Latent Transition Analysis (LTA) : A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

Abstract The latent transition analysis (LTA) model is a version of Latent Class Analysis (LCA) which is used in longitudinal data analysis. The goal of LTA is to examine the variation over time and to identify the association of repeated measures. LTA gives an elegant solution to study heterogeneous changes in longitudinal data. As a classic LCA, it assumes that the data consists of several unknown groups that have homogeneous choices. This paper aims to present a review of assess the performance of LTA to identify the differences in longitudinal differences among unobserved classes. An example of LTA application in educational assessment was developed to illustrate the process and to explore the change among the time in reading comprehension.

[1]  D. Cicchetti,et al.  Identifying maltreatment subgroups with patterns of maltreatment subtype and chronicity: A latent class analysis approach. , 2019, Child abuse & neglect.

[2]  Maarten Kroesen,et al.  Travel Pattern Transitions: Applying Latent Transition Analysis Within the Mobility Biographies Framework , 2017 .

[3]  John P. Meyer,et al.  Are Commitment Profiles Stable and Predictable? A Latent Transition Analysis , 2016 .

[4]  Exploring heterogeneity in clinical trials with latent class analysis. , 2018, Annals of translational medicine.

[5]  A Divided Latent Class analysis for Big Data , 2017, FNC/MobiSPC.

[6]  J. Tein,et al.  Changes in depression among older adults in China: A latent transition analysis. , 2017, Journal of affective disorders.

[7]  F. Azizi,et al.  Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study , 2019, Scientific Reports.

[8]  Mary Beth Terry,et al.  Latent class analysis suggests four distinct classes of complementary medicine users among women with breast cancer , 2015, BMC Complementary and Alternative Medicine.

[9]  Melissa M. Pangelinan,et al.  The trajectory of balance skill development from childhood to adolescence was influenced by birthweight: a latent transition analysis in a British birth cohort. , 2019, Journal of clinical epidemiology.

[10]  H. Timmermans,et al.  The impact of business models on electric vehicle adoption: A latent transition analysis approach , 2018, Transportation Research Part A: Policy and Practice.

[11]  M. Larimer,et al.  Transitions in drinking behaviors across the college years: A latent transition analysis. , 2019, Addictive behaviors.

[12]  S. Kanjanawasee,et al.  Student Factors Affecting Latent Transition of Mathematics Achievement Measuring From Latent Transition Analysis with a Mixture Item Response Theory Measurement Model , 2016 .

[13]  C. Ames,et al.  Determinants of Patient Satisfaction 2 Years After Spinal Deformity Surgery: A Latent Class Analysis , 2019, Spine.

[14]  Tanya P. Garcia,et al.  Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington’s Disease as a Model , 2017, Current Neurology and Neuroscience Reports.

[15]  Application of latent class analysis to identify the youth population who risk being cybercrime victim on social networks , 2015 .

[16]  Nilam Ram,et al.  Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups , 2009, International journal of behavioral development.

[17]  M. Furlong,et al.  A latent transition analysis of the longitudinal stability of dual-factor mental health in adolescence. , 2019, Journal of school psychology.

[18]  Kelly Trezise,et al.  Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis , 2017, Learning and Individual Differences.