ScholarGate
어시스턴트

방법 비교

선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.

다층 최대 이질성 표집×층화 표본 추출×
분야조사방법론조사방법론
계열Process / pipelineProcess / pipeline
기원 연도1990s–2000s1977
창시자Synthesized from Patton's maximum variation sampling (1990) and multi-level survey design traditionsWilliam G. Cochran
유형Purposive qualitative/mixed-methods sampling designProbability-based survey sampling design
원전Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Maximum variation sampling and purposeful sampling strategies] ISBN: 978-0761919711Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
별칭hierarchical maximum variation sampling, nested maximum diversity sampling, multi-tier purposive variation sampling, MLMVSProportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
관련52
요약Multi-level maximum variation sampling is a purposive strategy that deliberately selects cases at two or more nested organizational levels — such as schools within districts, or patients within clinics — while maximizing heterogeneity on key dimensions at each level. The aim is to capture the full range of variation within a hierarchically structured population so that patterns common across diverse contexts can be identified and context-specific differences can be documented with credibility.Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.
ScholarGate데이터셋
  1. v1
  2. 2 출처
  3. PUBLISHED
  1. v1
  2. 1 출처
  3. PUBLISHED

검색으로 이동 슬라이드 다운로드

ScholarGate방법 비교: Multi-level Maximum Variation Sampling · Stratified Sampling. 2026-06-15에 다음에서 검색함: https://scholargate.app/ko/compare