手法を比較

	Anthropological Household Survey	Behavioral Observation Coding
分野	Anthropology	Anthropology
系統	Process / pipeline	Process / pipeline
提唱年	2017	1974
提唱者	Survey-research tradition adapted to community fieldwork (Bernard)	Behavioral sampling tradition (Altmann), adapted to anthropology (Bernard)
種類	Structured survey design taking the household as the unit of analysis	Systematic procedure for sampling and coding observed behavior
原典	Bernard, H. R. (2017). Research Methods in Anthropology: Qualitative and Quantitative Approaches (6th ed.). Lanham, MD: Rowman & Littlefield. ISBN: 9780759112421	Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3–4), 227–267.
別名	Household Census, Community Household Survey, Household Economic Survey, Household Roster Survey	Behavior Coding, Systematic Observation Coding, Behavioral Sampling and Coding, Observational Coding
関連	4	4
概要	An anthropological household survey is a structured or semi-structured, census-style instrument administered to the households of a community to record their composition, economy, and assets in a standardized form. Taking the household rather than the individual as the unit of analysis, it captures who lives together, how they are related, what they own and produce, and how they make a living. Whether applied as a full census of every household or to a representative sample, it turns the texture of community life into comparable, aggregable data that complement participant observation.	Behavioral observation coding is the systematic practice of recording who does what, when, by applying an explicit sampling rule and a predefined coding scheme to observed behaviour. Rather than jotting impressions, the observer commits in advance to a rule — focal-individual, scan, ad libitum, or continuous sampling — and to a list of mutually defined behaviour categories, so that records are reproducible and comparable. Because two trained observers should code the same scene the same way, the method also requires measuring inter-observer reliability before the data are trusted.