Using real-time recording to enhance the analysis of within-session functional analysis data.
Stop chunking your functional analysis into intervals; record every second to see the real within-session pattern.
01Research in Context
What this study did
Field et al. (2001) wrote a how-to paper, not an experiment.
They tell you to stop using 10-second partial interval coding during a functional analysis.
Instead, mark the exact second the behavior starts and stops.
This real-time record lets you see quick spikes or drops inside one session.
What they found
The paper gives no new numbers.
It simply shows that moment-by-moment data paint a clearer picture than interval summaries.
You can spot extinction bursts, sudden recovery, or early avoidance that flat interval scores hide.
How this fits with other research
Prasher et al. (2007) meta-analysis backs the idea: the finer your assessment data, the stronger your later treatment works.
Jones et al. (1977) already told us to use time-series stats on single-case graphs; T et al. add "collect the stream live, not chunked."
DeRosa et al. (2019) ran an actual experiment and proved the risk: when they counted only active-treatment minutes, one method looked best, but when they counted the whole session time the winner flipped.
Together these papers say: both when you record and how you slice the session can change your conclusion.
Why it matters
If you run a 5-min IISCA or watch for within-session trends, switch to real-time coding.
Open a free timer app, tap onset and offset, then plot the session second-by-second.
You will catch brief bursts that interval sheets smooth away and avoid picking the wrong function or treatment.
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02At a glance
03Original abstract
Functional analysis methods have become standard practice for determining the maintaining variables of problem behavior. The analysis of within-session response patterns has been proposed as a useful adjunct to the functional analysis. Many within-session analyses have been conducted on data obtained from interval scoring methods. However, interval methods only provide an estimate of within-session data. The authors briefly describe a real-time recording method and provide a rationale for its use. The authors then provide descriptions of several research studies from their lab in which real-time data were crucial in determining behavioral function from experimental analyses.
Behavior modification, 2001 · doi:10.1177/0145445501251005