方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 基于场地的最大变异抽样× | 基于现场的分层抽样× | |
|---|---|---|
| 领域 | 调查方法论 | 调查方法论 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s | 1934 (Neyman's stratified sampling theory); field applications throughout 20th century |
| 提出者≠ | Michael Quinn Patton (maximum variation sampling); adapted for field research contexts | Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice |
| 类型≠ | Purposive qualitative/mixed-methods sampling strategy | Probability sampling design |
| 开创性文献≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Maximum variation sampling discussed in Chapter 5] ISBN: 978-0761919711 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| 别名 | field MVS, field-based purposeful maximum variation, maximum heterogeneity field sampling, diverse case field sampling | field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification |
| 相关 | 6 | 6 |
| 摘要≠ | Field-based maximum variation sampling is a purposive strategy in which a researcher deliberately selects field sites, ecological plots, communities, or observational units that span the widest possible range of relevant characteristics. By maximising heterogeneity among selected units, the approach ensures that both common patterns shared across diverse conditions and unique features specific to particular contexts are documented, making findings robust across a broad spectrum of real-world variation. | Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set. |
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