Data And Measurement Decisions 1

DATA AND MEASUREMENT DECISIONS 1 Data and Measurement Decisions Student’s Name

This article is the third element of Liam’s case study. Liam is a 12-year-old boy diagnosed with ASD or autism spectrum disorder while still young. He undergoes at least three refusal behaviors during his school day. Liam’s refusal behaviors include looking away from a class assignment or teacher for about 12 seconds, verbally refusing and ignoring the instructor’s directions, and occupying himself with other activities instead of classwork. These refusal behaviors are facilitated by tiredness, as Liam stayed up the previous night preoccupied with other activities.

His refusal behaviors are socially significant to modify because they negatively affect his academic performance, which could impact his adulthood. In the previous article, an intervention was chosen to improve Liam’s on-task behavior and reduce his refusal behaviors. The selected intervention is Differential Reinforcement of Alternative Behavior (DRA), which supports alternative behaviors while making targeted refusal behaviors extinct (Miltenberger, 2016).

This paper aims to evaluate the measurement, data display, and data-based decisions that will guide a study on Liam’s case. Effective measurement should be observable and measurable to establish a baseline for comparison post-intervention. Various measurement modalities in applied behavior analysis (ABA) include frequency, sampling response for momentary time sampling, partial interval recording, whole interval recording, duration, inter-response time, and latency. The decision-making model involved a quiz-based assessment to identify the most suitable measurement for Liam’s refusal behavior (LeBlanc, Raetz, Sellers, & Carr, 2016).

For example, the researcher indicated that observable behavior could be recorded as a yes/no answer but chose frequency measurement because Liam’s refusal behavior was not constant. Since Liam’s refusals did not occur continuously, the frequency method was appropriate to count the number of refusals during observation periods (Miltenberger, 2016). The researcher decided to measure how often Liam refused during school hours, considering environmental factors such as task difficulty, instruction settings, noise levels, and available resources for data collection.

The choice of measurement was influenced by Liam's environment, which includes small group instructions, noise levels, and resource availability like laptops, pencils, and datasheets. Frequency measurement was deemed most suitable because it captures variation in occurrence and duration of refusal behaviors across different contexts, such as independent reading or group work. It also aligns with resource constraints and staff's ability to monitor behavior efficiently without excessive interruption.

Regarding data display, a visual representation of behavior is essential for interpreting the effectiveness of interventions. Different types of graphs include bar graphs, scatter plots, semi-logarithmic charts, cumulative records, and line graphs. Line graphs are particularly useful for illustrating trends over time, showing changes in behavior across phases such as baseline and intervention (Kubina, Kostewicz, Brennan, & King, 2017).

The researcher selected a line graph to depict Liam’s refusal behavior over time, plotting frequency data on days before and after implementing the DRA intervention. The graph indicates a downward trend in refusal instances across intervention days, suggesting that DRA effectively reduced Liam’s refusal behaviors. Such visual data aids in making data-based decisions, like continuing, modifying, or withdrawing specific strategies (Hojnoski, Gischlar, & Missall, 2009).

Data-based decision making relies on the accurate, reliable, and valid interpretation of graphical data. Reliability refers to the consistency of measurement, accuracy to how well data reflect observed behaviors, and validity to the appropriateness of the measurement for the behavioral goals (Cooper, Heron, & Heward, 2020). During baseline, Liam’s refusal behaviors fluctuated between four and three occurrences, demonstrating stability and reliability. The intervention phase showed a consistent decrease, supporting the intervention’s effectiveness.

In conclusion, utilizing a line graph to display frequency data provides a clear, visual means to determine the impact of DRA on Liam’s refusal behaviors. The decline in refusals over time supports the conclusion that DRA is an effective intervention. Data-driven decisions—whether to maintain, adapt, or cease intervention—are grounded in these visual and quantitative analyses, ensuring targeted and effective behavioral management.

References

• Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3rd ed.). Pearson Education Inc.
• Hojnoski, R. L., Gischlar, K. L., & Missall, K. N. (2009). Improving child outcomes with data-based decision making: Graphing data. Young Exceptional Children, 12(4), 15-30. https://doi.org/10.1177/
• Kubina, R. M., Jr, Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A critical review of line graphs in behavior analytic journals. Educational Psychology Review, 29(3).
• LeBlanc, L. A., Raetz, P. B., Sellers, T. P., & Carr, J. E. (2016). A proposed model for selecting measurement procedures for the assessment and treatment of problem behavior. Behavior Analysis in Practice, 9(1), 77-83. https://doi.org/10.1007/s
• Miltenberger, R. G. (2016). Behavior modification: Principles and procedures (6th ed.). Cengage Learning.