Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays

By Jay Magidson, Jeroen K. Vermunt. 2001

325 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Food Science

View Paper: https://www.semanticscholar.org/paper/6bf1b9f6cfca118e7667509eb328e94ab93913c6

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Citations in the corpus

Latent Variable Modelling: A Survey * (2007, background): “Magidson & Vermunt (2001) discuss latent class factor models with several independent binary latent variables.”
Building an identifiable latent class model with covariate effects on underlying and measured variables (2004, methodology): “In fact, an unconstrained LC model with three dichotomous latent variables can be reparameterized as a single-latent-variable LC model with 23 = 8 classes ( Magidson and Vermunt, 2001 ).”
Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (2016, methodology): “Thus, rather than working with a single nominal latent variable, here we work with one or more dichotomous or ordered polytomous latent variables (Magidson and Vermunt, 2001; Vermunt and Magidson, 2005a).”
Researching Measurement Equivalence in Cross-Cultural Studies (2010, background): “A third, less well-known but promising approach that combines multiple-group latent class analysis (Clogg & Goodman, 1984; McCutcheon, 2002) with latent class factor analysis (LCFA; Magidson & Vermunt, 2001) has been recently introduced (Moors, 2004; Kankaraš & Moors, 2009).”
A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis (2011, methodology): “For a detailed comparison of cluster analyses and LPA or related models, the interested reader is referred to Magidson and Vermunt (2002). Unfortunately, to our knowledge, mixture models have still not been applied to the study of WAC.”
Latent Class Analysis (2021, methodology): “DFactor models are restricted LC cluster models where ordinality restrictions are imposed on each DFactor (Magidson & Vermunt, 2001).”
Introduction to Latent Class Analysis With Applications (2017, methodology): “An alternative is to specify a non-traditional LC model; that is, an LC factor model to include more than one LV. LC factor models were proposed as a general alternative to the traditional exploratory LC modeling (see Magidson & Vermunt, 2001, 2004, for more details of LC factor models).”
1. On the Assignment of Individuals to Latent Classes (2007, background): “(For views of other mixture models, and some extensions of latent class models, see, e.g., MacLachlan and Peel 2000; Magidson and Vermunt 2001.)”
Latent Class Models (2004, unknown): “LC factor models were proposed as a general alternative to the traditional exploratory LC modeling by Magidson and Vermunt (2001).”
A person‐centered approach to commitment research: Theory, research, and methodology (2016, methodology): “This suggests the use of auxiliary approaches, such as the AUXILIARY (e) implemented in MPLUS (also see Magidson & Vermunt, 2001).”
Latent class models for clustering : a comparison with K-means (2002, methodology): “These LC factor models have been found to outperform the traditional LC cluster models in several applications (Magidson & Vermunt 2001).”
Trust in the Capability of the Welfare State and General Welfare State Support: Sweden 1997-2002 (2006, methodology): “The software used for estimation of the latent variables is Latent GOLD (Vermunt and Magidson, 2000; Magidson and Vermunt, 2001).”
Factor Analysis with Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches (2005, methodology): “An LCFA model with L dichotomous latent variables is, actually, a restricted LC model with 2 L latent classes (Magidson & Vermunt, 2001) .”