As with any measurement system, the precision 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 seems that the obvious starting point is an attribute agreement analysis (or R&R attribute gauge). However, it may not be such a good idea. Click the Agreement Evaluation Tables button to create a graph showing the percentage of compliance for each reviewer with the standard and the 95% confidence intervals associated with it. The accuracy of a measurement system is analyzed by dividing it into two main components: repeatability (the ability of a particular evaluator to assign the same value or attribute several times under the same conditions) and reproducibility (the ability of several evaluators to agree among themselves on a particular set of circumstances). With an attribute measurement system, repeatability or reproducibility problems inevitably lead to accuracy problems. If overall accuracy, repeatability and reproducibility are known, bias can also be detected in situations where decisions are systematically wrong. Note: Details on how to perform the scan and perform the full scan go beyond the intent of this document. For more information, see Statistical Analysis References.

For example, if repeatability is the main issue, evaluators are confused or undecided about certain criteria. If reproducibility is the problem, then evaluators have strong opinions about certain conditions, but those opinions differ. If the problems are presented by several reviewers, they are clearly systemic or process-related. If the issues are only a few examiners, they may simply require a little personal attention. In both cases, training or work aids could be tailored either to specific individuals or to all evaluators, depending on the number of evaluators guilty of inaccurate attribute assignment. Since running an attribute agreement analysis can be time-consuming, expensive, and usually inconvenient for everyone involved (the analysis is simple compared to running), it`s best to take a moment to really understand what needs to be done and why. In addition to the question of sample size, the logistics that ensure reviewers don`t remember the original attribute they assigned to a scenario when they see it for the second time can also be challenging. Of course, this can be somewhat avoided by increasing the sample size and, better yet, waiting a while before giving the reviewers the scenarios for the second time (perhaps one to two weeks). Randomizing executions from one notice to another can also be useful. In addition, evaluators also tend to work differently when they know they are being examined, so the fact that they know that it is a test can also skew the results. Hiding this in any way can help, but it`s almost impossible to achieve, despite the fact that it borders on immorality.

And in addition to being marginally effective at best, these solutions add complexity and time to an already difficult study. An overview of the basic AAA analysis. Covers the 3 basic types of agreements: agreement with oneself, agreement with a peer, agreement according to the standard. The AAA and Kappa statistical values are checked. Confidence levels for the result bands are also checked. The inclusion of the AAA in the control plan and the frequency of “calibration” are verified. Duncan only agreed with the standard about 53% of the time. Hayes did much better with about 87% approval. Simpson agreed 93%, and Holmes and Montgomery agreed with the standard in all trials.

This graph shows that Duncan, Hayes and Simpson need additional training. This example uses a repeatability score to illustrate the idea, and it also applies to reproducibility. The point here is that many samples are needed to detect differences in an attribute agreement analysis, 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 recognized depends on the situation and the risk that the analyst is willing to bear in the decision, but the reality is that with 50 scenarios, an analyst can hardly assume that there is a statistical difference in the repeatability of two evaluators with matching rates of 96% and 86%. With 100 scenarios, the analyst will barely be able to tell the difference between 96% and 88%. Despite these difficulties, performing an attribute agreement analysis for bug tracking systems is not a waste of time. In fact, it is (or can be) an extremely informative, valuable and necessary exercise. Attribute matching analysis only needs to be applied judiciously and with some concentration. This table shows the extent to which the examiners agreed with each other. As you can see, the reviewers agreed with 40% (6 out of 15) of the time.

In addition to the match percentage, Statistica also displays Fleiss` Kappa statistics and Kendall`s concordance coefficient. Fleiss` Kappa statistics show how much the reviewers agreed on each standard answer. A value close to 1 indicates a strong match. The Kendall concordance coefficient indicates the strength of the relationship between evaluators. This value varies from -1 to 1. A value close to 1 indicates a strong match. Both measures indicate a fairly strong consensus among reviewers. Unlike a continuous meter, which may be (on average) but not accurate, a lack of precision in an attribute measurement system necessarily also leads to accuracy problems. If the error encoder is unclear or undecided on how to encode an error, different codes are assigned to multiple errors of the same type, making the database inaccurate. In fact, the inaccuracy of an attribute measurement system contributes significantly to the inaccuracy. Analytically, this technique is a wonderful idea. But in practice, it can be difficult to perform the technique significantly.

First of all, there is always the problem of sample size. Attribute data require relatively large samples to calculate percentages with relatively small confidence intervals. If an examiner looks at 50 different error scenarios – twice – and the compliance rate is 96% (48 chances out of 50 agree), the 95% confidence interval is between 86.29% and 99.51%. That`s a pretty large margin of error, especially given the challenge of selecting the scenarios, reviewing them thoroughly to make sure the right principal value is assigned, and then convincing the appraiser to do the job – twice. When the number of scenarios is increased to 100, the 95% confidence interval for a 96% match rate is reduced to a range of 90.1% to 98.9% (Figure 2). In the Variable Selection dialog box, click OK. In the Attribute Agreement Analysis dialog box, select the Advanced tab. Because the data is sorted, select the Sort attribute data categories check box. Whenever someone makes a decision – such as “Is this the right candidate?” – it is important that the decision-maker chooses the same choice again and that others come to the same conclusion. The analysis of award agreements measures whether or not several persons who make a judgment or assessment of the same subject have a high degree of agreement with each other. At this stage, the assessment of the attribute agreement should be applied and the detailed results of the audit should provide a good set of information to understand how best to design the assessment.

The remaining analysis of this data appears in the Minitab session window. Below is an excerpt from this analysis (Note: not all data analyses are displayed): Then click the Every Reviewer button against the standard agreement tables to create the following table (partial image below). Once it is established that the bug tracking system is an attribute measurement system, the next step is to look at the terms precision and accuracy in relation to the situation. First of all, it is useful to understand that precision and accuracy are terms borrowed from the world of continuous (or variable) measuring instruments. For example, it is desirable that a car`s speedometer measures just the right speed over a speed range (e.B. 25 mph, 40 mph, 55 mph and 70 mph), no matter who reads it. The absence of distortion over a range of values over time can usually be called accuracy (distortion can be considered false on average). The ability of different people to interpret and match the same meter value multiple times is called accuracy (and accuracy problems can come from a problem with the meter, not necessarily from the people who use it). However, a bug tracking system is not a continuous counter.

The assigned values are correct or not. there is not (or should be none) grey area. If the codes, locations, and severity levels are set correctly, there is only one correct attribute for each of these categories for a specific error. Session participants will observe the step-by-step actions to perform a AAA and their iterative results. A review of the 3 types of basic agreements will be conducted. Human operator calibration has several advantages. A pro/contra review is done to discuss the general benefits of argument reductions (which are good or not), internal/external tweaks, returns, premium freight, etc. Why you should participate: People CAN be calibrated.

The control plans provide for an “MSA” analysis of key processes. Inevitably, these mainly focus on the characteristics measured by variable measurement systems. However, MSA analysis should also be applied to attribute-based processes and inspected visually and/or via methods where gauges/tools are not used (mainly due to the need to save money or be efficient). . . .