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Optimal Stratified Sampling for Probability-Based Online Panels

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  • Jonathan Eggleston

Abstract

Online probability-based panels have emerged as a cost-efficient means of conducting surveys in the 21st century. While there have been various recent advancements in sampling techniques for online panels, several critical aspects of sampling theory for online panels are lacking. Much of current sampling theory from the middle of the 20th century, when response rates were high, and online panels did not exist. This paper presents a mathematical model of stratified sampling for online panels that takes into account historical response rates and survey costs. Through some simplifying assumptions, the model shows that the optimal sample allocation for online panels can largely resemble the solution for a cross-sectional survey. To apply the model, I use the Census Household Panel to show how this method could improve the average precision of key estimates. Holding fielding costs constant, the new sample rates improve the average precision of estimates between 1.47 and 17.25 percent, depending on the importance weight given to an overall population mean compared to mean estimates for racial and ethnic subgroups.

Suggested Citation

  • Jonathan Eggleston, 2025. "Optimal Stratified Sampling for Probability-Based Online Panels," Working Papers 25-69, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:25-69
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    File URL: https://www2.census.gov/library/working-papers/2025/adrm/ces/CES-WP-25-69.pdf
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