विधियों की तुलना करें

	Spot Observation Sampling	Behavioral Observation Coding
क्षेत्र	Anthropology	Anthropology
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1984	1974
प्रवर्तक	Time-allocation tradition in cultural anthropology (synthesized by Gross)	Behavioral sampling tradition (Altmann), adapted to anthropology (Bernard)
प्रकार	Instantaneous sampling procedure for estimating time allocation	Systematic procedure for sampling and coding observed behavior
मौलिक स्रोत	Gross, D. R. (1984). Time allocation: a tool for the study of cultural behavior. Annual Review of Anthropology, 13, 519–558.	Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3–4), 227–267.
उपनाम	Spot Check Sampling, Instantaneous Spot Observation, Random Spot Checks, Spot Sampling	Behavior Coding, Systematic Observation Coding, Behavioral Sampling and Coding, Observational Coding
संबंधित	4	4
सारांश	Spot observation sampling is a time-allocation technique in which an observer arrives at randomly chosen moments and records, instantaneously, exactly what each visible person is doing at that instant — not what they were doing before or after. Because the moments are sampled at random across the daily and seasonal round, the proportion of spot observations that fall in a given activity is an unbiased estimate of the proportion of time people spend in that activity. It turns a scatter of brief snapshots into a quantitative budget of how a community allocates its waking hours.	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.