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| Badania trendów wspomagane symulacją× | Badania podłużne× | |
|---|---|---|
| Dziedzina | Projektowanie badań | Projektowanie badań |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s (convergence of computational simulation with survey-based trend designs) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Twórca≠ | Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Typ≠ | Quantitative research design with computational augmentation | Quantitative (or mixed) observational research 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 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Inne nazwy | simulation-augmented trend study, Monte Carlo trend research, computational trend analysis, simulation-based longitudinal trend design | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Pokrewne | 4 | 4 |
| 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. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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