Sampling Methods and the Precision of Data Estimates: A Systematic Review of Recent Methodological Developments, 2000– 2026

Abstract

Sampling is an area of empirical research since it defines to what extent the chosen cases can be used to provide valid estimates of a target population. The recent methodological studies indicate that the precision cannot be deteriorated down to the level of the sample size only. The sampling frame, selection process, allocation plan, response pattern, data collection mode, nonresponse adjustment, data imputation, weighting plan, process of variance estimation, and transparency in reporting are all aspects that shape precision. This systematic review brings together the recent literature on the sampling methods and the accuracy of the data estimates within the timeframe of 2000 to 2026. The review is based on a PRISMA-based screening framework and a review log of 250 records initially identified, then 212 records screened based on title and abstract, 161 papers that have been assessed based on the full-text, and 68 records contained in the final synthesis. The findings reveal that probability sampling is the most robust ground on which population inferences can be drawn but its accuracy is subject to the quality of the sampling frame as well as the adequate consideration of the design effects, thenonresponse bias, missing data and the ratio. Nonprobability sampling is growing in both digital and applied spaces, but it demands direct modification using auxiliary information, pseudo-weighting, calibration, data integration, or model assisted inferencing. More recent developments around the estimation of small areas, Bayesian models, geospatial sampling constructs, mixed-method surveys, and margin of total error have shifted focus of the field to a broader conception of precision as a composite product of design quality and error control. It is concluded that in future sampling studies, the authors should be more open about their use of selection methods, frame building, including rules of eligibility, response procedures, weighting choices, missing data practices and uncertainty estimation.