Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Иерархическое подтверждающее исследование× | Конфирматорный факторный анализ (КФА)× | |
|---|---|---|
| Область≠ | Дизайн исследования | Психометрия |
| Семейство≠ | Process / pipeline | Latent structure |
| Год появления≠ | 1980s–2000s | 1969 |
| Автор метода≠ | Raudenbush & Bryk; Hox; Goldstein | Karl Gustav Jöreskog |
| Тип≠ | Quantitative confirmatory research design | Hypothesis-testing latent variable model |
| Основополагающий источник≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Другие названия | multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Связанные≠ | 5 | 4 |
| Сводка≠ | Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent. | Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing. |
| ScholarGateНабор данных ↗ |
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