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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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Systematic Sampling Method01:17

Systematic 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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Random Sampling Method01:09

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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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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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. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling Soils in a Heterogeneous Research Plot
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Respondent-driven sampling: Advantages and disadvantages from a sampling method.

María S Navarrete1, Constanza Adrian2, Vivienne C Bachelet3

  • 1Escuela de Medicina, Universidad de Santiago de Chile, Santiago, Chile. ORCID: 0000-0002-5411-4498.

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|January 3, 2022
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Summary
This summary is machine-generated.

Respondent-driven Sampling (RDS) offers unique advantages but faces challenges in its assumptions and reporting quality. Ensuring methodological soundness is crucial for reliable health program data and policy development.

Keywords:
Hard-to-reach populationsSampling MethodsRespondent-driven Sampling

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Area of Science:

  • Social Sciences
  • Public Health
  • Statistics

Background:

  • Respondent-driven Sampling (RDS) is a network-based sampling method.
  • It is increasingly used in public health research for hard-to-reach populations.
  • Concerns exist regarding its methodological rigor and applicability.

Purpose of the Study:

  • To summarize the core components, benefits, and drawbacks of RDS.
  • To highlight criticisms concerning RDS assumptions and statistical outputs.
  • To address issues in the reporting quality of RDS studies.

Main Methods:

  • Literature review and critical analysis of Respondent-driven Sampling methodology.
  • Examination of theoretical assumptions and practical implementation challenges.
  • Evaluation of reporting standards and potential biases in RDS studies.

Main Results:

  • RDS presents advantages but also significant disadvantages.
  • Criticisms focus on the feasibility of assumptions, point estimators, and variance calculations.
  • Suboptimal reporting quality and potential for implementation/analytical bias are noted.

Conclusions:

  • Methodological soundness is vital for RDS, especially in health policy.
  • Limited empirical evidence exists on the representativeness of RDS results.
  • Confidence in RDS requires verification of network structure, sampling assumptions, and reporting quality.