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Related Concept Videos

Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Updated: May 6, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Using 'found' data to augment a probability sample: Procedure and case study.

J C Mc Overton1, T C Young, W S Overton

  • 1Biology Department, UCLA, 90024, Los Angeles, CA, USA.

Environmental Monitoring and Assessment
|November 15, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a method to combine probability samples with nonprobability (

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Area of Science:

  • Environmental monitoring
  • Statistical methodology

Background:

  • Many environmental monitoring schemes rely on nonprobability samples, limiting unbiased population estimates.
  • Probability sampling, while ideal for unbiased estimates, is not always feasible or implemented.

Purpose of the Study:

  • To present a general procedure for augmenting existing probability samples with nonprobability ('found') data.
  • To demonstrate methods for integrating diverse data sources to improve population estimates.

Main Methods:

  • Utilizing sampling frame attributes to group probability and nonprobability samples into similar subsets.
  • Employing pseudo-random and calibration methods to establish similarity and generate estimates.
  • Treating augmented data as a probability sample for population estimation.

Main Results:

  • Demonstrated the applicability of the augmentation procedure using stream discharge data from the southeastern US.
  • Showcased how nonprobability data can be integrated to enhance the representativeness of monitoring schemes.
  • Validated the pseudo-random and calibration methods for different data contribution scenarios.

Conclusions:

  • The described procedure effectively integrates nonprobability data into probability sampling frameworks.
  • This approach enhances the utility of existing monitoring data for robust population estimation.
  • The methods offer a viable solution for improving environmental monitoring accuracy and scope.