Consistency in Single-Case ABAB Phase Designs: A Systematic Review.

Spiegel et al. (2023) hunted for a simple way to score how steady an ABAB graph looks. They pulled 460 published ABAB data sets and ran each through a new yardstick called CONDAP.

CONDAP counts how far each data point lands from the phase mean. A score of 0 means every dot hugs the middle line. Higher numbers mean wilder bounce.

Only 4 in 10 graphs earned a 'medium' consistency label. The rest were either super tight or all over the place.

The team then set cut-offs: CONDAP ≤ 0.5 = 'very high' steadiness, > 2 = 'very low' steadiness. These marks give BCBAs a fast ruler for graph trustworthiness.

LAller et al. (2023) are testing a reading program for the students with intellectual disability using multiple baseline graphs. Those graphs can now be checked with the same CONDAP ruler, letting one study speak the same numeric language as another.

Perez et al. (2015) used brief experimental analyses to pick reading interventions for kids with ADHD. Their quick-switch ABAB style often produces bouncy data. Applying CONDAP can tell us if the bounce is too high to trust the winner.

Hattier et al. (2011) complained that ID drug trials use 25+ different outcome measures, so meta-analysis is impossible. René’s single consistency metric could rescue single-case drug studies by giving them one shared score, turning apples and oranges into numbers you can average.

You can now open any ABAB graph, plug the data into the free CONDAP formula, and get a number that tells families, supervisors, or funders how clear the effect is. No more eyeball fights over 'Does it look flat to you?' If the score tops 2, gather more data or tweak the intervention before you claim victory.