مقایسهٔ روش‌ها

	پژوهش روند با کمک شبیه‌سازی	پژوهش روند
حوزه	طراحی پژوهش	طراحی پژوهش
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	1990s–2000s (convergence of computational simulation with survey-based trend designs)	Mid-20th century (formalised in social science methodology ~1950s–1960s)
پدیدآور	Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)	Earl Babbie and survey research tradition
نوع	Quantitative research design with computational augmentation	Quantitative longitudinal research design
منبع بنیادین	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
نام‌های دیگر	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
مرتبط	4	4
خلاصه	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.