Comments On The Information Base For Selection

lency scale, use the numeric equivalents of verbal characterizations as model inputs.

Subjective versus Objective The distinction between subjective and objective is generally misunderstood. All too often the word objective is held to be synonymous with fact and subjective is taken to be a synonym for opinion—where fact = true and opinion = false. The distinction in measurement theory is quite different, referring to the location of the standard for measurement. A measurement taken by reference to an external standard is said to be "objective." Reference to a standard that is internal to the system is said to be "subjective." A yardstick, incorrectly divided into 100 divisions and labeled "meter," would be an objective but inaccurate measure. The eye of an experienced judge is a subjective measure that may be quite accurate.

Quantitative versus Qualitative The distinction between quantitative and qualitative is also misunderstood. It is not the same as numeric and nonnumeric. Both quantity and quality may be measured numerically. The number of words on this page is a quantity. The color of a red rose is a quality, but it is also a wavelength that can be measured numerically, in terms of microns. The true distinction is that one may apply the law of addition to quantities but not to qualities |66j. Water, for example, has a volumetric measure and a density measure. The former is quantitative and the latter qualitative. Two one-gallon containers of water poured into one container give us two gallons, but the density of the water, before and after joining the two gallons, is still 1.0.

Reliable versus Unreliable A data source is said to be reliable if repetitions of a measurement produce results that vary from one another by less than a prespeci-fied amount. The distinction is important when we consider the use of statistical data in our selection models.

Valid versus Invalid Validity measures the extent to which a piece of information means what we believe it to mean. A measure may be reliable but not valid. Consider our mismarked yardstick 36 inches long but pretending to be a meter. It performs consistently, so it is reliable. It does not, however, match up well with other meter rules, so it would not be judged valid.

To be satisfactory when used in the previous project selection models, the measures may be either subjective or objective, quantitative or qualitative, but they must be numeric, reliable, and valid. Avoiding information merely because it is subjective or qualitative is an error and weakens our decisions. On the other hand, including information of questionable reliability or validity in selection models, even

, though it may be numeric, is dangerous. It is doubly dangerous if decision makers in the organization are comfortable dealing with the selection model but are unaware of the doubtful character of some input data. A condition a colleague has referred to as GIGO—garbage in, gospel out—may prevail.

Comment on Technological Shock

If the parent organization is not experienced in the type of project being considered for selection, performance measures such as time to installation, time to achieve flfc-

80 percent efficiency, cost to install, and the like are often underestimated. It is interesting to observe that an almost certain, immediate result of installing a new, cost-saving technology is that costs rise. Sometimes we blame the cost increases on resistance to change, but a more sensible explanation is that when we alter a system, we disturb it and it reacts in ways we did not predict. A steelmaker recalling the installation of the then new technology for manufacturing tinplate by electrolysis remarked: "We discovered and installed the world's first electrolytic method for making scrap. It took a year before we had that line running the way it was designed."

Of course, if the organization is experienced, underestimation is not likely to be a serious problem. The Reliance Electric Company undertook several "18-month" plant construction projects that they predicted, accurately, would require 36 months to build from decision to the point when the plant was capable of operating at or above three-fourths capacity. (Note the potential for ethical problems here.)

To the extent possible, past knowledge of system actions and reactions should be built into estimates of future project performance.

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