Three demands of meaningful measurement

The core issue in measurement is meaningfulness. There are three major aspects of meaningfulness to take into account in measurement. These have to do with the constancy of the unit, interpreting the size of differences in measures, and evaluating the coherence of the units and differences.

First, raw scores (counts of right answers or other events, sums of ratings, or rankings) do not stand for anything that adds up the way they do (see previous blogs for more on this). Any given raw score unit can be 4-5 times larger than another, depending on where they fall in the range. Meaningful measurement demands a constant unit. Instrument scaling methods provide it.

Second, meaningful measurement requires that we be able to say just what any quantitative amount of difference is supposed to represent. What does a difference between two measures stand for in the way of what is and isn’t done at those two levels? Is the difference within the range of error, and so random? Is the difference many times more than the error, and so repeatedly reproducible and constant? Meaningful measurement demands that we be able to make reliable distinctions.

Third, meaningful measurement demands that the items work together to measure the same thing. If reliable distinctions can be made between measures, what is the one thing that all of the items tap into? If the data exhibit a consistency that is shared across items and across persons, what is the nature of that consistency? Meaningful measurement posits a model of what data must look like to be interpretable and coherent, and then it evaluates data in light of that model.

When a constant unit is in hand, when the limits of randomness relative to stable differences are known, and when individual responses are consistent with one another, then, and only then, is measurement meaningful. Inconstant units, unknown amounts of random variation, and inconsistent data can never amount to the science we need for understanding and managing skills, abilities, health, motivations, social bonds, and environmental quality.

Managing our investments in human, social, and natural capital for positive returns demands that meaningful measurement be universalized in uniformly calibrated and accessible metrics. Scientifically rigorous, practical, and convenient methods for setting reference standards and making instruments traceable to them are readily available.

We have the means in hand for effecting order-of-magnitude improvements in the meaningfulness of the measures used in education, health care, human and environmental resource management, etc. It’s time we got to work on it.

We are what we measure. It’s time we measured what we want to be.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
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