Introduction to the Best Practice Recommendations for Longitudinal Latent Transition Analysis

Area of Science:

• Quantitative Psychology
• Statistical Modeling

Background:

• Finite mixture models identify latent subgroups from diverse data.
• Methodological advancements in finite mixture modeling are ongoing.
• Latent transition analysis (LTA) is a key technique for subgroup analysis.

Purpose of the Study:

• To provide a tutorial on Latent Transition Analysis (LTA).
• To guide researchers in rigorously answering questions about transitions between latent classes.
• To promote adherence to best practices in LTA.

Main Methods:

• Finite mixture modeling framework.
• Latent transition analysis (LTA) applied to longitudinal data.
• Illustrative example using three-timepoint sport psychology data on collegiate student-athletes' health behaviors.

Main Results:

• The tutorial details LTA's purpose, analysis steps, interpretation, and reporting.
• Supplemental materials include Mplus syntax, decision processes, results, tables, and figures.
• Demonstrates practical application of LTA in a specific research context.

Conclusions:

• LTA is a valuable tool for understanding dynamic subgroup membership.
• This tutorial enhances the applicability and approachability of LTA for researchers.
• Best practices for LTA are crucial for rigorous and reliable findings.