![]() ![]() The purpose of the present article is to disseminate information on how behavior analysts can efficiently generate similar graphs using the heat-map function in GraphPad Prism. Indeed, up until recently, the authors of this article spent countless hours modifying data tables and painstakingly changing the color of each scatter-plot symbol to reflect the outcomes we sought to convey graphically in some of our publications. Even behavior analysts who have the expertise and resources to do so may struggle to determine the optimal method for creating these types of graphs, especially if they seek the graphical quality desired for publication. However, creating fine-grained visual displays can be daunting for behavior analysts given limited training and resources. Such practices align with the Behavior Analyst Certification Board’s ( 2014) Professional and Ethical Compliance Code for Behavior Analysts, which describes a behavior analyst’s responsibility to clients to (1) identify variables affecting behavior-change programs and (2) graphically display data in a manner that allows for behavior-change program development. These fine-grained analyses can facilitate better prediction and control of behavior by tailoring interventions to address variables undetected by more global measures of responding. This differentiation would result in variability in a coarse measure of compliance but an orderly pattern in responding when analyzed at a finer level. By more closely examining compliance with specific tasks (e.g., visually depicting the child’s performance of addition, subtraction, multiplication, division across educational trials), the behavior analyst might identify that compliance occurs often with certain tasks (e.g., addition, subtraction) but less often with others (e.g., multiplication, division). For example, a scatter-and-line graph depicting the percentage of adult instructions with child compliance may reveal substantial variability during the child’s math class. To detect such variables, behavior analysts sometimes change the level of analysis in their graphs (e.g., evaluating aggregated versus separate topographies of problem behavior during functional analysis, detecting systematically poorer tact performance with certain teaching images ). This allows behavior analysts to better tailor the intervention procedures and improve treatment outcomes. Implications for clinical utility and research production are discussed.Ī strength of behavior analysis is the ability to use single-case research designs to detect variables related to an individual’s responding. In the present article, we provide an overview of how behavior analysts can use GraphPad Prism’s heat-map feature to efficiently populate fine-grained graphs of behavior with data points that are coded automatically (e.g., with categorical colors or gradients). Such analyses can be burdensome to conduct manually (e.g., changing the color of individual data points based on error type), and more efficient methods (e.g., using conditional formatting in Microsoft Excel data tables) might not be conducive for producing publication-quality figures. In a post-hoc analysis, a plot of within-session error patterns can reveal which variables may be contributing to faulty stimulus control. For example, an ongoing plot of when problem behavior occurs across days and times can yield useful information regarding the function(s) of problem behavior. Behavior analysts sometimes consider various forms of data analysis when making clinical decisions and when attempting to illuminate interesting relations in existing datasets.
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