विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| अनुदैर्ध्य मॉडल परीक्षण अनुसंधान× | मॉडल परीक्षण अनुसंधान× | |
|---|---|---|
| क्षेत्र | अनुसंधान अभिकल्प | अनुसंधान अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 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 |
| प्रवर्तक≠ | Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett) | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| प्रकार≠ | Quantitative, confirmatory, longitudinal design | Confirmatory quantitative research design |
| मौलिक स्रोत≠ | 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 |
| उपनाम | longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling | model-based research, structural model testing, theory-testing research, MTR |
| संबंधित≠ | 6 | 5 |
| सारांश≠ | 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. |
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