As performing an attribute analysis can be tedious, costly and generally uncomfortable for all stakeholders (the analysis is simple versus execution), it is best to take a moment to really understand what should be done and why. Modern statistical software such as Minitab can be used to collect study data and perform analysis. The output and kappa graphics can be used to verify the effectiveness and accuracy of operators in conducting their evaluations. Unlike a continuous measurement value, which cannot be accurate (on average), any lack of precision in an attribute measurement system inevitably leads to accuracy problems. If the error coder is not clear or undecided on how to encode a defect, different codes are assigned to several defects of the same type, making the database imprecise. In fact, the vagueness of an attribute measurement system is an important factor in inaccuracies. Like any measurement system, the accuracy and accuracy of the database must be understood before the information is used (or at least during use) to make decisions. At first glance, it appears that the apparent starting point begins with an analysis of the attribute (or attribute-Gage-R-R). That may not be a very good idea. If the test is planned and designed effectively, it can reveal enough information about the causes of the accuracy problems to justify a decision not to use attribute analysis at all. In cases where the trial does not provide sufficient information, the analysis of the attribute agreement allows for a more detailed review to inform the introduction of training changes and error correction in the measurement system. 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. 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.