2023
DOI: 10.1037/met0000486
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Ten frequently asked questions about latent transition analysis.

Abstract: Latent transition analysis (LTA), also referred to as latent Markov modeling, is an extension of latent class/profile analysis (LCA/LPA) used to model the interrelations of multiple latent class variables. LTA methods have become increasingly accessible and in-turn are being utilized in applied research. The current article provides an introduction to LTA by answering 10 questions commonly asked by applied researchers. Topics discussed include: (1) an overview of LTA; (2) a comparison of LTA to other longitudi… Show more

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Cited by 79 publications
(35 citation statements)
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“…We used latent transition analysis (LTA) to extract common sleep health phenotypes over time. LTA extends cross-sectional clustering techniques like latent class analysis to a longitudinal context (37). Latent class analysis explores how multiple dimensions of sleep health co-occur within a person and, as a result, potential subgroups within an overall population (i.e., sleep health phenotypes indicated by common within-person patterns of sleep health dimensions), whereas LTA additionally describes how a person’s membership to a subgroup may be stable or change over time.…”
Section: Methodsmentioning
confidence: 99%
“…We used latent transition analysis (LTA) to extract common sleep health phenotypes over time. LTA extends cross-sectional clustering techniques like latent class analysis to a longitudinal context (37). Latent class analysis explores how multiple dimensions of sleep health co-occur within a person and, as a result, potential subgroups within an overall population (i.e., sleep health phenotypes indicated by common within-person patterns of sleep health dimensions), whereas LTA additionally describes how a person’s membership to a subgroup may be stable or change over time.…”
Section: Methodsmentioning
confidence: 99%
“…Latent class analysis (LCA), 6 a classical modelling approach for discrete data developed more than 70 years ago by Lazarsfeld, 7 has been applied to identify a set of discrete, mutually exclusive latent (i.e., unobserved) classes representing exposure profiles based on the observed pattern of a set of 23 dichotomous variables representing occupational exposures (see Table 1). Variables representing occupational tasks were included in two versions: one, if gloves were worn regularly when the task is executed, two, if this was not the case—except for application of deep conditioner where generally no gloves are worn.…”
Section: Methodsmentioning
confidence: 99%
“…While we are interested in how adversity changes over time, and therefore expect the construct of adversity to be noninvariant over time by design, using an RI-LTA model could address some concerns about measurement noninvariance by accounting for between-individual variation in response probabilities for a given latent class indicator. Furthermore, recent work has indicated that longitudinal measurement invariance is not required for LTA models unless the assumption (or imposition) is that latent classes will be similar over time (Nylund-Gibson et al, 2023).…”
Section: Current Studymentioning
confidence: 99%