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

Dowdy, Art
; 
Prime, Kasey
; 
Peltier, Corey
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Journal article
Date
2024-04-30
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Department
Teaching and Learning
Subject
DPPXYR
 Functional analysis
 GLMM
 Graph construction
 Multielement design
 Open science
 Visual analysis
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https://scholarshare.temple.edu/handle/20.500.12613/11948
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DowdyEtAl-JournalArticle-2024-04.pdf
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DOI
https://doi.org/10.1007/s40614-024-00406-4
Abstract
Multielement designs are the quintessential design tactic to evaluate outcomes of a functional analysis in applied behavior analysis. Protecting the credibility of the data collection, graphing, and visual analysis processes from a functional analysis increases the likelihood that optimal intervention decisions are made for individuals. Time-series graphs and visual analysis are the most prevalent method used to interpret functional analysis data. The current project included two principal aims. First, we tested whether the graphical construction manipulation of the x-to-y axes ratio (i.e., data points per x- axis to y-axis ratio [DPPXYR]) influenced visual analyst’s detection of a function on 32 multielement design graphs displaying functional analyses. Second, we investigated the alignment between board certified behavior analysts (BCBAs; N = 59) visual analysis with the modified visual inspection criteria (Roane et al., Journal of Applied Behavior Analysis, 46, 130-146, 2013). We found that the crossed GLMM that included random slopes, random intercepts, and did not include an interaction effect (AIC = 1406.1, BIC = 1478.2) performed optimally. Second, alignment between BCBAs decisions and the MVI appeared to be low across data sets. We also leveraged current best practices in Open Science for raw data and analysis transparency.
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Citation
Dowdy, A., Prime, K. & Peltier, C. Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual Analysis. Perspect Behav Sci 47, 499–521 (2024). https://doi.org/10.1007/s40614-024-00406-4
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Springer Science and Business Media
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Perspectives on Behavior Science, Vol. 47, Iss. 2
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