विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| सिमुलेशन-सहायता प्राप्त क्रॉस-सेक्शनल अनुसंधान× | सर्वेक्षण अनुसंधान× | |
|---|---|---|
| क्षेत्र | अनुसंधान अभिकल्प | अनुसंधान अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2000s–2010s (consolidated as a named hybrid approach) | Late 19th century; methodologically systematised 1940s–1960s |
| प्रवर्तक≠ | Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues) | Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s |
| प्रकार≠ | Quantitative hybrid research design | Quantitative (and mixed) non-experimental design |
| मौलिक स्रोत≠ | Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397. DOI ↗ | Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications. ISBN: 978-1452259000 |
| उपनाम | simulation-enhanced cross-sectional study, hybrid simulation cross-sectional design, cross-sectional simulation study, SACSR | survey methodology, questionnaire research, survey design, survey study |
| संबंधित≠ | 3 | 4 |
| सारांश≠ | Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional data calibrate the simulation, which then explores counterfactuals, rare subgroups, or dynamic processes that the survey alone cannot reveal. | Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estimating prevalence, mapping attitude distributions, and testing bivariate or multivariate associations across social, behavioural, and health sciences. |
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