Latent Class Models

Magidson, Jay; Vermunt, Jeroen K.

doi:10.4135/9781412986311.n10

Cited by 458 publications

(423 citation statements)

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“…Additional covariates were not included in the LCA. To determine the best-fi tting model, Bayesian Information Criteria (BIC) and class error indices were used (Magidson and Vermunt, 2004). Two sets of parameters are primarily of interest when conducting LCA: (a) the probability of being in each subgroup (or latent class), which also provides estimates of the prevalence of latent class membership, and (b) conditional response probabilities, or the probability of a particular response given membership in a specifi ed class.…”

Section: Statistical Analysesmentioning

confidence: 99%

Patterns of Alcohol-Dependence Symptoms Using a Latent Empirical Approach: Associations With Treatment Usage and Other Correlates

Ko¹,

Martins²,

Kuramoto

et al. 2010

J. Stud. Alcohol Drugs

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ABSTRACT. Objective: The aim of this study was to understand the variation in response to alcohol use by identifying classes of alcohol users based on alcohol-dependence symptoms and to compare these classes across demographic characteristics, abuse symptoms, and treatment usage. Method: Data from combined 2002-2005 National Survey on Drug Use and Health identifi ed 110,742 past-year alcohol users, age 18 years or older. Latent class analysis defi ned classes based on observed clustering of alcohol-dependence symptoms based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Chi-square statistics were used to test differences in sociodemographic and alcohol-abuse characteristics across classes. Multivariable latent class regressions were used to compare treatment usage across classes. Results: The four-class model had the best overall fi t and identifi ed classes that differed quantitatively and qualitatively, with 2.3% of the users in the most-severe class and 83.8% in the least-severe/ not-affected class. These classes differed in a number of demographic characteristics and alcohol-abuse symptoms. All individuals in the mostsevere class met DSM-IV criteria for alcohol dependence; 80% of this class had alcohol-abuse symptoms. Twenty-six percent of the moderate and 50% of the moderate-high class met dependence criteria. Approximately 19% of the most-severe class and less than 5% of the moderate and moderate-high class received treatment for alcohol in the past year. Conclusions: This study demonstrates that meeting dependence criteria only partially captures variations in responses to severity of alcohol problems. Although individuals in the most-severe class were more likely to perceive need and receive treatment, the percentage of individuals receiving treatment was low. (J. Stud. Alcohol Drugs, 71, 870-878, 2010)

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Section: Statistical Analysesmentioning

confidence: 99%

Patterns of Alcohol-Dependence Symptoms Using a Latent Empirical Approach: Associations With Treatment Usage and Other Correlates

Ko¹,

Martins²,

Kuramoto

et al. 2010

J. Stud. Alcohol Drugs

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show abstract

“…This model had been used in earlier research with crowdsourced data ) and its general basis is re-stated here with further discussion provided in the literature (e.g. Rindskopf and Rindskopf 1986;Yang and Becker 1997;Vermunt andMagidson 2003, Magidson andVermunt 2004). The latent class model is based on the probability of observing the patterns of class allocation made by the set of contributors to the crowdsourcing project.…”

Section: Latent Class Modelmentioning

confidence: 99%

“…M denotes (M 1 ,., M c ) and m denotes (m 1 ,. m q )), the latent class model is that the probability of obtaining the response pattern m, represented as P(M = m), is a weighted average of the q class-specific probabilities P(M = m|T = t) (Magidson and Vermunt 2004). On the assumption that the labels derived from each contributor are conditionally independent of those from all others, the latent class model may be written as:…”

Section: Latent Class Modelmentioning

confidence: 99%

“…The model is normally viewed as providing a good fit if the L 2 value obtained is sufficiently small to be attributable to the effect of chance (i.e. p > 0.05; Magidson and Vermunt 2004). The only parameters of the latent class model in equation (2) are the latent class probabilities which provide useful information on the variable under investigation as well as of the quality of the contributing data sources.…”

Section: Latent Class Modelmentioning

confidence: 99%

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Rating crowdsourced annotations: evaluating contributions of variable quality and completeness

Foody

2013

International Journal of Digital Earth

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Crowdsourcing has become a popular means to acquire data about the Earth and its environment inexpensively, but the data-sets obtained are typically imperfect and of unknown quality. Two common imperfections with crowdsourced data are the contributions from cheats or spammers and missing cases. The effect of the latter two imperfections on a method to evaluate the accuracy of crowdsourced data via a latent class model was explored. Using simulated and real data-sets, it was shown that the method is able to derive useful information on the accuracy of crowdsourced data even when the degree of imperfection was very high. The practical potential of this ability to obtain accuracy information within the geospatial sciences and the realm of Digital Earth applications was indicated with reference to an evaluation of building damage maps produced by multiple bodies after the 2010 earthquake in Haiti. Critically, the method allowed data-sets to be ranked in approximately the correct order of accuracy and this could help ensure that the most appropriate data-sets are used.

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“…Through the segmentation, those specific factors that group together variables with the same common source of variation can be identified. 10 The overarching segments, in turn, are attractive from a popular point of view. customer base.…”

Section: Segmenting Consumers and The Problem Of Direct Applicabilitymentioning

confidence: 99%

Deconstructing automobile messaging: Clues to brand strategy

Moskowitz

Itty²,

Fink³

et al. 2003

J Database Mark Cust Strategy Manag

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Latent Class Models

Cited by 458 publications

References 0 publications

Patterns of Alcohol-Dependence Symptoms Using a Latent Empirical Approach: Associations With Treatment Usage and Other Correlates

Patterns of Alcohol-Dependence Symptoms Using a Latent Empirical Approach: Associations With Treatment Usage and Other Correlates

Rating crowdsourced annotations: evaluating contributions of variable quality and completeness

Deconstructing automobile messaging: Clues to brand strategy

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