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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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 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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Convenience Sampling Method00:55

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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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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.
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Nonprobability and probability-based sampling strategies in sexual science.

Joseph A Catania1, M Margaret Dolcini, Roberto Orellana

  • 1a Hallie E. Ford Center for Healthy Children and Families, School of Social and Behavioral Health Sciences, College of Public Health and Human Sciences , Oregon State University.

Journal of Sex Research
|April 22, 2015
PubMed
Summary

This study explores sampling methods in sexual science, highlighting internet sampling for cost-efficiency and discussing probability-based methods for hard-to-reach populations. It offers strategies to improve generalizability in sex research.

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Area of Science:

  • Sexual science research
  • Population sampling methodologies
  • Generalizability in research

Background:

  • Sexual science often relies on nonprobability samples with limited generalizability.
  • Probability-based studies, while ideal for generalizability, are expensive for hard-to-reach populations.
  • Recent sampling expert conclusions offer new perspectives for sexual science.

Purpose of the Study:

  • To review sampling methods relevant to sexual science.
  • To advocate for the use of nonprobability sampling methods like internet sampling.
  • To demonstrate the applicability of probability-based sampling to hard-to-reach populations in sex research.

Main Methods:

  • Overview of internet sampling as a cost-efficient, nonprobability method.
  • Discussion of probability-based sampling strategies for difficult-to-reach groups.
  • Presentation of three case studies using qualitative and quantitative techniques.

Main Results:

  • Internet sampling is a valuable, cost-efficient tool for sex researchers in modeling and clinical trials.
  • Probability-based sampling is more feasible for hard-to-reach populations than commonly assumed.
  • Case studies illustrate successful application of probability-based sampling in diverse populations.

Conclusions:

  • Nonprobability methods, particularly internet sampling, offer practical solutions for sex research.
  • Probability-based sampling can be adapted for hard-to-reach populations with strategic planning.
  • Recommendations are provided for improving sampling strategies and evaluation in sexual science.