Review of Methods to Equate Target Sets in the Adapted Alternating Treatments Design.

Cariveau et al. (2021) looked at how researchers make sure target sets are equal in AATD studies. They read every AATD paper they could find and wrote down how authors said they matched the tasks.

The team wanted to see if people follow any one way to equate targets. They also asked, 'Do authors even tell us what they did?'

Just over half of the studies said anything at all about equating. When authors did speak up, the steps were all over the map. Random assignment was the most common trick, but many other ways showed up too.

No single best practice stood out. The review makes it clear that most AATD papers leave readers guessing.

Cariveau et al. (2022) picked up where Tom et al. left off. They looked at control quality in the same design and also found about half of studies missing key steps. Together, the two reviews show that AATD papers often skip both equating and control details.

Stoddard et al. (1988) warned about this problem decades ago. They offered a way to match task difficulty, yet Tom et al. show most modern studies still do not follow a standard plan.

Bolte et al. (2013) saw the same scatter-shot style when they counted outcome measures in autism trials. Across topics, reviewers keep finding wide, unhelpful variety in how researchers describe their methods.

If you run an AATD study, spell out exactly how you made the targets equal and why you picked that way. Add a no-treatment control and graph the data so readers can see the effect. Clear, matched sets plus strong controls give BCBAs confidence that the better score really came from the intervention, not from an easier list of tasks.