C-11: Interpret graphed data ©
Target Terms: Level, Variability, Trend
Definition: The value of a data point along the x-axis of a graph.
Example in clinical context: A behavior analyst is conducting visual analysis of a client’s target behavior of head to wall self-injury. The behavior analyst determines the level by locating the number along the y-axis to the data points within the graph. The behavior analyst observes that the level of data points are located around the 10% interval along the y-axis.
Why it matters: Examining the level of a data point is a skill in visual analysis that allows the behavior analyst to determine how much or little a behavior has changed.
Definition: The extent to which the data move around on the graph.
Example in clinical context: A behavior analyst is conducting visual analysis of a client’s target behavior of dropping to the floor. The data path is scattered all around the graph. This shows a high degree of variability in the client’s dropping behavior.
Why it matters: Variability demonstrates the consistency to which change is taking place. A high variability may demonstrate a low degree of control of an intervention condition, whereas a low variability may demonstrate a high degree of control of an intervention condition. (In other words, if data points are all over the place, there is probably something else going on that has not been accounted for yet.)
Definition: The overall direction of the data path.
Example in clinical context: A behavior analyst is conducting visual analysis on a client’s hitting behavior. They observe that the data path is increasing in trend.
Why it matters: Examining trend provides us with information about the “bigger picture” of where a behavior is heading based on past responding. It is helpful as part of intervention planning and evaluation.