Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis
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 an...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/11/2076 |
| _version_ | 1827701811415351296 |
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| author | Seohee Park Seongeun Kim Ji Hoon Ryoo |
| author_facet | Seohee Park Seongeun Kim Ji Hoon Ryoo |
| author_sort | Seohee Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:41:40Z |
| format | Article |
| id | doaj.art-1b3a3c5232774e5698bb84d8494cd700 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T14:41:40Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-1b3a3c5232774e5698bb84d8494cd7002023-11-20T21:43:48ZengMDPI AGMathematics2227-73902020-11-01811207610.3390/math8112076Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component AnalysisSeohee Park0Seongeun Kim1Ji Hoon Ryoo2Department of Psychological and Quantitative Foundations, College of Education, University of Iowa, Iowa City, IA 52242, USADepartment of Educational Research Methodology, School of Education, University of North Carolina at Greensboro, Greensboro, NC 27412, USADepartment of Education, College of Educational Sciences, Yonsei University, Seoul 03722, KoreaLatent 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.https://www.mdpi.com/2227-7390/8/11/2076fuzzy clusteringgeneralized structured component analysislatent class regressionthree-step approach |
| spellingShingle | Seohee Park Seongeun Kim Ji Hoon Ryoo Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis Mathematics fuzzy clustering generalized structured component analysis latent class regression three-step approach |
| title | Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis |
| title_full | Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis |
| title_fullStr | Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis |
| title_full_unstemmed | Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis |
| title_short | Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis |
| title_sort | latent class regression utilizing fuzzy clusterwise generalized structured component analysis |
| topic | fuzzy clustering generalized structured component analysis latent class regression three-step approach |
| url | https://www.mdpi.com/2227-7390/8/11/2076 |
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