Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Testarea modelelor ierarhice× | Cercetarea testării modelului longitudinal× | |
|---|---|---|
| Domeniu | Design de cercetare | Design de cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s) |
| Autorul original≠ | Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen | Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett) |
| Tip≠ | Quantitative confirmatory research design | Quantitative, confirmatory, longitudinal design |
| Sursa seminală≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 |
| Denumiri alternative | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | 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. | Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables. |
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