Guest Blog Written by Sal Ruiz, Ph.D, BCBA
Functional Analysis (FA) gives us an assessment to determine the function of challenging behavior. Without the ability to determine function, behavior analysts would have a difficult time creating and sustaining behavior change. Procedurally, FA includes the use of motivating operation, elimination of confounding variables in the environment, and the use of visual analysis as a decision-making tactic to determine function (Neef & Peterson, 2007). FA’s have seen widespread use since 1982 (see Hanley et al., 2003 for a comprehensive review). Since that time variations have been developed that modify procedures and measurement of target behavior. For example, the use of Trial Based Functional Analysis (TBFA) has become more prevalent in the research literature (see Rispoli et al., 2014 for a comprehensive review). TBFA addresses many potential challenges that may prevent professionals in using FA to determine function. Visually, TBFA typically uses bar graphs to represent data.
Visual Analysis has long been a hallmark of single subject work (Kazdin, 1982). The use of trend, level, and stability allows the user to determine if a functional relation has occurred. FA relies on level as a primary decision-making tactic. By examining if one condition occurs at a higher level than the other conditions behavior analysts can develop interventions that will create long lasting change and are matched to function.
However, behavior may not always be the product of one function. For example, behavior can have multiply maintained or undifferentiated results. When behavior is multiply maintained how do we know how much more one function is responsible for the occurrence of behavior than another? If visual analysis produces undifferentiated results, what happens next?
Undifferentiated Results pose a different set of problems. When the results of the assessment do not provide guidance on how to proceed, what should a practicing behavior analyst do? One option is a pairwise comparison, which involves conducting additional sessions that compare one function against the control condition. Another option would allow for a manipulation of procedures. Neither of these options guarantee the ability to detect function. Undifferentiated results could occur for several reasons, however, something needs to occur.
The Functional Analysis Celeration Chart (FACC) is a standardized graphical display that can show data in two ways. First, is a sequential display of the data (See Figure 3). The sequential view allows for the ability to detect carry over effects through visual analysis. Second, is condition grouped (See Figure 4). The condition grouped display allows for the ability to easily visualize level and bounce. However, carry-over effects will not be detected. Each view allows for different depictions of the data, while maintaining the ability to quantify the data.
Quantification of FA Data
The FACC may help when FA data show multiply maintained or undifferentiated results. The FACC allows for the use of quantification of data to help assist in decision making. The FACC relies on level as the primary decision-making tactic and can compare each condition against the control, as well as the other test conditions. Quantification adds a supplementary check to visual analysis, regardless of the results of the assessment.
For example, matching the quantification of the level with visual analysis adds confidence to the decision of what function is maintaining the behavior. With multiply maintained behavior, the quantification can help distinguish what behavior is occurring more frequently and how much more frequently one behavior occurred than another per condition. Undifferentiated results can now quantify behavior and help with decision making.
The use of visual analysis and level can help determine function in situations where visual analysis does not work well. Further, the use of bounce can act as another check to determine function. On the SCC, bounce demonstrates control of a behavior. If a behavior has tight bounce, then we can assume that the behavior is controlled (Kubina & Yurich, 2012). Applying that principle to the FACC can help eliminate a possible maintaining function. If the bounce is variable then we can’t say that the behavior is coming into contact with the correct contingencies. The organism is looking for reinforcement and does not know how to obtain it.
In short, quantification is a powerful tool that can provide assistance when visual analysis is not enough. The ability to quantify can save time, increase confidence in decisions, and minimize exposure to reinforcing contingencies. The FACC can pair with variations of FA. Pairing a proven assessment and the power of quantification has the potential to help our learners decrease their challenging behaviors and teach a replacement behavior that will allow them to access the same reinforcement.
Hanley, G. P., Iwata, B. A., & McCord, B. E. (2003). Functional analysis of problem behavior: A review. Journal of Applied Behavior Analysis, 36(2), 147-185.
Kazdin, A.E. (1982). Single-case research designs: Methods for clinical and applied settings. New York, NY: Oxford University Press.
Kubina, R. M., & Yurich, K. K. (2012). Precision Teaching Book. Lemont, PA: Greatness Achieved Publishing Company.
Lee, R. (2009) Functional analysis of problem behavior. Retrieved from http://blog.qsac.com/functional-analysis-of-problem-behavior/
Neef, N. A., & Peterson, S. M. (2007). Functional behavior assessment. In Cooper J. O., Heron, W. L., & Heward, T. L. (Eds.). Applied behavior analysis, (2nd ed). Upper Saddle River, NJ: Pearson Prentice Hall.
Rispoli, M., Ninci, J., Neely, L., & Zaini, S. (2014). A systematic review of trial-based functional analysis of challenging behavior. Journal of Developmental Physical Disability, 26, 271-283. doi: 10.1007/s10882-013-9363-z
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, 130-146. doi: 10.1002/jaba.13
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