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Interpreting functional analysis outcomes using automated nonparametric statistical analysis
Author(s) -
Hall Scott S.,
Pollard Joy S.,
Monlux Katerina D.,
Baker Joseph M.
Publication year - 2020
Publication title -
journal of applied behavior analysis
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 76
eISSN - 1938-3703
pISSN - 0021-8855
DOI - 10.1002/jaba.689
Subject(s) - nonparametric statistics , functional data analysis , rank (graph theory) , computer science , interpretation (philosophy) , function (biology) , statistical analysis , data mining , statistics , artificial intelligence , machine learning , mathematics , combinatorics , evolutionary biology , biology , programming language
Current methods employed to interpret functional analysis data include visual analysis and post-hoc visual inspection (PHVI). However, these methods may be biased by dataset complexity, hand calculations, and rater experience. We examined whether an automated approach using nonparametric rank-based statistics could increase the accuracy and efficiency of functional analysis data interpretation. We applied Automated Nonparametric Statistical Analysis (ANSA) to a sample of 65 published functional analyses for which additional experimental evidence was available to verify behavior function. Results showed that exact behavior function agreement between ANSA and the publications authors was 83.1%, exact agreement between ANSA and PHVI was 75.4%, and exact agreement across all 3 methods was 64.6%. These preliminary findings suggest that ANSA has the potential to support the data interpretation process. A web application that incorporates the calculations and rules utilized by ANSA is accessible at https://ansa.shinyapps.io/ansa/.