So sánh phương pháp

	Nghiên cứu xu hướng có hỗ trợ mô phỏng	Nghiên cứu Dài hạn
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)	Late 19th–early 20th century; methodologically codified through the 20th century
Người khởi xướng	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
Loại	Quantitative research design with computational augmentation	Quantitative (or mixed) observational 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	Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841
Tên gọi khác	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
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.	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.