Metric Types

One of the major benefits of implementing Precision Teaching (also known as Frequency Based Instruction) is its consistent underlying measurement technique. This consistent measurement technique leads to greater data integrity and, as a result, the potential for greater insights from that data.

Precision Teaching also offers a variety of metrics calculated from measurement data in each condition. These metrics track various facets of each intervention, such as the direction, magnitude of growth, and variability of the measurement data collected. In addition, these metrics can be used to compare adjacent interventions to understand whether a change in the intervention has led to stronger growth.

Precision Teaching metrics are bucketed into two general groups:

  • Within-condition metrics: Provide an understanding of the performer’s progress in their current intervention (“within the current condition”) and can be compared to an organization’s standards of progress to understand whether the current intervention is effective or not.
    • For example, if an organization wants to achieve a x1.25 accel celeration for a specific type of pinpoint and your accel celeration is x1.38, this means the performer is achieving progress above the standard of progress for the organization.
    • The performer’s metrics can then be an input to the decision to continue the current intervention, or if progress is below your organization’s standard, change the intervention.
    • In the figure below, each of the 3 conditions represents within-condition data that is used to calculate within-condition metrics.


  • Between-condition metrics: Provide an understanding of the current intervention’s progress (current condition) compared to the immediately-preceding intervention’s progress (previous condition). This helps answer if the current intervention has produced better or worse progress in comparison to the last intervention.
    • These metrics help practitioners evaluate the effects of the new condition.
    • They shed light on the magnitude of progress in the current condition vs. the preceding condition which can help in decision making.
      • For example, the current condition is not producing as well of an effect as the previous condition, or in comparison to standards of progress in the organization.
    • In the figure above, measurement data and metrics calculated from conditions “Frequency Building” and “Double Digit”, as well as conditions “Single Digit” and “Frequency Building” are used to calculate between-condition metrics. These metrics would tell us the effect of changing the intervention from “Frequency Building” to “Double Digit” and “Single Digit” to “Frequency Building”, respectively.
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