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Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables
Multivariate Behavioral Research, 2020Research on stage-sequential shifts across multiple latent classes can be challenging in part because it may not be possible to observe the particular stage-sequential pattern of a single latent class variable directly. In addition, one latent class variable may affect or be affected by other latent class variables and the associations among multiple ...
Saebom, Jeon +3 more
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2020
Latent class (LC) analysis is a widely used method for extracting meaningful groups (LCs) from data. The basic concept was introduced by Paul Lazarsfeld in 1950 for building typologies (or clusters) from dichotomous variables as part of his more general latent structure analysis.
Magidson, Jay +2 more
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Latent class (LC) analysis is a widely used method for extracting meaningful groups (LCs) from data. The basic concept was introduced by Paul Lazarsfeld in 1950 for building typologies (or clusters) from dichotomous variables as part of his more general latent structure analysis.
Magidson, Jay +2 more
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Biometrics, 2000
Summary.In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques ...
Garrett, Elizabeth S., Zeger, Scott L.
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Summary.In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques ...
Garrett, Elizabeth S., Zeger, Scott L.
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Latent class analysis with ordered latent classe
British Journal of Mathematical and Statistical Psychology, 1990In this paper a latent class model is described in which the latent classes are ordered imposing inequality constraints on item response and cumulative response probabilities from subsequent latent classes. These inequality constraints are derived from the basic assumption that, when the latent classes may be ordered from low to high along the latent ...
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Advances in Data Analysis and Classification, 2013
The paper proposes a latent class version of Combination of Uniform and (shifted) Binomial random variables ( CUB ) models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB , is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal ...
Leonardo Grilli +3 more
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The paper proposes a latent class version of Combination of Uniform and (shifted) Binomial random variables ( CUB ) models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB , is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal ...
Leonardo Grilli +3 more
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2010
A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This
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A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This
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Multivariate Behavioral Research, 2014
The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data.
Hsiu-Ting, Yu, Jungkyu, Park
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The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data.
Hsiu-Ting, Yu, Jungkyu, Park
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Dirichlet Generalizations of Latent-Class Models
Journal of Classification, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Richard F. Potthoff +2 more
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Bootstrapping Latent Class Models
2005This paper deals with improved measures of statistical accuracy for parameter estimates of latent class models. It introduces more precise confidence intervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced.
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2018
Latent class analysis (LCA) is a statistical method for identifying unobserved groups based on patterns of categorical data. LCA is related to cluster analysis (see Chapter 4, this volume) in that both methods are concerned with the classification of cases (e.g., people or objects) into groups that are not known or specified a priori.
Karen M. Samuelsen, C. Mitchell Dayton
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Latent class analysis (LCA) is a statistical method for identifying unobserved groups based on patterns of categorical data. LCA is related to cluster analysis (see Chapter 4, this volume) in that both methods are concerned with the classification of cases (e.g., people or objects) into groups that are not known or specified a priori.
Karen M. Samuelsen, C. Mitchell Dayton
openaire +1 more source