Using the dual-criteria methods to supplement visual inspection: An analysis of nonsimulated data
The purpose of our study was to examine the probability of observing false positives in non-simulated data using the dual-criteria methods. We extracted data from published studies to produce a series of 16,927 datasets and then assessed the proportion of false positives for various phase lengths. Our results indicate that collecting at least 3 data points in the first phase (Phase A) and at least 5 data points in the second phase (Phase B) is generally sufficient to produce acceptable levels of false positives.
Data analysis Dual-criteria method False positive Single-case designs Type I error