Recommendations on the Sample Sizes for Multilevel Latent Class Models
Dados Bibliográficos
| ID | 20282971 |
|---|---|
| Autores | Jungkyu Park (0000-0002-8320-576X, McGill University, Montreal, Quebec, Canada), Hsiu-Ting Yu (National Chengchi University, Taipei, Taiwan), Hsiu‐Ting Yu (0000-0002-3668-8033, National Chengchi University, autor correspondente) |
| Ano | 2018 |
| Volume | 78 |
| Fascículo | 5 |
| Páginas | 737-761 |
| Data de publicação | 2018-10-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Educational and Psychological Measurement (JOURNAL) |
| Identificadores do periódico | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Editora | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164417719111 |
| PMID | 32655168 |
| OpenAlex | W2758319537 |
| Idioma | EN |
| Citações recebidas | 27 |
| Referências citadas | 59 |
A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis
Algorithm · Data mining · Econometrics · Latent class model · Model selection · Nested set model · Rule of thumb · Sample (material) · Sample size determination · Statistics · Bayesian Methods and Mixture Models · Computer Science · Data Analysis with R · Mathematics · Statistical Methods and Bayesian Inference
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| Obras citantes distintas | 27 |
|---|---|
| Citações por ano | 3,38 |
| Intervalo de citações | 2018 - 2026 (9) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 23 |