give a range of values that are typically collapsed into a few categories for decision purposes, such as “significant response,” “no significant response,” and an intermediate category called “inconclusive.”

There are two distinct aspects to accuracy. One is sensitivity. A perfectly sensitive indicator of deception is one that shows positive whenever deception is in fact present: it is a test that gives a positive result for all the positive (deceptive) cases; that is, it produces no false negative results. The greater the proportion of deceptive examinees that appear as deceptive in the test, the more sensitive the test. Thus, a test that shows negative when an examinee who is being deceptive uses certain countermeasures is not sensitive to deception. The other aspect of accuracy is specificity. An indicator that is perfectly specific to deception is one that always shows negative when deception is absent (is positive only when deception is present). It produces no false positive results. The greater the proportion of truthful examinees who appear truthful on the test, the more specific the test. Thus, a test that shows positive when a truthful examinee is highly anxious because of a fear of being falsely accused is not specific to deception because it also indicates fear. Box 2-1 gives precise definitions of sensitivity, specificity, and other key terms relevant to measuring the accuracy of polygraph testing. It also shows the quantitative relationships among the terms.

The false positive index (FPI) and the positive predictive value (PPV) are two closely related measures of test performance that are critical to polygraph screening decisions.6 The FPI is the ratio of false positives to true positives and thus indicates how many innocent examinees will be falsely implicated for each spy, terrorist, or other major security threat correctly identified. The PPV gives the probability that an individual with a deceptive polygraph result is in fact being deceptive. The two are inversely related: PPV = 1/(1 + FPI); the lower the PPV, the higher the FPI.

Much research on diagnostic accuracy draws on a general theory of signal detection that treats the discrimination between signals and noise. Signals are “positive” conditions—the polygraph test readings of respondents who are being deceptive, for example. Noise is any “negative” event that may mimic and be difficult to distinguish from a signal—such as the polygraph test readings of respondents who are not being deceptive (Peterson, Birdsall, and Fox, 1954; Green and Swets, 1966). Developed for radar and sonar devices during and following World War II, signal detection theory has since been applied extensively in clinical medicine (now upward of 1,000 articles per year) and also in nondestructive testing, information retrieval, aptitude testing, weather forecasting, cockpit warning systems, product inspection, survey research, clinical psychology, and other settings (see Swets, 1996).

In the model of diagnosis that is provided by the theory, a diagnosis



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