If your organization is challenged by honest disagreements about „calls for judgment,“ an attribute analysis can be exactly the tool you need to put everyone back on the same page. The assistant`s second report (hereafter) contains a graphic summary of the accuracy rates for the analysis. In the first part of this series, we have seen how conflicting opinions about a subjective factor can create business problems. In Part 2, we used Minitab`s assistant function to set up an attribute agreement analysis that provides a better understanding of where and when these discrepancies occur. The results of this analysis of attribute agreements give the Bank a clear indication of how auditors can improve their overall accuracy. Based on the results, the loan service offered additional training for Javier and Julia (who were also the least experienced critics of the team) and also organized a general review meeting for all reviewers to update their understanding of the most important factors for the application. As is often the case, you don`t need statistical software for this analysis – but with 240 data points you have to deal with, a computer and software like Minitab will make it much easier. This report card contains general information for analysis, including the calculation of accuracy percentages. It can also draw your attention to potential problems related to your analysis (for example. B if there is an imbalance in the amount of acceptable items to be rejected that are assessed); in this case, there are no warnings that we are concerned about.

The last article that was created as part of the assistant`s analysis is the report card: the Minitab assistant generates four reports as part of its attribute analysis. The first is a summary report that is presented below: after the completion of the additional training and the implementation of the new tools, the Bank conducted a second analysis of the attribute agreements, which examined improvements in the accuracy of auditors. The bar chart at the bottom left shows that Javier and Julia have 71.7% and 78.3% respectively of the lowest accuracy percentages among examiners. Jim has the highest accuracy with 96%, followed by Jill with 90%. If you can manually set up the worksheet or rename the columns, simply select the corresponding column for each item. Select the value for good or acceptable items („Accept“ in this case- then press OK to analyze the data. We asked four reviewers to reject or approve 30 selected applications, twice per piece. Once we have collected this data, we can analyze it. If you want to read, you can download the recording here.

Last time, we showed that the only data we need to record is whether each expert has accepted or rejected the standard application in all cases. The data collection forms generated by Minitab and the worksheet allow you to fill out the Results column of the worksheet.