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| Badania konfirmacyjne oparte na panelach× | Modelowanie równań strukturalnych× | |
|---|---|---|
| Dziedzina≠ | Projektowanie badań | Statystyka w badaniach |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1960s–1980s (formalization of panel methods with confirmatory inference) | 1921 |
| Twórca≠ | Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions | Sewall Wright |
| Typ≠ | Quantitative longitudinal research design | Method |
| Źródło pierwotne≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| Inne nazwy | confirmatory panel design, longitudinal confirmatory study, panel confirmatory analysis, PBCR | SEM, path analysis, latent variable modeling, causal modeling |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Panel-based confirmatory research combines the longitudinal power of panel data — repeated observations of the same units over time — with a pre-specified, hypothesis-driven analytic framework. Instead of exploring patterns post-hoc, the researcher commits to theoretical propositions before data collection and uses the panel structure to test causal or directional claims while controlling for unobserved time-invariant confounders. It is widely used in economics, sociology, epidemiology, and organizational research. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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