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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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Related Experiment Video

Updated: Jul 17, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Disproportionate sampling for population subgroups in telephone surveys.

William D Kalsbeek¹, Walter R Boyle, Robert P Agans

¹Survey Research Unit, Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, NC 27599-2400, USA. bill_kalsbeek@unc.edu

Statistics in Medicine

|February 15, 2007

Summary

Disproportionately sampling telephone survey subgroups can increase nominal sample sizes but may negatively impact effective sample sizes. Researchers should cautiously consider this design for telephone surveys.

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Last Updated: Jul 17, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Survey methodology
Statistical analysis
Public health research

Background:

Population studies often require examining specific subgroups.
Sampling frames for small subgroups are often unavailable or costly to access.
Telephone surveys face challenges in efficiently sampling rare subgroups.

Purpose of the Study:

To evaluate the statistical effects of two-stratum telephone sample designs with disproportionate sampling.
To compare disproportionate sampling to proportionate sampling for subgroup analysis.
To assess the impact on nominal and effective sample sizes in telephone surveys.

Main Methods:

Analysis of two-stratum telephone sample designs.
Comparison of disproportionate sampling with proportionate sampling.
Determination of impacts on nominal and effective sample sizes, considering sample weight variation.

Main Results:

Disproportionate sampling can improve nominal subgroup sample sizes.
Effective sample sizes may be reduced due to increased sample weight variation.
Findings are illustrated with data from two recent telephone surveys.

Conclusions:

Disproportionate sampling in telephone surveys requires careful consideration.
Both survey designers and analysts must cautiously approach this sampling strategy.
Potential trade-offs between nominal and effective sample sizes should be evaluated.