Latent Growth Curve Modeling - Online Course
A 3-Day Livestream Seminar Taught by
Dan McNeish10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This course provides an introduction to latent growth curve modeling, a class of models within the structural equation modeling framework that can be used to model change or growth in longitudinal data.
The course is intended for those who are looking to familiarize themselves with growth modeling. No previous experience with longitudinal data is assumed. The emphasis of the course is to build a solid foundation for (1) why longitudinal data require specialized methods, (2) the opportunities and unique research questions that can be asked and answered with longitudinal data, (3) the conceptual underpinning of latent growth curve models and how they accommodate features of longitudinal data, and (4) when latent growth curve models may or may not be ideally suited to help researchers model their longitudinal data.
As a rudimentary outline for the course, we will begin with an overview of longitudinal data to contextualize how latent growth curve models fit into the broader landscape of longitudinal modeling and to identify the types of questions that latent growth models can help answer. Then, we will introduce the basic tenets of latent growth curve modeling. Lastly, we will cover some more advanced topics that routinely arise in empirical analyses.
Starting January 18, we are offering this seminar as a 3-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
Examples and software code will be provided in both Mplus and the lavaan package in R.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
Participants who want a brief introduction or refresher on the basics of the lavaan package in R prior to the course may wish to consult lavaan’s tutorial website here.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
Examples and software code will be provided in both Mplus and the lavaan package in R.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
Participants who want a brief introduction or refresher on the basics of the lavaan package in R prior to the course may wish to consult lavaan’s tutorial website here.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
Who should register?
Graduate students, postdocs, early career researchers, and continuing researchers in academia or industry looking to gain a better understanding of the idea behind growth modeling and how these models can be applied to real data.
The course does not assume any background in longitudinal data analysis, but a working knowledge of principles in linear regression will be assumed and will not be reviewed. Any experience or familiarity with multilevel modeling or structural equation modeling will be helpful, but no knowledge of these topics is assumed and relevant topics from these areas will be covered to the extent needed to fully engage with latent growth curve modeling.
Some equations will be provided to foster a deeper understanding of the modeling methods covered, but the focus will be on application and interpretation of the methods so that participants will be able to fit growth curve models to data by the conclusion of the course.
Graduate students, postdocs, early career researchers, and continuing researchers in academia or industry looking to gain a better understanding of the idea behind growth modeling and how these models can be applied to real data.
The course does not assume any background in longitudinal data analysis, but a working knowledge of principles in linear regression will be assumed and will not be reviewed. Any experience or familiarity with multilevel modeling or structural equation modeling will be helpful, but no knowledge of these topics is assumed and relevant topics from these areas will be covered to the extent needed to fully engage with latent growth curve modeling.
Some equations will be provided to foster a deeper understanding of the modeling methods covered, but the focus will be on application and interpretation of the methods so that participants will be able to fit growth curve models to data by the conclusion of the course.
Seminar outline
Day 1
-
- Different types of longitudinal data (e.g., growth, time-series, survival)
- Special considerations with longitudinal data vs. data covered in linear regression courses
- They are not independent and covariance between time points is important
- They may not be homoskedastic and variances may change over time
- Exploratory data analysis and plotting
- Brief overview of repeated measures ANOVA
- Weaknesses of the method and why more modern approaches can be helpful
- How growth models differ from typical linear regression
Day 2
-
- The idea behind random effects/latent variables for intercepts and slopes
- How this helps decompose the variance into between-person and within-person sources
- How this permits research questions about individual differences
- How to fit and interpret a basic linear growth curve model
- Example analyses, software applications, and output interpretation
Day 3
-
- How to expand the model with time-invariant or time-varying predictors
- Differences between these types of predictors
- How to fit these models in software
- How to interpret output with predictors
- Centering predictors and the important role this plays in avoiding conflated coefficients
Topics Covered if Time Permits
-
- Modeling non-linear change over time
- Accommodating data where each person has a different number of time-points or where each person was measured at different times
- Assess model fit
- Missing data
- Full information methods, auxiliary variables, and overview of selection models
Day 1
-
- Different types of longitudinal data (e.g., growth, time-series, survival)
- Special considerations with longitudinal data vs. data covered in linear regression courses
- They are not independent and covariance between time points is important
- They may not be homoskedastic and variances may change over time
- Exploratory data analysis and plotting
- Brief overview of repeated measures ANOVA
- Weaknesses of the method and why more modern approaches can be helpful
- How growth models differ from typical linear regression
Day 2
-
- The idea behind random effects/latent variables for intercepts and slopes
- How this helps decompose the variance into between-person and within-person sources
- How this permits research questions about individual differences
- How to fit and interpret a basic linear growth curve model
- Example analyses, software applications, and output interpretation
- The idea behind random effects/latent variables for intercepts and slopes
Day 3
-
- How to expand the model with time-invariant or time-varying predictors
- Differences between these types of predictors
- How to fit these models in software
- How to interpret output with predictors
- Centering predictors and the important role this plays in avoiding conflated coefficients
- How to expand the model with time-invariant or time-varying predictors
Topics Covered if Time Permits
-
- Modeling non-linear change over time
- Accommodating data where each person has a different number of time-points or where each person was measured at different times
- Assess model fit
- Missing data
- Full information methods, auxiliary variables, and overview of selection models
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.