Longitudinal Structural Equation Modeling

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, R, and Stata break-out groups at the end of each day (you choose which to attend). Note that R can be downloaded for free. While it is helpful to have some familiarity with Mplus, R, or Stata, this is not necessary. The lectures which constitute the majority of the workshop are software-independent.

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.

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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, R, 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 continuous beverage service and afternoon snacks are provided each day. Participants are also welcome to enjoy the breakfast buffet at the Hampton Inn before class begins each morning.

Learn more about what makes our training unique...

We begin each day at 9:00 and continue until 12:15 with a mid-morning break. Lunch is from 12:15 to 1:30 and attendees can select from a large number of restaurants in the downtown area. The afternoon session continues from 1:30 to 3:30 and includes a mid-afternoon break. On Monday through Thursday, break-out sessions are held from 3:30 to 5:00 that focus on fitting models in Mplus, R, 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.

We are very pleased to hold our workshop in the grand meeting room of the Chapel Hill-Carrboro Hampton Inn & Suites located at 370 East Main Street in Carrboro, NC. The Hampton Inn is set in the heart of Chapel Hill-Carrboro and is within walking distance of the campus of the University of North Carolina as well as to many dining, entertainment, and leisure options throughout the downtown area. All participants have full access to the breakfast buffet as well as drinks throughout the day and a light afternoon snack. Complimentary day time parking is provided at the parking deck immediately behind the Hampton Inn for workshop participants.

We have reserved a block of rooms in the Hampton Inn at a reduced rate that will be available until four weeks prior to the workshop or until the block is sold out. The hotel offers a wide range of amenities including workout facilities, a swimming pool, coin operated laundry, and much more. Rooms can be booked through online reservations or via phone at 919.969.6989 (refer to LSE group code).

There are also many additional local hotels available, some within walking distance and others which may offer a shuttle service to the downtown.

We offer a 10% reduction in total tuition for group enrollments (code GROUP) and for individuals enrolling in two or more workshops (code MULTI).

We also offer a fixed number of reduced-price registrations 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 choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

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 fully refund registration fees for cancellations made with two weeks or more notice prior to the event. 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 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. Longitudinal SEMs with Non-normal and Discrete Outcomes
6.1 Outcomes that are Continuous but Non-Normal
6.2 Longitudinal SEMs with Binary and Ordinal Outcomes: Concepts
6.3 Longitudinal SEMs with Binary and Ordinal Outcomes: Estimation and Interpretation

Chapter 7. Multivariate Latent Curve Models
7.1 Time-Varying Covariates: Defining Contemporaneous and Lagged Effects
7.2 The Fully Multivariate Latent Curve Model
7.3 The Autoregressive Latent Trajectory Model (ALT)
7.4 The LCM with Structured Residuals (LCM-SR) Model

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

Appendix. The Latent Change Score (LCS) Model
A.1 Observed Change Scores vs. Latent Change Scores
A.2 Univariate LCS Model
A.3 Bivariate LCS Model
A.4 Extensions of the LCS Model