Differential changes that occur to each behavior, person or in each setting help to strengthen what is essentially an AB design with its problematic competing hypotheses.
By gathering data from many subjects (instances), inferences can be made about the likeliness that the measured trait generalizes to a greater population.
Multiple baseline designs are associated with potential confounds introduced by experimenter bias, which must be addressed to preserve objectivity.
Experimenters are advised not to remove cases that do not exactly fit their criteria, as this may introduce sampling bias and threaten validity.
The ability to retrieve complete data sets within well defined time constraints is a valuable asset while planning research.
Nonconcurrent multiple baseline studies apply treatment to several individuals at delayed intervals.
A priori (beforehand) specification of the hypothesis, time frames, and data limits help control threats due to experimenter bias.
If in-session data is gathered a note of the dates should be tagged to each measurement in order to provide an accurate time-line for potential reviewers.