Computerized programs have been specifically developed in the field of applied behavior analysis for the purpose of automating data collection. Although they can potentially improve practicality of data collection for applied researchers and clinicians, program features of existing computerized programs do not include graphs and data interpretation generated in real time. We developed the Problem Behavior Multilevel Interpreter (PB.MI), which is designed to (a) allow for ongoing visual analysis of data displayed in real time and (b) support visual analysis with a computerized interpretation of functional control. The program was intended to be used during the functional analysis of problem behavior, specifically the single-session, interview-informed synthesized contingency analysis. In this article, we describe the program’s functioning abilities and how we validated those abilities. In addition, we discuss the PB.MI program’s practical utility.
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Bailey, J. D., Baker, J. C., Rzeszutek, M. J., & Lanovaz, M. J. (2021). Machine learning for supplementing behavioral assessment. Perspectives on Behavior Science. Advanced online publication. https://doi.org/10.1007/s40614-020-00273-9
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Bourret, J. C., & Pietras, C. J. (2013). Visual analysis in single-case research. In G. J. Madden, W. V. Dube, T. D. Hackenberg, G. P. Hanley, & K. A. Lattal (Eds.), APA handbook of behavior analysis (pp. 191–218). American Psychological Association.
Bullock, C. E., Fisher, W. W., & Hagopian, L. P. (2017). Description and validation of a computerized behavioral data program: “BDataPro.”. The Behavior Analyst, 40(1), 275–285. https://doi.org/10.1007/s40614-016-0079-0
Coffey, A. L., Shawler, L. A., Jessel, J., Nye, M. L., Bain, T. A., & Dorsey, M. F. (2020). Interview-informed synthesized contingency analysis (IISCA): Novel interpretations and future directions. Behavior Analysis in Practice, 13(1), 217–225. https://doi.org/10.1007/s40617-019-00348-3
Danov, S. E., & Symons, F. J. (2008). A survey evaluation of the reliability of visual inspection and functional analysis graphs. Behavior Modification, 32(6), 828–839. https://doi.org/10.1177/0145445508318606
Dowdy, A., Jessel, J., Saini, V., & Peltier, C. (in press). Structured visual analysis of single case experimental design data: Developments and technological advancements. Journal of Applied Behavior Analysis.
Fisher, W. W., Kelley, M. E., & Lomas, J. E. (2003). Visual aids and structured criteria for improving visual inspection and interpretation of single-case designs. Journal of Applied Behavior Analysis, 36(3), 387–406. https://doi.org/10.1901/jaba.2003.36-387
Hagopian, L. P., Fisher, W. W., Thompson, R. H., Owen-De Schryver, J., Iwata, B. A., & Wacker, D. P. (1997). Toward the development of structured criteria for interpretation of functional analysis data. Journal of Applied Behavior Analysis, 30(2), 313–326. https://doi.org/10.1901/jaba.1997.30-313
Hanley, G. P., Jin, C. S., Vanselow, N. R., & Hanratty, L. A. (2014). Producing meaningful improvements in problem behavior of children with autism via synthesized analyses and treatments. Journal of Applied Behavior Analysis, 47(1), 16–36. https://doi.org/10.1002/jaba.106
Jessel, J., Hanley, G. P., & Ghaemmaghami, M. (2016). Interview-informed synthesized contingency analyses: Thirty replications and reanalysis. Journal of Applied Behavior Analysis, 49(3), 576–595. https://doi.org/10.1002/jaba.316
Jessel, J., Hanley, G. P., Ghaemmaghami, M., & Metras, R. (2019). An evaluation of the single-session interview-informed synthesized contingency analysis. Behavioral Interventions, 34(1), 62–78. https://doi.org/10.1002/bin.1650
Jessel, J., Metras, R., Hanley, G. P., Jessel, C., & Ingvarsson, E. T. (2020a). Does analytic brevity result in loss of control? A consecutive case series of 26 single-session interview-informed synthesized contingency analyses. Behavioral Interventions, 35(1), 145–155. https://doi.org/10.1002/bin.1695
Jessel, J., Metras, R., Hanley, G. P., Jessel, C., & Ingvarsson, E. T. (2020b). Evaluating the boundaries of analytic efficiency and control: A consecutive controlled case series of 26 functional analyses. Journal of Applied Behavior Analysis, 53(1), 25–43. https://doi.org/10.1002/jaba.544
Lanovaz, M. J., Giannakakos, A. R., & Destras, O. (2020). Machine learning to analyze single-case data: A proof of concept. Perspectives on Behavior Science, 43(1), 21–38. https://doi.org/10.1007/s40614-020-00244-0
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/BM.2012.031
Project Jupyter (2021) [Computer software]. Retrieved from https://jupyter.org/index.html
Roane, H. S., Fisher, W. W., Kelley, M. E., Mevers, J. L., & Bouxsein, K. J. (2013). Using modified visual-inspection criteria to interpret functional analysis outcomes. Journal of Applied Behavior Analysis, 46(1), 130–146. https://doi.org/10.1002/jaba.13
Saini, V., Fisher, W. W., & Retzlaff, B. J. (2018). Predictive validity and efficiency of ongoing visual-inspection criteria for interpreting functional analyses. Journal of Applied Behavior Analysis, 51(2), 303–320. https://doi.org/10.1002/jaba.450
Scruggs, T. E., Mastropieri, M. A., & Casto, G. (1987). The quantitative synthesis of single-subject research: Methodology and validation. Remedial and Special Education, 8(2), 24–33. https://doi.org/10.1177/074193258700800206
Turgeon, S., & Lanovaz, M. J. (2020). Tutorial: Applying machine learning in behavioral research. Perspectives on Behavior Science, 43(4), 697–723. https://doi.org/10.1007/s40614-020-00270-y
Unity 3D, Unity Real-Time Development Platform, URL: https://unity.com/ (n.d.)
Zheng, Z. K., Staubitz, J. E., Weitlauf, A. S., Staubitz, J., Pollack, M., Shibley, K., Hopton, M., Martin, W., Swanson, A., Juarez, P., Warren, Z. E., & Sarkar, N. (2021). A predictive multimodal framework to alert caregivers of problem behaviors for children with ASD (PreMAC). Sensors, 21(2), 370–389. https://doi.org/10.3390/s21020370
Z. Kevin Zheng, John Staubitz, and Joshua Jessel made equal contributions to this work.
We would like to thank Matt Santini, Jessica Moses, and Victoria Stewart for their assistance in scoring sessions.
There is no funding to report.
For this type of study, formal consent is not required.
This article does not contain any studies with human participants performed by any of the authors.
The PB.MI program was developed for use in computers and tablets operating on a Windows system. The program is currently in the alpha test phase and is not available to the public; however, versions of the program may be available for use, free of charge, upon request. Any inquiries regarding the PB.MI program can be sent to Z. Kevin Zheng.
Conflict of interest
John Staubitz declares no conflict of interest. Z. Kevin Zheng declares no conflict of interest. Joshua Jessel has a part-time consultative role at FTF Behavioral Consulting, Worcester, MA, USA. Tess Fruchtman declares no conflict of interest. Nilanjan Sarkar declares no conflict of interest.
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Zheng, Z.K., Staubitz, J., Jessel, J. et al. Validating a Computerized Program for Supporting Visual Analysis During Functional Analysis: The Problem Behavior Multilevel Interpreter (PB.MI). Behav Analysis Practice (2021). https://doi.org/10.1007/s40617-021-00656-7
- automated data interpretation
- computer-generated graphs
- data collection
- functional analysis
- synthesized contingencies