About Diagnostic Calculator
This calculator helps you review a diagnostic table from SPSS or any similar analysis tool. It uses the four cell counts. They are true positives, false positives, true negatives, and false negatives. From those values, it builds a broad performance profile. The results describe how well a test detects cases and excludes non cases.
Why Sensitivity Matters
Sensitivity measures the share of real positive cases found by the test. A high value is useful when missed cases are costly. Screening programs often prefer high sensitivity. The tool also reports the false negative rate. That rate shows how often true cases are missed. Together, both values explain detection strength.
Why Specificity Matters
Specificity measures the share of real negative cases correctly ruled out. A high value helps reduce false alarms. Confirmatory tests usually need strong specificity. The false positive rate is shown with it. This makes the trade off easier to understand. You can compare both sides of classification quality.
Predictive Values and Prevalence
Positive predictive value shows the chance that a positive result is truly positive. Negative predictive value shows the chance that a negative result is truly negative. These values depend on case mix. When disease prevalence changes, predictive values can change too. The calculator displays prevalence from your table.
Advanced Summary Measures
Accuracy can look strong when groups are unbalanced. So the calculator also gives balanced accuracy, Youden index, F1 score, likelihood ratios, diagnostic odds ratio, and Matthews correlation coefficient. These measures add depth. They help analysts judge the model from several angles. Likelihood ratios are especially useful for clinical interpretation.
Using Results Carefully
No single number proves that a test is perfect. Review the purpose of the study first. Then compare sensitivity, specificity, and predictive values. Check the sample size behind each result. Small tables can create unstable percentages. Confidence intervals are included as approximate Wilson intervals. Use them as a guide, not as final proof.
Good Reporting Practice
Report the original four counts with every summary. This keeps the analysis transparent. Export the CSV for spreadsheets. Use the PDF option for a quick record. If your SPSS table uses reversed coding, swap positive and negative groups before calculating. Clean labels reduce reporting errors.