Screening procedures aim at identifying persons who have a certain (binary) characteristic. They are often used to identify people at risk who later develop mental disorders, diseases or school problems. For the evaluation of the screenings there are a number of characteristic values, of which sensitivity and specificity are the best known. They show how many people were correctly identified by the test as risk and how many as non-risk persons.
Besides these basic data, however, there are a number of other important data, such as the positive and negative predictive values or the relative increase in the hit rate compared to the random hit rate (RIOC); further information and a detailed description of the various parameters can be found in Marx & Lenhard, 2010; see also the glossary).
You can use the table to determine various characteristic values for screening procedures. Please enter the absolute case numbers in the table cells.
Area proportions see graphic please fill in absolute numbers |
Predictor (e. g. from a screening) |
||
positive result | negative result | ||
Criteria | not affected | b (false positive) |
d (correct negative) |
affected | a (correct positive) |
c (false negative) |
Quality indicators of the screening: | |
Sensitivity or Recall | |
Specificity | |
Positive predictive value or Precision | |
Negative predictive value | |
Contingency Coefficient r_{Phi} | |
Hit ratio or Accuracy | |
Random hit ratio | |
Relative Improvement Over Chance (RIOC) |
Ok, let's turn the logic around and look at the numbers of correct positive and false positive diagnoses that result. If the base rate is very low, then screening very quickly reaches its limits and it is then primarily used to pre-select people for further diagnostics. Let us take COVID19 as an example and a fictitious incidence of 100 / 100 000 persons for infections in the last 7 days, which corresponds to a baserate of 0.1%. Good rapid tests have at least a specificity of 99.5% and a sensitivity of 95%. What number of false positive diagnoses results if 1000 people are examined with a rapid test in this infection situation?
Baserate between 0 and 100 in percent |
Sensitivity between 0 and 100 in percent |
Spezifity between 0 and 100 in percent |
Number of persons |
Results of the screening: | |
False Positive | |
False Negative | |
Correct Positive | |
Correct Negative | |
Share of false positives in all positive results (in percent) | |
Share of false-negative results in all negative results (percent) |
Glossary:
The online tool is available as an Excel sheet and as Source-Code in Java under the General Public License (GPL):
Citeable: Lenhard, W. & Lenhard, A. (2014). Calculation of test quality criteria for screenings. available: http://www.psychometrica.de/testkennwerte.html. Psychometrica.