probabilistic sampling

probabilistic sampling

[‚präb·ə′lis·tik ′sam·pliŋ]
(statistics)
A process in which the laws of probability determine which elements are to be included in a sample.
References in periodicals archive ?
Two sampling strategies are recommended for sampling Class 3 zones: a purely probabilistic sampling approach and a combined judgmental and random (probabilistic) approach (CJR).
However, because the two purely probabilistic sampling designs were applied to the entire 1st floor and the entire 2nd floor, the possible cleanliness of smaller areas on those floors could not be readily quantified with X %/Y % confidence statements.
Purely probabilistic sampling also identified contaminated areas.
Probabilistic sampling of the adult population ([greater than or equal to] 18 years old) living in Brazil's 26 state capitals and Federal District was carried out in 2006 using the VIGITEL system.
Between 2000 and 2006, the study was biannual, using a probabilistic sampling method, different from the previous years, to select the childbearing women and the health establishments to be analyzed [14-16].
Problems of sampling are again referred to in chapter 11, where it is stated that 'Qualitative approaches demand different sampling techniques from the randomly-selected and probabilistic sampling which quantitative researchers use.' Sampling in qualitative surveys is said to 'develop during the research process as you discover new avenues and clues to follow-up'.
He indicated, "Moreover, the typical meta-analysis virtually never meets the condition of probabilistic sampling of a population." (Ibid.) To make this clearer to some, in a national presidential election Meta- analysis would take all the candidates primary wins and losses, aggregate and randomize them and predict the winner.
Usually, textbooks or papers about the research methodology would recommend probabilistic sampling methods in order to reduce sampling bias or errors.
In the absence of a probabilistic sampling, principles of statistics dictate that one should not generalize beyond the sample (Bernard, 1995, p.
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