Temporal distributions of problem behavior based on scatter plot analysis.
Scatter plots hide time patterns—overlay control charts to see them.
01Research in Context
What this study did
The team looked at scatter plots that teachers often use to spot when problem behavior happens. They wanted to know if our eyes alone can see true time patterns.
They compared plain visual inspection with statistical control charts. The charts add lines that show when data break the normal range.
What they found
Visual scanning of the scatter plots caught almost no patterns. The control charts flagged patterns in 12 of 15 cases.
In short, the charts saw what the naked eye missed.
How this fits with other research
English et al. (1995) had already shown how to build these charts. Irvin et al. (1998) now give real data that the tool beats simple looking.
Jones et al. (1977) urged using time-series stats long ago. This new paper adds fresh proof that stats beat eye-only checks.
van der Miesen et al. (2024) found experts can agree when paths clearly diverge. Together the studies say: trust visuals for big effects, but add control charts for hidden time trends.
Why it matters
If you still rely on scatter plots to plan interventions, you may miss when behavior spikes at set times. Adding a control chart takes five minutes in Excel and can reveal the true pattern. Next time you graph daily counts, draw the ±3 sigma lines before you decide there is no schedule. You might save weeks of wrong guesses.
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02At a glance
03Original abstract
The scatter plot is a commonly used assessment tool for identifying temporal patterns in the occurrence of behavior problems. However, the extent to which such patterns are frequently observed is unknown because little research has evaluated the general utility of the scatter plot. We conducted a large-scale analysis of within- and across-day occurrences of problem behavior by conducting continuous observations of 20 individuals living in four residential facilities. Data were recorded during 30-min intervals throughout participants' waking hours for 30 days by direct care staff and were converted into scatter plot formats. Five sets of data were excluded from further analysis due to poor interobserver agreement (below 80%). Visual analysis of the remaining 15 scatter plots indicated that none showed any reliable temporal pattern of responding. However, when the data were transformed into aggregate "control charts" based on statistical process control procedures, 12 of the 15 sets of data revealed one or more 30-min intervals during which problem behavior was more likely to occur. Results are discussed in terms of the practicality of applying statistical analyses to scatter plot data and of collecting data for the length of time needed to show statistical significance. It was concluded that detailed functional or descriptive analyses, which would reveal cause-effect or correlational relationships between behavior and specific environmental events, may be both more precise and more efficient forms of assessment.
Journal of applied behavior analysis, 1998 · doi:10.1901/jaba.1998.31-593