Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дослідження з тестування ієрархічних моделей× | Конфірмаційне дослідження× | |
|---|---|---|
| Галузь | Дизайн дослідження | Дизайн дослідження |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1934 (Popper); widely adopted in social sciences from 1960s onward |
| Автор методу≠ | Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen | Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others |
| Тип≠ | Quantitative confirmatory research design | Quantitative research design |
| Основоположне джерело≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447 |
| Інші назви | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | hypothesis-testing research, deductive research, theory-testing research, confirmatory study |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping. | Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry. |
| ScholarGateНабір даних ↗ |
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