Experimental analysis of individual differences and personality.

Hobson (1984) wrote a position paper. He told behavior analysts to stop erasing individuality. Instead of averaging data across people, he said we should use single-subject designs to study personality itself.

The paper is theoretical. No new experiment was run. It is a call to change how we plan and interpret SCED work.

The author found a blind spot. By trying to control or average out individual differences, we throw away orderly patterns. Those patterns are the very thing we call personality.

He argued that stable, person-specific trends can be revealed when each participant’s own data are kept intact and analyzed.

Waterhouse et al. (2014) and Mottron (2021) extend the same logic to autism. They say stop hunting one universal autism brain and start mapping each child’s unique profile.

Spackman et al. (2023) give a live example. They split insistence on sameness into three clear sub-types in almost two thousand autistic youth, showing individual patterns are measurable and meaningful.

Tanious et al. (2026) adds a technical note. If you follow P’s advice and run SCEDs, check for serial dependency so your individual-level conclusions stay solid.

Next time you run a single-subject study, treat each line on the graph as a personality signature, not noise. Ask what stable traits the pattern reveals, then test those traits in the next participant. This mindset turns your everyday SCED into a tool for discovering individual difference variables instead of hiding them.