Categorical latent variable modeling utilizing fuzzy clustering generalized structured component analysis as an alternative to latent class analysis

Ryoo, Ji Hoon; Park, Seohee; Kim, Seong‐Eun

doi:10.1007/s41237-019-00084-6

Cited by 7 publications

(17 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gscaLCA algorithm was recently proposed by the authors of [19]. It was developed by combining fuzzy clusterwise GSCA [25] and optimal scaling in GSCA [22], which allows the algorithm to fit latent class analysis (LCA) within a component-based SEM framework.…”

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

“…Prior to the gscaLCA algorithm, the fuzzy clusterwise GSCA was proposed by [22] but limited to the cases where outcome variables are continuous. To extend limited outcome variable type (or continuous outcome variable) in fuzzy clusterwise GSCA to discrete variables, the gscaLCA algorithm [19] was engaged with the optimal scaling technique [26], which is also known as optimal data transformation [22].…”

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

“…The fuzzy clustering updates the individuals' membership based on optimal scaled data for each iteration. Alternating processes of updating the parameters with optimal scaling and fuzzy clustering recursively are applied with the aim of minimizing the residuals of models in the LSE in gscaLCA [19].…”

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

“…To resolve these estimation issues, the authors of [19] proposed an alternative estimation approach for LCA by using fuzzy clusterwise generalized structured component analysis (gscaLCA). Although we provided the conceptual and computational algorithm of gscaLCA, the model used in our previous study was somewhat limited to a simple LCA model focusing only on classifying the number of classes without considering covariates.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

2020

Self Cite

View full text Add to dashboard Cite

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences.

show abstract

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

Section: Generalized Structured Component Analysis For Latent Class Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The fourth paper by Ryoo et al (2020) offers such an extension by combining GSCA with optimal scaling and fuzzy clustering to capture unobserved class-level heterogeneity in the data. The authors test their new approach on real-world data to show that it yields the same results as maximum likelihood-based latent class analysis, while avoiding identification issues.…”

mentioning

confidence: 99%