"Characterizing Changing Classifications: Practical Illustrations of Latent Transition Analysis"

18 Mar 2012    

"Characterizing Changing Classifications: Practical Illustrations of Latent Transition Analysis"
March 30, 11:30 a.m. to 1:00 p.m.
242 Mabel Lee Hall

CYFS presenters:
Ji Hoon Ryoo, postdoctoral fellow
Chaorong Wu, statistics and measurement consultant
Carina McCormick, statistics and measurement consultant

Sometimes true latent individual differences are categorical or qualitative rather than continuous. Likewise, change in measured category memberships over time may reflect transitions between latent classes rather than change in levels. Additional caveats may include the presence of unmeasured heterogeneity, or subgroups, among participants, or substantial measurement error leading to incorrect observed classifications. Latent transition analysis (LTA) is a latent variable modeling approach that enables the researcher to estimate the prevalence and degree of transitioning between latent class memberships over time. In contrast to a latent growth curve approach, which models change over an entire developmental period, LTA allows modeling of period-to-period change in status. This talk will demonstrate LTA with two empirical data sets, exploring change in psychological status and change in reading proficiency designation.