Journal of Medical Statistics and Informatics

Journal of Medical Statistics and Informatics

ISSN 2053-7662
Original Research

Can We Learn from the“Wisdom of the Crowd”? Finding the Sample-Size Sweet Spot – an Analysis of Internet-Based Crowdsourced Surveys of Fertility Professionals

Gon Shoham1*, Milton Leong2, Ariel Weissman3 and Yuval Yaron4

*Correspondence: Gon Shoham

1. Sackler Faculty of Medicine, Tel Aviv University, P.O.B. 39040, Ramat Aviv, Tel Aviv 69978, Israel.

Author Affiliations

2. IVF Clinic, The Women’s Clinic, 12/F, Central Tower, 28 Queen’s Road Central, Central, Hong Kong, China.

3. IVF Unit, Department of Obstetrics & Gynecology, Edith Wolfson Medical Center, 62 Ha-Lokhamim St., Holon 5822012, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

4. Prenatal Genetic Diagnosis Unit, Genetics Institute, Tel Aviv Sourasky Medical Center, 6 Weizmann Blvd., Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.


Background: The purpose of this research was to calculate the minimum sample size needed to obtain reliable results from crowdsourced retrospective online surveys of IVF clinics, where the sample was IVF cycles performed annually per clinic– a new metric that may offer more survey flexibility than number of clinics or respondents.

Methods: This analysis used two statistical formulas to calculate sample sizes and confidence intervals, initially assessing a published self-report survey conducted online by IVF-Worldwide of global IVF practices. The survey covered 592,900 IVF cycles from 795 clinics worldwide. A subset of 275,600 European IVF cycles was used as a test sample population, which was compared statistically with the actual survey population. To validate results, two additional geographic subsets (North America and Asia) from the initial survey and three additional previously published surveys were also evaluated in same fashion. Only one survey entry was accepted per clinic.

Results: Results showed that to obtain reliable survey outcomes, the estimated minimum sample size was 35,000 cycles. Given a 99% confidence level, 0.5 probability, and a 5% estimation error, the minimum sample size for Europe was 35,340. Similarly, sample sizes for earlier surveys and other continent subsets were between 35,280 and 35,340.

Conclusions: Surveyors often strive for small sample sizes to savecost, effort, time and/or management overhead. Currently, however, sample size standards for online surveys do not exist. The results presented here suggest that sample sizes below 35,000 may lead to unreliable results. Finding the right sample size for multiple-choice crowdsourced surveys will save cost and effort, accelerate research, and empower surveys originally deemed too difficult and/or expensive due to inordinate sample sizes required. Surveys that count number of IVF cycles as the survey population may offer researchers more options for how to conduct and analyze surveys. Further research is required to apply findings to other areas of medicine, such as surveying using the number of procedures performed by clinic.

Keywords: Infertility, survey, sample size, statistics, crowdsource, IVF-Worldwide

ISSN 2053-7662
Volume 7
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