Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data

Authors

Ji Hoon Ryoo, University of VirginiaFollow
Cixin Wang, University of Maryland at College Park
Susan M. Swearer, University of Nebraska–LincolnFollow
Michael Hull, University of Virginia
Dingjing Shi, University of Virginia

Document Type

Article

Date of this Version

2018

Citation

Front. Psychol. 9:675.

Comments

Copyright Ryoo et al.

Open access

doi: 10.3389/fpsyg.2018.00675

Abstract

Applications of latent transition analysis (LTA) have emerged since the early 1990s, with numerous scientific findings being published in many areas, including social and behavioral sciences, education, and public health. Although LTA is effective as a statistical analytic tool for a person-centered model using longitudinal data, model building in LTA has often been subjective and confusing for applied researchers. To fill this gap in the literature, we review the components of LTA, recommend a framework of fitting LTA, and summarize what acceptable model evaluation tools should be used in practice. The proposed framework of fitting LTA consists of six steps depicted in Figure 1 from step 0 (exploring data) to step 5 (fitting distal variables). We also illustrate the framework of fitting LTA with data on concerns about school bullying from a sample of 1,180 students ranging from 5th to 9th grade (mean age = 12.2 years, SD = 1.29 years at Time 1) over three semesters. We identified four groups of students with distinct patterns of bullying concerns, and found that their concerns about bullying decreased and narrowed to specific concerns about rumors, gossip, and social exclusion over time. The data and command (syntax) files needed for reproducing the results using SAS PROC LCA and PROC LTA (Version 1.3.2) (2015) and Mplus 7.4 (Muthén and Muthén, 1998–2015) are provided as online supplementary materials.