Hello Nneka,

Sensitivity reflects the proportion of true positives (on some criterion variable) that a given indicator flags as positive. A value of 1 indicates that every true positive case was so identified/flagged by your indicator variable.

This could occur if:

1. The indicator scores are genuinely indicative of and highly corresponding to true status (this is the ideal case; the others which follow are not).

2. All cases were true positives, in which case the indicator scores don't necessarily have to relate to true status.

3. You have a very small sample size.

4. The indicator variable is actually redundant with the true status measurement (for example: if the indicator variable was BMI score and the true status was whether or not a case was classified as morbidly obese).

Good luck with your work.

@David Morse

Thank you for this. The third option is true in the work I'm doing.

Less than one means parameters are robust. Greater than one means it is a sensitive parameter i.e one that elicits a large change in model output for a small change in a given input parameter :)

@Andrew Paul

Thank you so much. Your answer gives more insight to my study.