Incorporating personality trait measures in behavioral assessment. Nuts in a fruitcake or raisins in a mai tai?

The authors wrote a think piece, not an experiment. They asked, "Do we need personality tests in ABA?"

They argued that broad trait labels like "neurotic" or "introverted" are fuzzy and slow us down.

The paper says skip the questionnaires. Watch what the person does in the exact spot where the problem happens.

Traits, they claim, add little value and can mislead treatment choices.

Bouck et al. (2016) directly contradicts the target. In adults with autism, Five-Factor traits explained 70 % of symptom spread. The two papers clash because they look at different levels. Singh et al. (1993) attacks broad, cross-situation labels. Bouck et al. (2016) shows traits can still map meaningful patterns inside one diagnosis group.

Later work backs the target’s call for tight, situational data. Morris et al. (2023) trimmed sociability coding to simple approach-avoid counts and kept strong validity. Gutierrez et al. (1998) showed the Motivation Assessment Scale has weak reliability, echoing the warning against sloppy checklists.

Matson et al. (1999) and Fox et al. (2001) extend the theme by proving that brief, function-based tools like the QABF predict treatment success better than global ratings.

You can safely drop long personality packets from your intake. Replace them with quick, context-bound measures like brief QABF interviews or conditional approach tallies. You save time, keep precision, and avoid trait labels that don’t guide intervention.