Latent Growth Curve Modeling - Online Course
A 4-Day Livestream Seminar Taught by
Aidan Wright10:30am-12:30pm (convert to your local time) Tuesday-Friday
1:30pm-3:30pm Tuesday, Wednesday, Friday
Behavioral, health, and natural scientists are often interested in modeling trajectories of change. These trajectories of change may represent aging, learning, development, or degeneration, among other constructs. Generically these processes can be referred to as growth. Latent Growth Curve Models (LGMs) in a structural equation modeling framework offer a powerful and highly flexible approach to studying normative (i.e., average) change and individual heterogeneity (i.e., individual differences) in that change.
The change process can be linear or non-linear, including accelerating, decelerating, and discrete/abrupt change. Because LGMs are special cases of structural equation models (SEMs), they offer many attractive features, including the ability to study multivariate change, account for measurement error, study predictors of trajectories, use trajectories as predictors of other outcomes, as well as adjust for time-varying covariates.
This seminar offers an in-depth treatment of LGMs, starting with basic two-wave models of change and then building up to complex multivariate models of growth in two or more processes simultaneously. Data for planned exercises will be provided, but you are encouraged to bring your own data for hands-on exercises and discussions.
Starting June 6, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
Computing
The empirical examples and exercises in this course will emphasize Mplus and the lavaan package in R. Code for each software will be provided. Mplus is arguably the most powerful SEM package available with the most flexibility and functionality. However, lavaan in R is also an excellent basic SEM package and it is free to download and use.
R users are encouraged to install RStudio, a free software designed to add to and facilitate R’s functionality. You are welcome to use other packages (e.g., LISREL, SAS, Stata), but examples and code will not be provided.
To fully benefit from the course, you should use your own computer with a recent version of Mplus or R (with the lavaan package) installed. You should already be familiar with basic data management operations and the commands/procedures for doing linear regression, logistic regression, etc. in the package of your choice.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
The empirical examples and exercises in this course will emphasize Mplus and the lavaan package in R. Code for each software will be provided. Mplus is arguably the most powerful SEM package available with the most flexibility and functionality. However, lavaan in R is also an excellent basic SEM package and it is free to download and use.
R users are encouraged to install RStudio, a free software designed to add to and facilitate R’s functionality. You are welcome to use other packages (e.g., LISREL, SAS, Stata), but examples and code will not be provided.
To fully benefit from the course, you should use your own computer with a recent version of Mplus or R (with the lavaan package) installed. You should already be familiar with basic data management operations and the commands/procedures for doing linear regression, logistic regression, etc. in the package of your choice.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This seminar is designed for applied data analysts interested in studying change processes or trajectories over time. LGMs are ideally suited for modeling change in data that has 3 to approximately 8 waves of observations. They are less suited for modeling variability or fluctuations in intensive longitudinal data (e.g., considerably more than 10 waves of observations per person).
This is an intermediate-level course, and attendees are expected to be familiar with the basics of SEM, and have some experience estimating and implementing models in an SEM. Typically this would mean having taken at least one introductory course or workshop.
Highly-experienced SEM users who are not deeply familiar with longitudinal SEM and estimating change processes will also find this course useful.
This seminar is applied in tone, with technical excursions as necessary. The primary way of communicating models will be using diagrammatic notation (i.e., diagrams) with an emphasis on translating them into software code and supplemented with equations and matrices as needed to highlight technical points of estimation and troubleshooting.
This seminar is designed for applied data analysts interested in studying change processes or trajectories over time. LGMs are ideally suited for modeling change in data that has 3 to approximately 8 waves of observations. They are less suited for modeling variability or fluctuations in intensive longitudinal data (e.g., considerably more than 10 waves of observations per person).
This is an intermediate-level course, and attendees are expected to be familiar with the basics of SEM, and have some experience estimating and implementing models in an SEM. Typically this would mean having taken at least one introductory course or workshop.
Highly-experienced SEM users who are not deeply familiar with longitudinal SEM and estimating change processes will also find this course useful.
This seminar is applied in tone, with technical excursions as necessary. The primary way of communicating models will be using diagrammatic notation (i.e., diagrams) with an emphasis on translating them into software code and supplemented with equations and matrices as needed to highlight technical points of estimation and troubleshooting.
Seminar outline
Day 1
-
- Review of SEM basics
- Two-wave change models
- Residualized change
- Latent difference scores
Day 2
-
- Univariate growth models
- Linear growth models
- Non-linear growth models
- Polynomials
- Spline/piece-wise models
- Exponential
- Latent basis models
Day 3
-
- Univariate growth models continued
- Including time-invariant covariates
- Including time-varying covariates
- Multivariate growth models
- Parallel process growth curves
- Higher-order growth curves
Day 4
-
- Multivariate growth models continued
- Cross-lagged panel models
- Random intercept cross-lagged panel models
- Latent growth curves with structured residuals
Day 1
-
- Review of SEM basics
- Two-wave change models
- Residualized change
- Latent difference scores
Day 2
-
- Univariate growth models
- Linear growth models
- Non-linear growth models
- Polynomials
- Spline/piece-wise models
- Exponential
- Latent basis models
- Univariate growth models
Day 3
-
- Univariate growth models continued
- Including time-invariant covariates
- Including time-varying covariates
- Multivariate growth models
- Parallel process growth curves
- Higher-order growth curves
- Univariate growth models continued
Day 4
-
- Multivariate growth models continued
- Cross-lagged panel models
- Random intercept cross-lagged panel models
- Latent growth curves with structured residuals
- Multivariate growth models continued
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.