Latent Class Cluster Analysis

By Jeroen K. Vermunt, Jay Magidson. Encyclopedia of Gerontology and Population Aging, 2021

960 citations recorded by the source corpus.

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Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/04b61ba38b4e74a5ab92bb106bffe7fd85406fb3

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Jeroen K. Vermunt

Citations in the corpus

Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, background): “There are other fit indices and metrics that have been recommended, and these may be included as the researcher feels is appropriate and/or as is common in a particular field’s literature (Marsh et al., 2009; Masyn, 2013; Nylund et al., 2007; Tein et al., 2013; Vermunt & Magidson, 2002).”
Measuring consumers attitudes towards health and taste and their association with food-related life-styles and preferences (2019, methodology): “LCA is a model-based clustering approach outperforming traditional cluster analysis (e.g. K-means) (Magidson & Vermunt, 2002; Vermunt & Magidson, 2002).”
Task-values across subject domains: A gender comparison using a person-centered approach (2011, background): “Latent class analysis is a statistical procedure which estimates the number of latent homogeneous classes in a heterogeneous sample based on the pattern of observed responses (Vermunt & Magidson, 2002).”
Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, methodology): “…into clusters based upon membership probabilities estimated directly from the model; (b) variables may be continuous, categorical (nominal or ordinal), counts, or any combination of these; and (c) demographics and other covariates can be used for profile description (Magidson & Vermunt, 2002).”
Latent Class Analysis (2021, background): “One of these is as a probabilistic cluster analysis tool for continuous observed variables, an approach that offers many advantages over traditional cluster techniques such as K-means clustering (Wolfe, 1970; McLachlan and Peel, 2000; Vermunt and Magidson, 2002).”
Measuring bullying at work with the short-negative acts questionnaire: identification of targets and criterion validity (2019, background): “First LC analysis does not depend strongly upon distributional assumptions (Magidson & Vermunt, 2002; Vermunt & Magidson, 2002).”
Consensus in multi-agent systems: a review (2021, neutral): “Each group is called a cluster (Vermunt and Magidson 2002).”