Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pētījumi par hierarhisko modeļu testēšanu× | Pētījumi modeļu testēšanai× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994) | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s |
| Autors≠ | Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| Tips≠ | Quantitative confirmatory research design | Confirmatory quantitative research design |
| Pirmavots≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 |
| Citi nosaukumi | multilevel model testing, hierarchical SEM, nested model testing, HLM model testing | model-based research, structural model testing, theory-testing research, MTR |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence. |
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