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Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual

Art Dowdy1, Kasey Prime1, Corey Peltier2

  • 1Department of Teaching and Learning, College of Education and Human Development, Temple University, Philadelphia, PA USA.

Perspectives on Behavior Science
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Summary
This summary is machine-generated.

Graphical manipulations in functional analysis can affect data interpretation. Board Certified Behavior Analysts showed low alignment with visual inspection criteria, highlighting a need for improved analysis methods in applied behavior analysis.

Keywords:
DPPXYRFunctional analysisGLMMGraph constructionMultielement designOpen scienceVisual analysis

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Area of Science:

  • Applied Behavior Analysis (ABA)
  • Experimental Design
  • Data Visualization

Background:

  • Multielement designs are crucial for evaluating functional analysis outcomes in ABA.
  • Accurate data collection, graphing, and visual analysis are essential for effective intervention decisions.
  • Time-series graphs and visual analysis are standard methods for interpreting functional analysis data.

Purpose of the Study:

  • To assess if the x-to-y axes ratio (DPPXYR) in graphical construction impacts the detection of functional relationships in multielement designs.
  • To evaluate the alignment between visual analyses conducted by Board Certified Behavior Analysts (BCBAs) and the modified visual inspection (MVI) criteria.

Main Methods:

  • Thirty-two multielement design graphs from functional analyses were used to test the effect of DPPXYR manipulation on visual analysts' detection of function.
  • Fifty-nine BCBAs performed visual analysis on data sets, and their decisions were compared against the MVI criteria.
  • Statistical modeling (crossed GLMM) was employed to determine optimal analytical approaches for the data.

Main Results:

  • The graphical construction manipulation of the DPPXYR influenced the detection of functional relationships.
  • A low alignment was observed between the visual analysis decisions of BCBAs and the MVI criteria across the tested data sets.
  • The optimal statistical model identified was a crossed GLMM with random slopes and intercepts, excluding an interaction effect.

Conclusions:

  • Graphical presentation characteristics, such as the DPPXYR, can significantly impact the interpretation of functional analysis data.
  • There is a notable discrepancy between how BCBAs visually analyze functional analysis data and established MVI criteria, suggesting potential inconsistencies in interpretation.
  • The study underscores the importance of transparent data analysis and adherence to standardized visual inspection methods in applied behavior analysis.