Finite mixture model used to identify underlying (latent) subgroups within a population based on individuals’ responses to multiple observed variables. Factor analysis is based on continuous latent variables, whereas LCA is based on categorical latent variables.
Observed variable used in a measurement model such as LTA to measure a latent variable (often referred to as an item). For example, if the latent variable is “teen delinquency,” the indicators might include shoplifting, lying to parents, property damage, and carrying a gun.
Latent transition analysis (LTA) and latent class analysis (LCA) are closely related methods. LCA identifies unobservable (latent) subgroups within a population based on individuals’ responses to multiple observed variables. LTA is an extension of LCA that uses longitudinal data to identify movement between the subgroups over time.
Our research on LTA allows scientists to better understand the impact of exposure to multiple risks and the nature of changes in complex behaviors, particularly when change can be represented as movement between discrete categories or stages. This makes it possible to identify groups of at-risk individuals. We also distribute PROC LTA, a free, easy-to-use SAS procedure for LTA.
Introductory Example: Changes in Teen Sexual Risk
In this example, LTA reveals five profiles of teen/young adult sexual behavior. The analyses indicate which youth are more likely to transition to higher risk behavior over the course of two years. By applying LTA, interventions can be developed to target youth most at risk of initiating high-risk sexual behavior.