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| Badania trendów wspomagane symulacją× | Badania panelowe× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s (convergence of computational simulation with survey-based trend designs) | 1970s-1980s (econometric formalization); earlier social survey use from 1940s |
| Twórca≠ | Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997) | Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s |
| Typ≠ | Quantitative research design with computational augmentation | Quantitative longitudinal observational design |
| Źródło pierwotne≠ | Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). SAGE Publications. ISBN: 978-1071817971 | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 |
| Inne nazwy | simulation-augmented trend study, Monte Carlo trend research, computational trend analysis, simulation-based longitudinal trend design | panel study, panel survey, longitudinal panel, repeated-measures panel |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Simulation-assisted trend research combines repeated cross-sectional survey data collected at multiple time points with computational simulation techniques — such as Monte Carlo methods or agent-based modeling — to project, validate, and stress-test observed trends. It extends classic trend research by replacing or supplementing extrapolation with probabilistic scenario modeling, allowing researchers to quantify uncertainty around trend trajectories and explore counterfactual futures under varying assumptions. | Panel research is a quantitative longitudinal design in which the same individuals, organizations, or other units are measured repeatedly across two or more time points. Unlike cross-sectional surveys that capture a single snapshot, a panel tracks change within units, enabling researchers to separate genuine within-unit change from between-unit differences and to model causal dynamics over time. |
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