The tool used for this type of analysis is called the R-R pledge attribute. R-R is synonymous with repeatability and reproducibility. Repeatability means that the same operator who measures the same thing must get the same reading each time with the same measuring instrument. Reproducibility means that different operators who measure the same thing, with the same measuring device, should get the same measurement value each time. The most problematic problems of the measurement system are related to the measurement of attribute data in terms of human judgment such as good/bad, pass/fail, etc. This is because it is very difficult for all testers to apply the same operational definition of what is « good » and « bad. » Step 2: Have a master examiner of each test example categorized into its true category of attributes. This percentage indicates the overall effectiveness of the measurement system (Minitab calls it « All Appraiser vs. Standard »). This is the percentage of time agreed by all inspectors and their agreement is in accordance with the standard.
This percentage is called individual efficiency (Minitab calls it « Every expert by default »). In this case, Operator 1 only meets the standard 80 percent of the time. It needs to be re-schooled. Minitab produces many more statistics in the editing of the attribute analysis, but in most cases and in the mode of use, the analysis described in this article should be sufficient. Step 5: Count for each inspector how many times his two statements match. Share this number with the total amount that will be verified to get the percentage of the agreement. This is the individual repeatability of this inspector (Minitab calls him « within the examiner »). I wanted to know please if the method used for the R-R attributes test can be considered a reliable source? What is your academic (and bibliographical) basis for doing this test? ——————————————————————————————————————————————————— – I would like to know if the method used for testing the R and R attributes can be considered a reliable source? Figure 5: Reproducibility of the measurement system.
All statistical methods are very sensitive to sample size to the point where MOST hypothesis tests reject zero when sample size is large. This has nothing to do with the economic difference between the alternative data categories. However, attribute data require much larger samples than continuous data to reach this « always rejected » level. In both cases, economic issues dominate decision-making processes. However, if the sample size is very small and there are no significant differences, it is necessary to go back to the experts in the economic sector and determine the cost of a larger survey by survey to be more sure not to lack a key factor, although for no other reason than to question the definition of factors, granularity, etc. These percentages can be obtained with simple mathematics, and there really is no need for sophisticated software. Nevertheless, Minitab has a module called Attribute Analysis (in Minitab 13, the Gage R-R attribute) that does the same and much more, making life easier for analysts. The key in all measurement systems is a clear testing method and clear criteria on what to accept and what should be rejected. The steps are as follows: Attribute-Gage-R-R shows two important discoveries – percentage of repeatability and percentage of reproducibility.
Ideally, both percentages should be 100%, but in general, the basic rule is slightly above 90 percent is fairly sufficient. However, these measurement systems are observed throughout the industry. For example, quality control inspectors use a powerful microscope to determine if a pair of contact lenses is flawless. It is therefore important to quantify how well these measurement systems work.