Data considerations for Expanded Gage R&R Study

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

Operators should measure parts in a random order
To ensure that the data collection order does not influence the results, each operator should measure all parts randomly within a replicate. After all operators measure all parts one time, you repeat the process for all replicates.
The design can be balanced or unbalanced
A balanced design has an equal number of observations for all possible combinations of factor levels. An unbalanced design has an unequal number of observations.
Factors can be crossed or nested
Two factors are nested when each level of one factor occurs with only one level of the other factor. For example, if two operators measure two different, but similar, sets of parts, parts are nested under operator, and is indicated by Part (Operator).
Two factors are crossed when each level of one factor occurs in combination with each level of the other factor. For example, if two operators measure all parts, parts are crossed under operator.
For more information on crossed and nested factors, go to Types of factors in a gage R&R study.
Factors may be fixed or random
Usually, if the investigator controls the levels of a factor, the factor is fixed. However, if the investigator randomly samples the levels of a factor from a population, the factor is random.
Suppose the factor, Operator, has three levels. If you intentionally select three operators and you want your results to apply to only these operators, the factor is fixed. However, if you randomly sample three operators from a larger number of operators, and you want your results to apply to all operators, the factor is random.
Select parts that represent the actual or expected range of process variation
Select parts from the entire range of your process to increase the likelihood of having a good estimate of the part-to-part variation. For example, do not measure consecutive parts, parts from a single shift or a single production line, or parts from the reject pile.
You should have at least 3 operators for an adequate study
For the best results, include 3 to 5 operators in your study. You should not have fewer than 3 operators in the study, unless the number of operators who use the measurement system is actually less than 3. If you suspect that there are large differences between operators, you should consider using more than 3 to 5 operators. If you identify differences between operators, such as an operator whose measurements are lower than other operators, you can often improve consistency with training.
When selecting operators for the study, ensure that they are representative of all the operators who use the measurement system. If you perform the study with only the best (or worst) operators, the results will be biased and will not provide an accurate estimate of operator differences. The best way to ensure accuracy is to randomly select the operators for the study.
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