Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pētījumi ar salīdzinošu modeļu testēšanu× | Pētījumi modeļu testēšanai× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1969–2000s | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s |
| Autors≠ | Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000) | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| Tips≠ | Quantitative confirmatory-comparative research design | Confirmatory quantitative research design |
| Pirmavots | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 |
| Citi nosaukumi | comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation | model-based research, structural model testing, theory-testing research, MTR |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups. | 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. |
| ScholarGateDatu kopa ↗ |
|
|