Sidman or statistics?
Ditch group averages—plot each learner’s conditional-discrimination accuracy to find hidden variables.
01Research in Context
What this study did
Iversen (2021) wrote a theory paper. He asked: should we use group statistics or Sidman-style single-case methods to study conditional discrimination?
He argued that averaging data across people hides the real story. Each learner has unique sources of error. Group stats bury those clues.
What they found
The paper says: skip group stats. Look at each subject’s accuracy line by line.
When you graph one learner’s hits and misses, you can spot hidden variables. These might be flickering lights, off-topic comments, or tiny schedule changes.
How this fits with other research
Christophersen et al. (1972) once said ANOVA works fine for single-case reversal designs. Iversen (2021) flatly rejects that view. The two papers clash, but the fight is about purpose. R wanted a yes-no verdict on an intervention. Iversen wants to uncover why behavior changes at all.
Blough (1980) warned that accuracy alone can trick you. Iversen (2021) agrees and adds: only single-subject graphs let you dig deeper to find the trick.
Wolfe et al. (2018) showed the CDC method lines up with expert eyeballing. Iversen would still say: even a quick rule is second best to full visual inspection of each learner’s data.
Why it matters
Next time you run a conditional-discrimination probe, graph each learner separately. Look for sudden jumps or drops. Ask: what happened right before? You may catch an uncontrolled variable you can fix in the next session.
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02At a glance
03Original abstract
Murray Sidman's statements regarding variability, experimental control, and generality are interwoven with examples from the literature on conditional discrimination. Sidman's position was that statistical inferences from group studies produce no information about the behavior of individual subjects and that statistical treatment of individual subject data masks variability which may represent conditions that are not controlled. Sidman's work on conditional discrimination provides excellent examples of how complex discriminations should be examined in detail with accuracy levels obtained for each type of discrimination within an experiment. Sidman made important contributions to the foundation of behavior analysis with extensive basic research as well as applications of methods and principles to clinical and educational settings.
Journal of the Experimental Analysis of Behavior, 2021 · doi:10.1002/jeab.660