Uploaded June 2020 | Updated September 2026, 2 weeks ago
This video demonstrates how you can easily obtain a confidence interval around sample proportions using SPSS. In this video I focus on demonstration of Clopper-Pearson exact confidence intervals. You can obtain a copy of the data used in the video here: drive.google.com/file/d/1OndJ2wuzxTJA0RaVqW7K3r2bXqX81nNs/view?usp=sharing
Note: Clopper-Pearson intervals are formed using the binomial sampling distribution (see en.wikipedia.org/wiki/Binomial_proportion_confidence_interval). An alternative method (not shown in the video, but sometimes shown in textbooks) is to use the z-distribution to construct a symmetric interval around the proportion (referred to as 'normal distribution approximation'). That approach, however, is best used when the sample proportion is nearer to .50 [and not very close to 0 or 1] and/or your sample size is large. As your proportion gets closer to 0 or 1 (i.e., the boundaries for proportions), then larger and larger samples would be required to construct symmetric confidence intervals where one of the bounds does not end up falling outside the range of 0 and 1. A Powerpoint (drive.google.com/file/d/1IQWDl3OjZzLF8tgI3cNgYVZGb1JaguFy/view?usp=sharing) and Excel (drive.google.com/file/d/1eDIE9GDXpQ0vXnbMrz-xKJ3zVMF6Novk/view?usp=sharing) file can be downloaded that goes into more detail regarding using the 'normal distribution approximation'.
This video demonstrates how you can easily obtain a confidence interval around sample proportions using SPSS. In this video I focus on demonstration of Clopper-Pearson exact confidence intervals. You can obtain a copy of the data used in the video here: drive.google.com/file/d/1OndJ2wuzxTJA0RaVqW7K3r2bXqX81nNs/view?usp=sharing
Note: Clopper-Pearson intervals are formed using the binomial sampling distribution (see en.wikipedia.org/wiki/Binomial_proportion_confidence_interval). An alternative method (not shown in the video, but sometimes shown in textbooks) is to use the z-distribution to construct a symmetric interval around the proportion (referred to as 'normal distribution approximation'). That approach, however, is best used when the sample proportion is nearer to .50 [and not very close to 0 or 1] and/or your sample size is large. As your proportion gets closer to 0 or 1 (i.e., the boundaries for proportions), then larger and larger samples would be required to construct symmetric confidence intervals where one of the bounds does not end up falling outside the range of 0 and 1. A Powerpoint (drive.google.com/file/d/1IQWDl3OjZzLF8tgI3cNgYVZGb1JaguFy/view?usp=sharing) and Excel (drive.google.com/file/d/1eDIE9GDXpQ0vXnbMrz-xKJ3zVMF6Novk/view?usp=sharing) file can be downloaded that goes into more detail regarding using the 'normal distribution approximation'.
