Assessing challenging behaviors in children with autism spectrum disorders: a review.

Kleinert et al. (2007) read every paper they could find on measuring problem behavior in children with autism. They did not run new kids; they simply mapped what tools already exist. Their goal was to see which ones are solid enough for everyday clinic use.

The team found plenty of lab-grade checklists and scatter plots that work well in research. Yet they warn that most are too long or need special training. Clinicians often skip them and go by guesswork, so the best tools sit on the shelf.

Gal et al. (2009) give the next step. They show autism-specific patterns of stereotypy and self-injury that differ from kids with ID or vision loss. Use their short checklist to know which repetitive movements point to autism.

Leaf et al. (2012) tighten the lens further. They tracked toddlers and found that sheer frequency of repetitive moves is not enough; you also need to note how rigid the child is when you interrupt. Their data add a time rule to the 2007 call for better markers.

Kang et al. (2013) show one quick fix. In a tiny alternating-treatments study, tangibles raised stereotypy while social praise kept it low. If you must test reinforcers, count stereotypy during each trial and pick the reinforcer that keeps hands calm.

Safer-Lichtenstein et al. (2021) look at a different piece of the puzzle—how kids with autism learn to predict events. Their review does not clash with L et al.; it simply widens the scope from behavior topographies to learning processes you might also want to track.

You now have a short path: run the Eynat checklist to flag autism-style movements, use B et al.’s rigidity probe if the child is under five, and pick social over tangible reinforcers when Soyeon counts show a jump in stereotypy. These three moves take ten extra minutes and give you sharper, diagnosis-linked data for your FBA.