How to Choose a Model for Longitudinal Data - Online Course
A 3-Day Livestream Seminar Taught by
Kenneth A. Bollen10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
With the growing availability of longitudinal data, researchers are inevitably confronted with the challenge of choosing an appropriate model. The range of possibilities is enormous. In an ideal world, theory and substantive arguments would be sufficiently clear to dictate a single, best model. But in practice, there is usually little guidance and much confusion. Often, researchers find themselves limited by conventional models and trends that are specific to their field, potentially missing out on more effective alternatives.
This seminar is designed to break those boundaries. It will teach and illustrate empirical methods for comparing and selecting the most suitable longitudinal models. Building on a structural equation modeling framework, it will introduce autoregressive models, random and fixed effects, latent growth curve models, and the autoregressive latent trajectory (ALT) model.
You’ll then learn how these and other models can be embedded in a general latent variable ALT (LV-ALT) model. This approach not only streamlines the selection of an appropriate longitudinal model but also enhances your ability to interpret and communicate the results from complex models with confidence.
Starting January 14, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
The course begins by guiding you through the process of fitting multiple models to repeated measures for a single variable. The emphasis will be on practical skills like model estimation, assessing model fit, and comparing different models against the same data set. We then broaden the scope to the analysis of repeated measures for two or more variables, using detailed empirical examples to bring the theory to life.
Whether you’re a seasoned researcher or new to the field of longitudinal data analysis, this seminar offers invaluable insights and practical skills to elevate your research, taught by one of the giants of the field.
The course begins by guiding you through the process of fitting multiple models to repeated measures for a single variable. The emphasis will be on practical skills like model estimation, assessing model fit, and comparing different models against the same data set. We then broaden the scope to the analysis of repeated measures for two or more variables, using detailed empirical examples to bring the theory to life.
Whether you’re a seasoned researcher or new to the field of longitudinal data analysis, this seminar offers invaluable insights and practical skills to elevate your research, taught by one of the giants of the field.
Computing
Examples and software code will be provided in the lavaan package for R . The class slides will present lavaan input and output runs using the class examples to illustrate the material. The input code for many of these examples also are provided in Mplus.
You are welcome and encouraged to use a computer with R (including the lavaan package) installed (or with Mplus). However, this is not required unless you want to run one or more of the class examples. You will benefit from the comprehensive set of slides, the input and output files for all examples programmed in lavaan, and the input code for about a dozen models in Mplus. You can apply these at a later time.
Basic familiarity with R (or Mplus) is highly desirable, but even novice coders should be able to follow the presentation and exercises.
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.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
Examples and software code will be provided in the lavaan package for R . The class slides will present lavaan input and output runs using the class examples to illustrate the material. The input code for many of these examples also are provided in Mplus.
You are welcome and encouraged to use a computer with R (including the lavaan package) installed (or with Mplus). However, this is not required unless you want to run one or more of the class examples. You will benefit from the comprehensive set of slides, the input and output files for all examples programmed in lavaan, and the input code for about a dozen models in Mplus. You can apply these at a later time.
Basic familiarity with R (or Mplus) is highly desirable, but even novice coders should be able to follow the presentation and exercises.
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.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
Who should register?
This seminar is designed for graduate students, faculty, and nonacademic researchers with interests in analyzing longitudinal data. Experience with structural equation models and longitudinal data is highly recommended. Additionally, the course does assume that you have a solid background in linear regression, which will not be reviewed.
Models will be presented using both path diagrams and equations. The emphasis is on assumptions, applications, and interpretations of the models.
This seminar is designed for graduate students, faculty, and nonacademic researchers with interests in analyzing longitudinal data. Experience with structural equation models and longitudinal data is highly recommended. Additionally, the course does assume that you have a solid background in linear regression, which will not be reviewed.
Models will be presented using both path diagrams and equations. The emphasis is on assumptions, applications, and interpretations of the models.
Seminar outline
Day 1
-
- Introduction
- Longitudinal data
- Types of variables
- Patterns of changes over time
- Models for one repeated measure with and without exogenous covariates
- Autoregressive
- Growth curve model
- Fixed and random effects models
- Autoregressive latent trajectory (ALT)
- Latent variable ALT
- Empirical examples
Day 2
-
- Model estimation
- Maximum likelihood (classic & robust versions)
- Empirical examples
- Model fit
- Chi square test, degrees of freedom, p-value
- Indexes of model fit
- TLI, RNI (CFI), IFI, RMSEA, BIC
- Components of fit
- Empirical examples
- Comparing fit of models
- Empirical examples
Day 3
-
- Models for two or more repeated measures
- Autoregressive
- Growth curve model
- Fixed and random effects models
- Autoregressive latent trajectory (ALT)
- Latent variable ALT
- Empirical examples
- Concluding thoughts
Day 1
-
- Introduction
- Longitudinal data
- Types of variables
- Patterns of changes over time
- Models for one repeated measure with and without exogenous covariates
- Autoregressive
- Growth curve model
- Fixed and random effects models
- Autoregressive latent trajectory (ALT)
- Latent variable ALT
- Empirical examples
- Introduction
Day 2
-
- Model estimation
- Maximum likelihood (classic & robust versions)
- Empirical examples
- Model fit
- Chi square test, degrees of freedom, p-value
- Indexes of model fit
- TLI, RNI (CFI), IFI, RMSEA, BIC
- Components of fit
- Empirical examples
- Comparing fit of models
- Empirical examples
- Model estimation
Day 3
-
- Models for two or more repeated measures
- Autoregressive
- Growth curve model
- Fixed and random effects models
- Autoregressive latent trajectory (ALT)
- Latent variable ALT
- Empirical examples
- Concluding thoughts
- Models for two or more repeated measures
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.