Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Продольная теория отклика на задания (LIRT)× | Продольный конфирматорный факторный анализ× | |
|---|---|---|
| Область | Психометрия | Психометрия |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 1991 | 1970s–1990s |
| Автор метода≠ | Susan E. Embretson | Karl Jöreskog (CFA framework); longitudinal extension by Wheaton, Muthén, and Alwin in the 1970s–1990s |
| Тип≠ | Latent trait / longitudinal psychometric model | Longitudinal latent variable / measurement model |
| Основополагающий источник≠ | Embretson, S. E. (1991). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56(3), 495–515. DOI ↗ | Widaman, K. F. & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association. link ↗ |
| Другие названия | LIRT, longitudinal IRT, repeated-measures IRT, dynamic item response modeling | longitudinal CFA, repeated-measures CFA, longitudinal measurement model, panel CFA |
| Связанные≠ | 4 | 6 |
| Сводка≠ | Longitudinal IRT extends classical item response theory to data collected at multiple time points, allowing researchers to model both the initial latent trait level and its change over time. It is used in educational assessment, clinical trials, and panel studies where the same items or item banks are administered repeatedly to the same individuals. | Longitudinal confirmatory factor analysis (longitudinal CFA) applies a theoretically specified measurement model to data collected at two or more time points. Its primary purpose is to verify that a scale measures the same latent construct in the same way over time — a prerequisite for drawing valid conclusions about change from repeated-measures data. |
| ScholarGateНабор данных ↗ |
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