Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Ricerca per test di modelli gerarchici× | Test di Modelli Longitudinali× | |
|---|---|---|
| Campo | Disegno della ricerca | Disegno della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s) |
| Ideatore≠ | 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) |
| Tipo≠ | Quantitative confirmatory research design | Quantitative, confirmatory, longitudinal design |
| Fonte seminale≠ | 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 |
| Alias | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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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