Many organizations measure attributes of populations and processes in the hope of guiding the organization towards stated goals. But some of these measurements yield misleading data, for a variety of reasons. For instance, we sometimes use surveys that require respondents to supply their subjective judgments.
An example of subjective judgment: "Rate your subordinate's ability to work with others on a scale from 1 to 5." Such attributes cannot actually be measured. If you try to measure them, by means of, say, surveys — even anonymous surveys — you actually measure the rating that people enter on the form. That rating might or might not reflect what you think you're measuring.
Some measurements do work. Here are some properties of useful measurements.
- The organization must have in mind some adjustment of operations that it would make in response to the results. If you don't use the results of the measurement, why are you measuring it at all?
- Boolean, numeric, or member of a defined list
- The answer to the question "What is the observed value of this metric?" must be true or false; a number; or an element of a defined list. For instance, did we complete the project on time? If we were late, how late were we?
- Determining the observed value of the measurement shouldn't involve subjective judgment. For example, the number of malware incidents per month, or the number of timecard hours or hours "badged in" per employee per month.
- The people who provide the data, or whose activities the data describes, should be confident that the data they enter cannot be traced to them personally. This enhances (but does not ensure) the honesty of submissions, especially when the submitted data conveys bad news.
- Out of awareness
- If you don't use the
results of the measurement,
why are you measuring
it at all?The people whose activity is being measured should be unaware that a measurement is taking place. This limits the impact of the so-called Hawthorne Effect. See "Getting Around Hawthorne," Point Lookout for October 2, 2002, for more.
- The measurement process itself should be measured, to determine its quality. Measures that are helpful include traceability checks, the probability of the data actually being used, and multiple data collections to evaluate precision.
- Fraud resistant
- Sometimes people attempt to achieve desired measurement results by means of fraud. They conceal, misrepresent, spin, or do whatever is necessary to get the results they want or the results they believe the measurer wants. Plan enforcement actions in advance of the data collection, and establish organizational policy regarding measurement fraud.
How many of the measurements you now make meet these criteria? Most important, how many measurements do you actually use? If you eliminate those you never use, you might find resources that you can use to improve the rest. Top Next Issue
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Forthcoming issues of Point Lookout
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- Some cognitive biases can cause people in collaborations to have inaccurate understandings of what each other is doing. Confirmation bias and self-serving bias are two examples of cognitive biases that can contribute to disjoint awareness in some situations. Available here and by RSS on January 22.
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