পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সিমুলেশন-সহায়ক প্রবণতা গবেষণা× | দীর্ঘস্থায়ী গবেষণা× | |
|---|---|---|
| ক্ষেত্র | গবেষণা নকশা | গবেষণা নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1990s–2000s (convergence of computational simulation with survey-based trend designs) | Late 19th–early 20th century; methodologically codified through the 20th century |
| প্রবর্তক≠ | 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 |
| ধরন≠ | Quantitative research design with computational augmentation | Quantitative (or mixed) observational research design |
| মৌলিক উৎস≠ | 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 |
| অপর নাম | 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 |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | 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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