CHAPTER 8 The Transition: From What to Measure to How to Measure
If you’ve applied the lessons of the previous chapters to your measurement problem, you’ve defined the issue in terms of what decision it affects and how you observe it, you’ve quantified your uncertainty about it, and you’ve computed the value of additional information. Based on the information values, you selected what to measure from among all of the variables in the decision and you have a good idea of the level of effort that would be appropriate. All of that was really what you do before you begin measuring. Now we need to figure out how to reduce our uncertainty further—in other words, to conduct the actual measurement.
It’s time to introduce some concepts behind powerful and practical empirical methods. Given the way we have defined measurement, the oft-heard phrase “empirical measurement” is redundant. Empirical refers to the use of observation as evidence for a conclusion. (You might also hear the equally redundant phrase “empirical observation.”) Empirical methods are formal, systematic approaches for making observations to avoid or at least reduce certain types of errors that observations (and observers) are likely to have. And observation is not limited to sight, although this is a commonly assumed notion. Observation may not even be direct; it may be augmented by the use of measurement instruments. This is, in fact, almost always the case in the modern physical sciences as well as social sciences.