Measurement Systems Analysis (MSA)
Measurement Systems Analysis is designed to improve the process when the apparent variation is caused by variations in the measuring system. Measurement systems are subject to variation and error. The following are common MSA terms:
To properly build the required Measurement Systems Analysis, the combination of analytical methods which lead the team in the direction to developing a solution for the problem may require a combination of multiple tools.
A Measurement Systems Analysis (MSA) is a designed experiment that seeks to identify the components of variation in the measurement. A Measurement Systems Analysis evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data.
Precision and Accuracy – Precision/tolerance (P/T) tells how well a given measurement can be reproduced. This is a standard deviation around a mean value. Tolerance may have two errors: systematic and random.
Bias, Linearity, & Stability – Bias is a measure of the distance between the actual value and the average value of a part. Bias occurs when the survey sample does not accurately represent the population. The bias that results from an unrepresentative sample is called selection bias.
Under-coverage bias occurs when some members of the population are inadequately represented in the sample.
Nonresponse bias occurs when individuals chosen for the sample are unwilling or unable to participate in the survey. Voluntary response bias occurs when sample members are self-selected volunteers; the resulting sample tends to over-represent individuals who have strong opinions.
Random sampling is a sampling from a population in which the selection of a sample unit is based on chance and every element of the population has a known, non-zero probability of being selected. Random sampling helps produce representative samples by eliminating voluntary response bias and guarding against under coverage bias.
Response bias is bias that results from problems in the measurement process. Bias due to measurement error can occur with a poor measurement process. This includes leading questions where the wording of the question may be loaded in some way to favor one response over another. Social desirability occurs when survey respondents are reluctant to admit to questions in the survey if the results are not confidential. Their responses may be biased toward what they believe is socially desirable.
Linearity measures the consistency of bias over the range of the measuring device. Accuracy is the degree of closeness to an expected mark.
Stability of a measurement system is analyzed using control charts. Your goal is to ensure the measurements taken by the appraisers for the process are stable and consistent over time. Use the control charts you develop to monitor measures over time so that you can make corrections to processes and operating procedures. Don’t forget when thinking about measurement that special causes can also occur with the process control limits, and these must be given corrective action before proceeding to validate the measurement system.
A Measurement Systems Analysis (MSA) is a designed experiment that seeks to identify the components of variation in the measurement. A Measurement Systems Analysis evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data.
Precision and Accuracy – Precision/tolerance (P/T) tells how well a given measurement can be reproduced. This is a standard deviation around a mean value. Tolerance may have two errors: systematic and random.
Bias, Linearity, & Stability – Bias is a measure of the distance between the actual value and the average value of a part. Bias occurs when the survey sample does not accurately represent the population. The bias that results from an unrepresentative sample is called selection bias.
Under-coverage bias occurs when some members of the population are inadequately represented in the sample.
Nonresponse bias occurs when individuals chosen for the sample are unwilling or unable to participate in the survey. Voluntary response bias occurs when sample members are self-selected volunteers; the resulting sample tends to over-represent individuals who have strong opinions.
Random sampling is a sampling from a population in which the selection of a sample unit is based on chance and every element of the population has a known, non-zero probability of being selected. Random sampling helps produce representative samples by eliminating voluntary response bias and guarding against under coverage bias.
Response bias is bias that results from problems in the measurement process. Bias due to measurement error can occur with a poor measurement process. This includes leading questions where the wording of the question may be loaded in some way to favor one response over another. Social desirability occurs when survey respondents are reluctant to admit to questions in the survey if the results are not confidential. Their responses may be biased toward what they believe is socially desirable.
Linearity measures the consistency of bias over the range of the measuring device. Accuracy is the degree of closeness to an expected mark.
- Linear Regression is a linear relationship with one input and one output.
- Multiple Linear Regression is a linear relationship with several inputs.
- Logistics Regression is regression where the output is a probability.
Stability of a measurement system is analyzed using control charts. Your goal is to ensure the measurements taken by the appraisers for the process are stable and consistent over time. Use the control charts you develop to monitor measures over time so that you can make corrections to processes and operating procedures. Don’t forget when thinking about measurement that special causes can also occur with the process control limits, and these must be given corrective action before proceeding to validate the measurement system.
