Linganisha mbinu

	Sampuli ya Mabadiliko ya Juu Kulingana na Uga	Uchaguzi wa Sampuli za Nguzo Zinazotegemea Uga
Nyanja	Metodolojia ya Dodoso	Metodolojia ya Dodoso
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s	1950s (theory); 1970s–1980s (field survey practice)
Mwanzilishi	Michael Quinn Patton (maximum variation sampling); adapted for field research contexts	William G. Cochran (theoretical foundations); WHO EPI programme (field application)
Aina	Purposive qualitative/mixed-methods sampling strategy	Probability sampling design
Chanzo asilia	Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Maximum variation sampling discussed in Chapter 5] ISBN: 978-0761919711	World Health Organization. (1991). Training for mid-level managers: The EPI coverage survey. WHO/EPI/MLM/91.10. World Health Organization.
Majina mbadala	field MVS, field-based purposeful maximum variation, maximum heterogeneity field sampling, diverse case field sampling	field cluster sampling, in-field cluster sampling, area cluster sampling (field), field survey cluster design
Zinazohusiana	6	6
Muhtasari	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 cluster sampling is a probability sampling method in which naturally occurring geographic or administrative groups (clusters) are first randomly selected, and then data are collected in person from units within those clusters. It is the standard design for large-scale field surveys in public health, agriculture, education, and humanitarian response, where compiling a full population list is impractical but clusters such as villages, schools, or census tracts can be identified and physically accessed.