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Systematic Sampling Method01:17

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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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A latent variable model approach to estimating systematic bias in the oversampling method.

Katherina K Hauner1, Richard E Zinbarg, William Revelle

  • 1Department of Psychology, Northwestern University, Evanston, IL, USA, hauner@u.northwestern.edu.

Behavior Research Methods
|October 22, 2013
PubMed
Summary

Oversampling rare outcomes in research does not significantly bias effect size estimates. This method is crucial for smaller samples to prevent inflated results when rare outcomes are common.

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Area of Science:

  • Behavioral Science
  • Statistical Methodology

Background:

  • Oversampling rare outcomes is common practice to manage sample size.
  • Potential for statistical bias due to disproportionate data representation is unknown.

Purpose of the Study:

  • To investigate if oversampling introduces systematic bias in effect size estimates.
  • To compare oversampling bias with estimates from random samples.

Main Methods:

  • Utilized simulated data sets to analyze oversampling effects.
  • Examined the relationship between oversampled predictors and outcome variables.

Main Results:

  • Increased oversampling correlated with a decrease in the absolute value of effect size estimates.
  • The magnitude of this decrease was nominal, suggesting minimal practical impact.
  • Oversampling is vital in small samples to prevent inflated effect sizes.

Conclusions:

  • Oversampling rare outcomes does not systematically bias results in behavioral research.
  • The method is statistically sound and does not typically impact findings.
  • Essential for maintaining accurate effect sizes in studies with limited data.