Assessment and diagnosis of an individual’s problems and concerns represent important precursors to effective counseling. In order to help someone, the counselor must formulate an accurate appraisal of the problems to be targeted in the counseling process. Using diagnostic interviews and psychological tests, mental health professionals hope to develop an accurate diagnosis of the client, to be followed by the application of the appropriate intervention. However, both clinicians and the tests they rely on to render a diagnosis are less than perfect, and the potential to under- or overdiagnose psychological conditions remains. When a clinician or test fails to diagnose a condition when it exists, it is called a false negative. A false positive occurs when the test or clinician identifies a condition when in fact the condition does not exist. The consequences of inaccurate diagnosis can be quite costly. For example, a person might be denied a job in a preemployment screening if the test he or she is given tends to overdiagnose problems. Similarly, an individual might be denied access to a much needed treatment program if the test or interview administered to him or her tends to underdiagnose conditions.
Because mental health professionals often rely heavily on the results of psychological testing in formulating diagnoses, test developers recognize the importance of evaluating tests’ diagnostic accuracy. During the test validation process, it is often the case that a cutting score is determined. This is a score which, when exceeded, leads to the conclusion that the person has the condition measured by the test. Of course, to determine a test’s optimal cutting score, its results must be compared to some real-world criterion. For example, a test assessing depression might be compared to whether or not an individual currently has a diagnosis of depression.
By comparing a test’s outcome to some real-world criterion, it is possible to determine the test’s operating characteristics. These include its hit rate, sensitivity, specificity, positive predictive power, and negative predictive power. The test’s hit rate is the total number of correctly identified cases divided by the total number of cases assessed. Sensitivity is the rate at which the test correctly identifies the existence of disorder, whereas specificity represents the test’s ability to accurately identify the nonexistence of a disorder. Positive predictive power refers to the percentage of individuals identified by the test as having a disorder who actually have the disorder, and negative predictive power refers to the percentage of cases identified as not having the disorder who actually do not. Both tests and clinicians can be evaluated in terms of whether such test operating characteristics concerning the diagnoses they make are optimal.
- Embretson, S. E., & Hershberger, S. L. (Eds.). (1999). The new rules of measurement: What every psychologist and educator should know. Mahwah, NJ: Lawrence Erlbaum.
- Meehl, P. E. (1996). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Lanham, MD: Jason Aronson. (Original work published 1954)