able to thoughtfully apply a variety of basic and advanced models to their own data.
In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our 2016 LSEM workshop:
One of the best statistical workshops I’ve ever taken, the first where I would go home and work for additional hours analyzing data or reading the notes for greater understanding of the material.
This workshop provides clear, understandable, and engaging teaching with wonderful resources for the future. Dan and Patrick are fantastic instructors, and they provide a very comfortable, enjoyable atmosphere for a week-long stats workshop.
This is the best option for training that will effectively prepare you to foray into the method. It greatly expanded my perspective on longitudinal analysis.
Excellent instructors, a great opportunity to clarify issues that I was always unsure about, helpful and comprehensive materials that are priceless!
The instruction was first-rate. It’s no surprise Curran & Bauer are award-winning instructors.
Dan Bauer and Patrick Curran co-teach the workshop and alternate lecturing throughout the day. We provide approximately 35 hours of total lecture time as well as a bound copy of the course notes and the computer demonstrations (approximately 600 pages total). Although there is not a hands-on computer lab component to this workshop, we provide extensive live demonstrations in Mplus and Stata and distribute the data and code for all examples. Further, participants are welcome to bring personal laptop computers to follow along with the software demonstrations or to work on their own data applications.
For examples of our course materials see sample copies of our structural equation modeling lecture notes and associated Mplus demonstration notes, as well as our multilevel modeling lecture notes and SAS software notes.
All participants also have the opportunity to sign up for individual consulting meetings on Wednesday, Thursday, or Friday.
A continental breakfast and continuous snack and beverage service are also provided each day.
Each day begins at 8:30 with a free continental-style breakfast provided at the conference center. The morning session is from 9:00 to 12:15 and includes a mid-morning break. Lunch is from 12:15 to 1:30 and attendees can select from a large number of restaurants in downtown Chapel Hill. The afternoon session continues from 1:30 to 3:30 and includes a mid-afternoon break. Finally, on Monday through Thursday, two break-out sessions each led by Dan and Patrick are held from 3:30 to 5:00 that are focused on the demonstration of fitting models in either Mplus or Stata. The day concludes at 5:00 except on the final day which concludes at approximately 3:30 to allow for afternoon travel. Finally, we will host a happy hour on Monday at 5:00 with appetizers and drinks at The Back Bar which is located on the same floor as the lecture hall.
We are very pleased to hold our workshop in the Great Room located at the Top of the Hill Restaurant and (yes) Brewery. The Great Room is located at 100 East Franklin Street at the historic intersection of Franklin Street and Columbia Street in downtown Chapel Hill and is just steps from the campus of the University of North Carolina. The Great Room is housed in the space once occupied by the legendary Carolina Theater and is characterized by soaring ceilings, hard wood floors, red brick walls, and flowing natural light.
We have reserved a block of reduced-rate rooms at the Hampton Inn for workshop participants that will be available until April 21st or the block is sold out (if you prefer to make a reservation by phone, please indicate you are with the Curran-Bauer Analytics group). Set in the heart of Chapel Hill/Carrboro, the Hampton Inn is less than one mile from the Great Room and it is approximately a 15 minute walk from the hotel to the meeting room. Chapel Hill also offers free buses and the J-line bus route stops right in front of the hotel and then at Franklin and Columbia, the intersection nearest the Great Room.
There are also many additional local hotels available, some within walking distance and others which offer free shuttle service to and from the Great Room.
Tuition for the five-day workshop is $1795 per registrant. We offer a fixed number of reduced-price registrations of $1295 for graduate students who are actively enrolled in a recognized masters or doctoral training program. No application is necessary to qualify for the student tuition rates; simply select “student” when beginning the registration process. Confirmation of student status may be requested at a later time. We will maintain a waitlist once the student seats are filled.
We also offer a 10% reduction in total tuition when enrolling in two or more workshops. Simply proceed through the registration process and enter the code multi when prompted.
We are fortunate to have the opportunity to work in collaboration with the Society of Multivariate Experimental Psychology (SMEP) to provide a limited number of financial awards to students from under-represented groups to attend methodological workshops. These awards are made to qualifying students and post doctoral fellows with available funds of up to $1000 per student. Please see Support for Students from Underrepresented Groups to Attend Methodological Workshops for full details on both of these sources of support.
Curran-Bauer Analytics will refund registration fees for cancellations made with two weeks or more notice prior to the event. For credit card registrations, 10% will be deducted from the refund to pay transaction fees imposed by the credit card companies; there is an industry-imposed 4.95% charge to book the registration and another 4.95% to cancel the registration. For check or purchase order registrations, registration fees will be refunded in full. Registration fees are non-refundable if a cancellation is made less than two weeks before the event.
Chapter 1. Introduction, Summary of Traditional Models of Change, and Review of SEM
1.1 Introduction and Organization of the Workshop
1.2 Why Longitudinal Data are Needed
1.3 Raw Score Change versus Residualized Change
1.4 Traditional Methods as a Foundation for Autoregressive, Latent Change Score, & Latent Curve Models
1.5 Review of Structural Equation Models (SEMs)
Chapter 2. Longitudinal Measurement Models and Factorial Invariance
2.1 Assumptions Invoked when Modeling Manifest Variables Over Time
2.2 Measurement Invariance across Group
2.3 Measurement Invariance Over Time
2.4 The Longitudinal Confirmatory Factor Analysis Model
2.5 Defining and Testing Alternative Forms of Longitudinal Factorial Invariance
Chapter 3. The Autoregressive Cross-lagged (ARCL) Panel Model
3.1 Residualized Change as a Two Time-point Autoregressive Panel Model
3.2 Bivariate Autoregressive Cross-Lagged Models
3.3 Extensions of the ARCL
3.4 Strengths and Limitations of the ARCL
Chapter 4. The Unconditional Linear Latent Curve Model (LCM)
4.1 Defining a Latent Growth Curve
4.2 Latent Growth Curves as a Confirmatory Factor Model
4.3 Intercept-only vs. Intercept and Linear Slope LCMs
4.4 Alternative Metrics of Time
Chapter 5. Nonlinear and Conditional Latent Curve Models
5.1 Modeling Nonlinear Trajectories: Quadratic, Piecewise, and Freed-Loading Models
5.2 Time-Invariant Covariates (TICs): Defining Main Effects and Interactions
5.3 Probing and Graphing Effects Between TICs and Growth Factors
Chapter 6. Multivariate Latent Curve Models
6.1 Time-Varying Covariates: Defining Contemporaneous and Lagged Effects
6.2 The Fully Multivariate Latent Curve Model
6.3 The Autoregressive Latent Trajectory Model (ALT)
6.4 The LCM with Structured Residuals (LCM-SR) Model
Chapter 7. Longitudinal SEMs with Non-normal and Discrete Outcomes
7.1 Outcomes that are Continuous but Non-Normal
7.2 Longitudinal SEMs with Binary and Ordinal Outcomes: Concepts
7.3 Longitudinal SEMs with Binary and Ordinal Outcomes: Estimation and Interpretation
Chapter 8. Modeling Population Heterogeneity: Part 1
8.1 Population Heterogeneity in the Conventional LCM
8.2 The Multiple Groups Growth Model
8.3 Extensions of Multiple Groups LCM
Chapter 9. Modeling Population Heterogeneity: Part 2
9.1 Growth Mixture Models: Theory
9.2 Growth Mixture Models: Specification
9.3 Class Enumeration
9.4 Model Sensitivity
Chapter 10. The Latent Change Score (LCS) Model
10.1 Observed Change Scores vs. Latent Change Scores
10.2 Univariate LCS Model
10.3 Bivariate LCS Model
10.4 Extensions of the LCS Model
Please contact us either via email or by phone (919.533.9817) if you need any additional information or have any further questions.