Latent class and latent transition analysis

By Stephanie T. Lanza, B. Bray, Linda M. Collins. 2009

1,018 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a5be75bf258405f88178c308683a39f51110cbfe

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In Computer Networks and Communications

In Experimental and Cognitive Psychology

In Statistics and Probability

Citations in the corpus

Latent Class Analysis (2021, background): “It is also referred to as latent transition model (Collins & Lanza, 2010), hidden Markov model (MacDonald & Zucchini, 1997; Visser, 2011), Markov switching or regime-switching model.”
Relationships between 9-Year-Olds' Math and Literacy Worries and Academic Abilities (2012, methodology): “Because of the relatively large math and literacy worry SD’s, two separate latent profile analysis [39] were employed to identify possible worry subgroups in the two domains.”
Mixture Models: Latent Profile and Latent Class Analysis (2016, background): “A comprehensive introduction-level textbook is Collins and Lanza (2013).”
Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (2016, background): “It is also referred to as latent transition model (Collins and Lanza, 2010; Collins and Wugalter, 1992), hidden Markov model (MacDonald and Zucchini, 1997; Visser, 2011), Markovswitching (Frühwirth-Schnatter, 2009), or regime-switching model (Hamilton, 2008).”
Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “Before considering the distal outcomes, we examined several models of depression with different numbers of latent classes to confirm that the five-class model reported by Lanza et al. (2003) was optimal for this particular sample.”
Sensitivity and Specificity of Information Criteria (2018, neutral): “We then discuss their application to a common application of model selection in medical, health and social scientific applications: that of choosing the number of classes in a latent class analysis (e.g., Collins and Lanza, 2010).”
Introduction to Latent Class Analysis With Applications (2017, methodology): “Lanza et al. (2003) used eight binary indicators of adolescent depression to group respondents into five classes of depressed early adolescents.”
Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification (2015, background): “For all information-based criteria, comparatively lower values indicate better fitting models (Muthén & Muthén, 2010) although all criteria might not be minimized for the same model (Collins & Lanza, 2010).”