Data peeking: a quantitative and qualitative exploration of the use of interim analysis
Data peeking, quitting data collection early or adding more participants at the end, offers the advantage of saving time and money. However, performing an interim analysis without correction leads to a Type-I error inflation. Using alpha spending function could be used to solve this problem. In this paper, we simulated the effects of interim analysis with and without an alpha spending function on type-I error, power and expected sample size. We also offer a Bayesian perspective to interim analysis. In the last part, we discuss the use of interim analysis in psychological research using a qualitative approach.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted under the conditions of the Creative Commons Attribution-Share Alike (CC BY-SA) license and that copies bear this notice and the full citation on the first page.