Attribute Agreement Analysis Interpretation
First, the analyst should determine that there is indeed attribute data. One can assume that the assignment of a code – that is, the division of a code into a category – is a decision that characterizes the error with an attribute. Either a category is correctly assigned to an error, or it is not. Similarly, the appropriate source location is either attributed to the defect or not. These are „yes” or „no” and „correct allocation” or „wrong allocation” answers. This part is pretty simple. In addition to the sample size problem, logistics can ensure that listeners do not remember the original attribute they attributed to a scenario when they see it for the second time, also a challenge. Of course, this can be avoided a bit by increasing the sample size and, better yet, waiting a while before giving the scenarios to the evaluators a second time (perhaps one to two weeks). Randomization of transitions from one audit to another can also be helpful. In addition, evaluators tend to work differently when they know they are being examined, so that the fact that they know it is a test also distorts the results.
Hiding this in one way or another can help, but it`s almost impossible to achieve, despite the fact that it borders on the inthesis. And in addition to being at best marginally effective, these solutions increase an already demanding study with complexity and time. Despite these difficulties, performing an attribute analysis on bug tracking systems is not a waste of time. In fact, it is (or may be) an extremely informative, valuable and necessary exercise. The analysis of attributes should only be applied with caution and with a certain focus. In this example, a repeatability assessment is used to illustrate the idea, and it also applies to reproducibility. The fact is that many samples are needed to detect differences in an analysis of the attribute, and if the number of samples is doubled from 50 to 100, the test does not become much more sensitive. Of course, the difference that needs to be identified depends on the situation and the level of risk that the analyst is prepared to bear in the decision, but the reality is that in 50 scenarios, it is difficult for an analyst to think that there is a statistical difference in the reproducibility of two examiners with match rates of 96 percent and 86 percent. With 100 scenarios, the analyst will not be able to see any difference between 96% and 88%. Repeatability and reproducibility are components of accuracy in an analysis of the attribute measurement system, and it is advisable to first determine if there is a precision problem.
This means that before designing an attribute contract analysis and selecting the appropriate scenarios, an analyst should urgently consider monitoring the database to determine if past events have been properly coded.