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| シミュレーション支援トレンド研究× | トレンド調査× | |
|---|---|---|
| 分野 | 研究デザイン | 研究デザイン |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1990s–2000s (convergence of computational simulation with survey-based trend designs) | Mid-20th century (formalised in social science methodology ~1950s–1960s) |
| 提唱者≠ | Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997) | Earl Babbie and survey research tradition |
| 種類≠ | Quantitative research design with computational augmentation | Quantitative longitudinal research design |
| 原典≠ | Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). SAGE Publications. ISBN: 978-1071817971 | Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage. ISBN: 978-1452226101 |
| 別名 | simulation-augmented trend study, Monte Carlo trend research, computational trend analysis, simulation-based longitudinal trend design | trend study, trend survey, longitudinal trend study, time-series survey |
| 関連 | 4 | 4 |
| 概要≠ | 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. | Trend research is a longitudinal quantitative design that tracks changes in a characteristic of a general population over time by surveying different, independently drawn samples at two or more time points. Unlike panel studies, the same individuals are not followed; rather, each wave draws a fresh sample from the same population, allowing researchers to detect population-level shifts in attitudes, behaviours, or conditions while avoiding the attrition and panel conditioning problems of repeated-measures designs. |
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