Flexible and modular latent transition analysis-A tutorial using R.

Area of Science:

• Statistics
• Data Analysis
• Social Sciences

Background:

• Latent transition analysis (LTA) is a statistical method to study changes in group membership over time.
• Current LTA implementations are often restricted to commercial or specialized software.
• Understanding predictors of these transitions is crucial in many research fields.

Purpose of the Study:

• To present a flexible and modular R-based approach for conducting Latent Transition Analysis (LTA).
• To offer an open-source alternative to existing commercial software for LTA.
• To demonstrate the utility of combining latent class analysis and multiple logistic regression models in R.

Main Methods:

• The study proposes an R-based methodology integrating latent class analysis and multiple logistic regression.
• This approach allows for detailed examination of transition probabilities and their predictors.
• The tutorial provides R code snippets and a reproducible script for practical application.

Main Results:

• The R-based LTA approach yielded results comparable to commercial software.
• The novel approach identified similar patterns of class prevalence and transition probabilities.
• Additional insights and flexibility in model assumptions, covariate adjustment, and missing data handling were achieved.

Conclusions:

• A powerful and flexible R-based alternative for Latent Transition Analysis (LTA) is now available.
• This open-source method enhances accessibility and offers advanced modeling capabilities.
• Researchers can now conduct sophisticated LTA with greater control over model specifications and data handling.