A researcher separates the area of a forest into 1,000 small regions. The researcher collects data on all of the trees in each of a sample of 20 small regions. Which sampling method is being used?
Cluster sampling is being used.
In this scenario, the researcher divides the forest into 1,000 small regions and samples entire groups of these regions, which is characteristic of cluster sampling. This method involves selecting whole clusters or groups for study rather than individual elements, allowing for practical data collection across a wide area.
Cluster sampling involves dividing a population into groups (clusters) and randomly selecting some of these groups to collect data from all members within them. Here, the researcher has separated the forest into regions and sampled entire regions, making this the appropriate sampling method.
Judgment sampling, or purposive sampling, involves selecting samples based on the researcher's judgment about which individuals will be most useful or representative. This method does not apply here, as the researcher is not selecting specific regions based on judgment, but rather sampling defined clusters.
Quota sampling requires the researcher to ensure certain characteristics are represented within the sample. This method typically involves non-random selection until specific quotas are met. In the given situation, the researcher does not appear to enforce quotas for specific characteristics but is sampling whole regions instead.
Simple random sampling entails selecting individuals from the entire population in such a way that each individual has an equal chance of being chosen. In this case, since the researcher is sampling entire regions rather than individuals, this method does not apply.
In the context of the study, cluster sampling is the most accurate description of the method employed by the researcher. By selecting entire regions to study rather than individual trees, the researcher is effectively utilizing a cluster sampling approach. This technique is efficient for large populations and is particularly useful when geographic or logistical constraints exist.
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