Exploratory latent structure analysis using both identifiable and unidentifiable models

Goodman, Leo A.

doi:10.1093/biomet/61.2.215

Cited by 1,371 publications

(766 citation statements)

References 11 publications

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“…The method was introduced by Lazarsfeld (1950). Goodman (1974) made the model applicable in practice by developing an algorithm for obtaining maximum likelihood estimates of the model parameters. The Owen and Videras (2009) study is, to our knowledge, the only application of LCA to the analysis of social capital.…”

Section: Institutional Trustmentioning

confidence: 99%

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Characterization of Social Capital Using a Nested Latent Class Model: Case of Rural Areas in Central Malawi

Dzanja¹

2018

JAS

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Social capital relates to capital created when a group of individuals or organizations develop the ability to work together for mutually productive gain. Gains in economic performance and innovative capacity depend on the institutional effectiveness of these relationships as measured by the available stock of social capital. Studies on social capital have however, been criticized for failing to account for the multi-dimensional and latent nature of the concept. Using household survey data from Malawi, this study uses latent class analytical methods to explore social capital and how it relates to welfare of people in rural communities in Malawi in Africa. It highlights the usefulness of latent class analytical methods for providing statistically valid information about the characteristics and determinants of social capital. Using the social capital dimensions of trust, participation and volunteering a four class LCA typology was constructed. Around 30% of the sample were classified as 'trusty participants', reporting active participation in the socio-economic activities of their communities and a high degree of community and institutional trust. Multinomial logistic regression revealed the covariates of the different typologies of social capital.

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Section: Institutional Trustmentioning

confidence: 99%

“…The specification of the LCA is defined below and is based on the notation from Goodman (1974). First, let us assume that there is a cross-classification table of M = 4 variables, A, B, C and D. Also, assume that variable A can take values i = 1, …, I, variable B can take values j = 1, …, J, and so on.…”

Section: Model Formulation and Data Analysismentioning

confidence: 99%

Characterization of Social Capital Using a Nested Latent Class Model: Case of Rural Areas in Central Malawi

Dzanja¹

2018

JAS

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“…We estimated latent classes of students reflecting particular strategy choice profiles using MLCA, which classifies respondents in latent classes that are each characterized by a particular pattern of response probabilities for a set of problems (Goodman, 1974;Hagenaars & McCutcheon, 2002). For our case, let Y ijk denote the strategy choice of student i of teacher j for item k. A particular strategy choice on item k is denoted by s k .…”

Section: Multilevel Latent Class Analysismentioning

confidence: 99%

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation Between the Curriculum and Students’ Mathematical Strategies

Auer

Hickendorff

Putten

et al. 2016

Applied Measurement in Education

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“…First, latent class analysis (LCA; Collins & Lanza, 2010;Goodman, 1974;Lazarsfeld, 1950) was used to identify and describe susceptibility and ever use risk profiles across school types after accounting for nesting within schools. All LCAs were conducted using PROC LCA Version 1.2.7 (Lanza, Dziak, Huang, Xu, & Collins, 2011) in SAS for Windows (version 9.3; SAS Institute Inc.).…”

Section: Statistical Analysesmentioning

confidence: 99%

Socioeconomic Differences in the Risk Profiles of Susceptibility and Ever Use of Tobacco Among Indian Urban Youth: A Latent Class Approach

Mathur

Stigler

Erickson

et al. 2013

Nicotine & Tobacco Research

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Purpose: To empirically determine the socioeconomic differences in risk profiles of susceptibility and ever use of tobacco among adolescents in India and to investigate the association between the risk profiles and the psychosocial factors for tobacco use.Methods: Students in 16 private (higher socioeconomic status [SES]; n = 4,489) and 16 government (lower SES; n = 7,153) schools in two large cities in India were surveyed about their tobacco use and related psychosocial factors in 2004. Latent class analysis was used to identify homogenous, mutually exclusive typologies existing within the data.results: Overall, 3 and 4 latent classes of susceptibility and ever use of tobacco best described students in higher-and lower-SES schools, respectively. Profiles with various combinations of susceptibility and ever use of tobacco were differentially related to psychosocial factors, with lower-SES students being more vulnerable to increased levels of tobacco use than higher-SES students.Conclusions: Acknowledging the multiple dimensions of tobacco use behaviors and identifying constellations of risk behaviors will enable more accurate understanding of etiological processes and will provide information for refining and targeting preventive interventions. Additionally, identifying the socioeconomic differences in susceptibility and ever use risk profiles and their psychosocial correlates will enable policy makers to address these inequities through improved allocation of resources.

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Exploratory latent structure analysis using both identifiable and unidentifiable models

Cited by 1,371 publications

References 11 publications

Characterization of Social Capital Using a Nested Latent Class Model: Case of Rural Areas in Central Malawi

Characterization of Social Capital Using a Nested Latent Class Model: Case of Rural Areas in Central Malawi

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation Between the Curriculum and Students’ Mathematical Strategies

Socioeconomic Differences in the Risk Profiles of Susceptibility and Ever Use of Tobacco Among Indian Urban Youth: A Latent Class Approach

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