Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Латентний профілактичний аналіз (LPA)× | Аналіз латентних класів (LCA)× | |
|---|---|---|
| Галузь≠ | Психометрія | Статистика |
| Родина | Latent structure | Latent structure |
| Рік появи≠ | 2010 | 1950s–1968 |
| Автор методу≠ | Lazarsfeld & Henry; Collins & Lanza | Paul F. Lazarsfeld |
| Тип≠ | Person-centered finite mixture model | Latent variable / person-centered classification |
| Основоположне джерело≠ | Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7 | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗ |
| Інші назви | Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi | LCA, latent class model, latent categorical analysis, finite mixture of multinomials |
| Пов'язані≠ | 2 | 6 |
| Підсумок≠ | Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile. | Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data. |
| ScholarGateНабір даних ↗ |
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