Meta-analysis of noncontingent reinforcement effects on problem behavior.
NCR delivers huge cuts in problem behavior when you use the right reinforcer and keep the schedule intact.
01Research in Context
What this study did
The team pooled every single-case NCR study they could find. They looked at how much problem behavior dropped when adults or kids got free reinforcers on a set schedule.
They compared functional reinforcers (the ones that matched the behavior’s purpose) with nonfunctional ones. They also checked if thinning the schedule later hurt the gains.
What they found
Problem behavior fell hard. The average drop was very large.
Functional reinforcers worked best. Schedule thinning barely touched the good results.
How this fits with other research
Mueller et al. (2000) already showed that variable-time NCR works as well as fixed-time. The 2015 meta backs them up and adds power in numbers.
Verriden et al. (2019) starts with NCR plus DRA, sees little change, then adds a punisher. That study does not fight the meta; it just shows NCR can need a boost when behavior is automatically reinforced.
Jones et al. (2022) warns that 80% integrity can break NCR. The meta proves NCR works, but Jones tells us to guard the dose carefully.
Jessel et al. (2018) reached 90% drops with FCT plus thinning. Their result mirrors the meta: thinning is safe when you pair it with solid reinforcement logic.
Why it matters
You now have a green light to use NCR with confidence. Pick the functional reinforcer, start rich, then thin without fear. Watch integrity like Jones urges, and add a punisher only if the free reinforcers stall.
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02At a glance
03Original abstract
A meta-analysis of noncontingent reinforcement (NCR) outcomes was conducted using hierarchical linear modeling (a) to document the effect size for decreasing problem behavior, (b) to compare effect sizes for NCR using functional reinforcers and nonfunctional reinforcers, and (c) to document the influence of schedule thinning on effect size. Analyses were conducted with data from 55 studies and 91 participants. Results indicate that NCR was associated with a very strong effect size (d =-1.58) for reduction of problem behavior, functional reinforcers were slightly more effective than nonfunctional reinforcers, and schedule thinning resulted in minor degradation of effect size. Meta-analysis of single-case design data provides a method to quantitatively estimate effect sizes of interventions across participants. Therefore, it allows one to identify important variables that are not otherwise evident in single-case data, helps to disseminate findings to the broader scientific community, and contributes to the documentation of empirically supported interventions.
Journal of applied behavior analysis, 2015 · doi:10.1002/jaba.189