Measurement Tool Categories

Throughout the Measurement Library, you will notice each measure is categorized by one of three illustrations: a fruit-bearing tree, a sapling/young tree, or a seedling.

There is a range of evidence available on the reliability and validity of each measure, and we recognize that this evidence may be difficult to interpret. Reliability refers to how consistent and accurate the scores provided by the measure are. Validity refers to the extent to which evidence about the measure supports the intended uses and interpretation of its scores. Evidence of validity should provide whether the measure captures the appropriate, hypothesized construct the measure intended to assess, for its intended uses. Different purposes may require different types and levels of robustness of evidence of reliability and validity. The ratings categories, seedling, sapling, and fully-grown trees, are meant to illustrate the extent of the evidence thus far, and how confident we are that the measure can be used in the specified context and for the specified purpose to provide high-quality data.

The “fully-grown” tree rating signifies a measure that has strong evidence of validity, including clearly defined purposes and evidence of reliable scores, for the specified purpose, usage, and context. The “sapling” rating signifies a measure that shows promising evidence of validity and reliability but requires further evidence, revisions, and testing for replication and use in other samples/contexts for its intended purpose. The “seeding” signifies that the measure is under development and not yet ready for its intended use.

The “Seedling” rating is given when the current evidence of reliability and validity is not sufficient to suggest that the measure is ready for use for its intended purpose(s). Given the sampling design, sample size, and rigor of the psychometric method, there is uncertainty in the replicability and/or accuracy of the evidence if tested with a similar sample. In this case, the ML will publish the evidence report, but will not share actual tools nor the training materials. The ML will also provide developer contact information in case an interested party wants to reach out to the developers for use and adaptation of the tool. 

The “Sapling/young tree” rating is given when there is promising evidence for reliability and validity that the measure is ready for use for its intended purpose(s) with similar samples/contexts. Given the sampling design, sample size, and rigor of the psychometric method, there is some confidence in the replicability and/or accuracy of the evidence if tested with a similar sample. Recommendations for revisions are provided, and we encourage additional testing of the measure and sharing back of the results. The ML will publish the entire packet of the measure that would comprise the measurement tool, the evidence report, and accompanying training materials.

The “fully-grown tree” rating is given when there is strong evidence of the reliability and validity of the measure for its intended purposes, and the measure is ready for use with adaptation in new contexts/samples. Given the sampling design, sample size, and rigor of the psychometric method, we have a high-level of confidence in the quality of the evidence and its successful replication for its intended purposes. Recommendations for modest revisions are provided for use in a similar context and for the same purpose. For use in a new context or for a new purpose, the measure should be adapted, tested, and confirmed for its psychometric evidence with new samples/contexts/purposes. In such a case, we highly encourage the evidence and the specific adaptation it has taken to be shared back with INEE ML, which will then be evaluated for the quality of evidence for a new sample, context, purpose, and adaptation process and could be published on the ML. For this rating, the ML will publish the entire packet of the measure that would comprise the measurement tool, the evidence report, and accompanying training materials.