Сравнение на методи
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| Изследване на тестване на лонгитюдни модели× | Изследване за тестване на модели× | |
|---|---|---|
| Област | Дизайн на изследването | Дизайн на изследването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s) | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s |
| Създател≠ | Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett) | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| Тип≠ | Quantitative, confirmatory, longitudinal design | Confirmatory quantitative research design |
| Основополагащ източник≠ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 |
| Други названия | longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling | model-based research, structural model testing, theory-testing research, MTR |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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