เปรียบเทียบวิธี

	Bayesian Observational Quantitative Research	การจับคู่คะแนนแนวโน้ม
สาขาวิชา	การออกแบบการวิจัย	สถิติการวิจัย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1990s–2000s (systematic application to observational research)	1983
ผู้ริเริ่ม	Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s)	Paul Rosenbaum and Donald Rubin
ประเภท	Quantitative non-experimental research design with Bayesian inference	Method
แหล่งต้นตำรับ	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
ชื่อเรียกอื่น	Bayesian observational study, Bayesian non-experimental quantitative design, Bayesian causal observational analysis, BOQR	PSM, propensity score weighting, covariate balance
ที่เกี่ยวข้อง	4	3
สรุป	Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.