))
Sample Design - Part 1. Sample Size - or why just over 1000 responses is enough to generalize findings to millions of people!
Manage episode 421733344 series 3573207
内容由Azdren Coma and Seon Yup Lee, Azdren Coma, and Seon Yup Lee提供。所有播客内容（包括剧集、图形和播客描述）均由 Azdren Coma and Seon Yup Lee, Azdren Coma, and Seon Yup Lee 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品，您可以按照此处概述的流程进行操作https://zh.player.fm/legal。
In this episode of the Total Survey Design Podcast, we explore the fascinating concept of sample size and its impact on survey accuracy. Discover why just about 1000 responses can represent the views of millions, debunking common misconceptions about survey sampling. We'll dive into the key parameters for calculating sample size, such as margin of error, confidence level, predicted variance, and target population size. Learn how a small, well-chosen sample can provide precise and reliable data, making large-scale surveys both practical and cost-effective. Tune in to understand the math behind the magic!
Here's a general formula commonly used for calculating sample size for simple random sampling:
𝑛=𝑍^2*𝑃*(1−𝑃)/𝐸^2
Where:
𝑛 = required sample size
𝑍 = Z-value (the number of standard deviations from the mean corresponding to the desired confidence level)
For a 95% confidence level, 𝑍≈1.96
𝑃 = estimated proportion of the population (if unknown, 𝑃 is often assumed to be 0.5 to maximize the sample size)
𝐸 = margin of error (the desired level of precision, expressed as a decimal)
Find us online at: instagram.com/totalsurveydesign/
https://taplink.cc/totalsurveydesign
Contact us at: totalsurveydesign@gmail.com
19集单集
分享
