Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pētījumi ar salīdzinošu modeļu testēšanu× | Apstiprinošā pētniecība× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1969–2000s | 1934 (Popper); widely adopted in social sciences from 1960s onward |
| Autors≠ | Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000) | Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others |
| Tips≠ | Quantitative confirmatory-comparative research design | Quantitative research design |
| Pirmavots≠ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447 |
| Citi nosaukumi | comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation | hypothesis-testing research, deductive research, theory-testing research, confirmatory study |
| Saistītās | 4 | 4 |
| 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. | 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. |
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