TY - JOUR
T1 - Monte Carlo Analyses for Single-Case Experimental Designs
T2 - An Untapped Resource for Applied Behavioral Researchers and Practitioners
AU - Friedel, Jonathan E.
AU - Cox, Alison
AU - Galizio, Ann
AU - Swisher, Melissa
AU - Small, Megan L.
AU - Perez, Sofia
N1 - Funding Information:
The authors thank Kenneth W. Jacobs for productive conversations about Monte Carlo analyses and randomization tests. The app can be found at https://shiny.georgiasouthern.edu/BA_Monte_Carlo/. Data and programming code at the time of publication are archived on the Open Science Framework (https://osf.io/gqtxz/) and the programming code that is running the app will be maintained on GitHub (https://github.com/jefriedel/BA_Monte_Carlo).
Publisher Copyright:
© 2021, Association for Behavior Analysis International.
PY - 2022/3
Y1 - 2022/3
N2 - Group-based experimental designs are an outgrowth of the logic of null-hypothesis significance testing and thus, statistical tests are often considered inappropriate for single-case experimental designs. Behavior analysts have recently been more supportive of efforts to include appropriate statistical analysis techniques to evaluate single-case experimental design data. One way that behavior analysts can incorporate statistical analyses into their practices with single-case experimental designs is to use Monte Carlo analyses. These analyses compare experimentally obtained behavioral data to simulated samples of behavioral data to determine the likelihood that the experimentally obtained results occurred due to chance (i.e., a p value). Monte Carlo analyses are more in line with behavior analytic principles than traditional null-hypothesis significance testing. We present an open-source Monte Carlo tool, created in shiny, for behavior analysts who want to use Monte Carlo analyses in addition as part of their data analysis.
AB - Group-based experimental designs are an outgrowth of the logic of null-hypothesis significance testing and thus, statistical tests are often considered inappropriate for single-case experimental designs. Behavior analysts have recently been more supportive of efforts to include appropriate statistical analysis techniques to evaluate single-case experimental design data. One way that behavior analysts can incorporate statistical analyses into their practices with single-case experimental designs is to use Monte Carlo analyses. These analyses compare experimentally obtained behavioral data to simulated samples of behavioral data to determine the likelihood that the experimentally obtained results occurred due to chance (i.e., a p value). Monte Carlo analyses are more in line with behavior analytic principles than traditional null-hypothesis significance testing. We present an open-source Monte Carlo tool, created in shiny, for behavior analysts who want to use Monte Carlo analyses in addition as part of their data analysis.
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U2 - 10.1007/s40614-021-00318-7
DO - 10.1007/s40614-021-00318-7
M3 - Article
C2 - 35342867
AN - SCOPUS:85120604268
SN - 2520-8969
VL - 45
SP - 209
EP - 237
JO - Perspectives on Behavior Science
JF - Perspectives on Behavior Science
IS - 1
ER -