So sánh phương pháp

	Nghiên cứu xu hướng có hỗ trợ mô phỏng	Nghiên cứu xu hướng
Lĩnh vực	Thiết kế nghiên cứu	Thiết kế nghiên cứu
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s (convergence of computational simulation with survey-based trend designs)	Mid-20th century (formalised in social science methodology ~1950s–1960s)
Người khởi xướng	Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)	Earl Babbie and survey research tradition
Loại	Quantitative research design with computational augmentation	Quantitative longitudinal research design
Công trình gốc	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
Tên gọi khác	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
Liên quan	4	4
Tóm tắt	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.