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Spot Observation Sampling×Behavioral Observation Coding×
DziedzinaAnthropologyAnthropology
RodzinaProcess / pipelineProcess / pipeline
Rok powstania19841974
TwórcaTime-allocation tradition in cultural anthropology (synthesized by Gross)Behavioral sampling tradition (Altmann), adapted to anthropology (Bernard)
TypInstantaneous sampling procedure for estimating time allocationSystematic procedure for sampling and coding observed behavior
Źródło pierwotneGross, D. R. (1984). Time allocation: a tool for the study of cultural behavior. Annual Review of Anthropology, 13, 519–558. DOI ↗Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3–4), 227–267. DOI ↗
Inne nazwySpot Check Sampling, Instantaneous Spot Observation, Random Spot Checks, Spot SamplingBehavior Coding, Systematic Observation Coding, Behavioral Sampling and Coding, Observational Coding
Pokrewne44
PodsumowanieSpot 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.
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