Chapter 3: Attribute Measurement
Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems Analysis
3.2 Conducting an Attribute MSA
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Chapter 3: Attribute Measurement
Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems
Analysis
3.2 Conducting an Attribute MSA
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Objectives
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Introduce the basic concepts of an attribute
measurement systems analysis (MSA).
Understand operational definitions for inspection
and evaluation.
Define attribute MSA terms.
What Is an MSA?
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A measurement systems analysis is an evaluation of
the efficacy of a measurement system.
It is applicable to both continuous and attribute data.
An attribute MSA evaluates whether a classification
system correctly sorts items.
Companies make decisions each day based on
classifications; it is necessary to evaluate the efficacy
of such classifications.
Operational Definitions
In order for a rater to decide if a product is defective
or not, he must have a clear description, or an
operational definition, of what constitutes a defect.
Such a definition might include the following:
 photographs
 physical specimens
 descriptions
 specifications.
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Effectiveness
The effectiveness of an inspection process
 is the percentage of time that a rater, or other
measurement tool, is correct in its classification
of quality
 is often significantly low before any attempts at
improvement are instigated
 should be at least 95%.
Effectiveness = number of correct evaluations
number of total opportunities
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3.01 Multiple Choice Poll
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is his effectiveness?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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3.01 Multiple Choice Poll – Correct Answer
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is his effectiveness?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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False Alarms
A false alarm is a non-defective item that is classified
as defective.
The probability of a false alarm, also known as Type I
error or producer’s risk, is given by:
P(False Alarm) =
10
number of false alarms
number of non-defective items
3.02 Multiple Choice Poll
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is the probability of a
false alarm?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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3.02 Multiple Choice Poll – Correct Answer
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is the probability of a
false alarm?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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Misses
A miss is a defective item that is classified as
non-defective.
The probability of a miss, also known as Type II
error or consumer’s risk, is given by:
P(Miss) =
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number of misses
number of defective items
3.03 Multiple Choice Poll
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is the probability of a
miss?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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3.03 Multiple Choice Poll – Correct Answer
Suppose 100 windshields are inspected, and 10 are
defective and 90 are non-defective. If an inspector
decides that 6 non-defectives are defective, and 1
defective is non-defective, what is the probability of a
miss?
a. .67
b. .067
c. .1
d. .93
e. None of the above
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Escape Rate
An escape rate gives the percentage of time a
customer is likely to see a defective item.
Escape Rate = P(Miss) × P(Defect)
where P(Defect) =
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number of defects
number of items inspected.
Bias
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Bias is the tendency of an inspector to classify items
either as defective or as non-defective.
Bias is defined as P(False Alarm)/P(Miss).
Bias =1 implies there is no bias.
Bias < 1 implies a bias towards accepting bad items.
Bias > 1 implies a bias towards rejecting good items.
3.04 Multiple Choice Poll
The bias is given by the probability of a false alarm
divided by the probability of a miss. In the windshield
example, the bias is given by .067/.1 = .67. What is the
interpretation of this value?
a. There is no bias.
b. There is a bias towards accepting bad items.
c. There is a bias towards rejecting good items.
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3.04 Multiple Choice Poll – Correct Answer
The bias is given by the probability of a false alarm
divided by the probability of a miss. In the windshield
example, the bias is given by .067/.1 = .67. What is the
interpretation of this value?
a. There is no bias.
b. There is a bias towards accepting bad items.
c. There is a bias towards rejecting good items.
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Rater Agreement
Rater agreement
 is a measure of how well raters agree with each other
 is not an indication of correctness
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Kappa Statistic
The Kappa statistic
 is used to measure between-rater variability, or how
often two or more raters agree in their interpretations
 is a measure and not a test
 is given by:
kappa = po – pe
1 – pe
where po is the sum of observed proportions in diagonal
cells of the contingency table and pe is the sum of
expected proportions in diagonal cells of the contingency
table.
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Chapter 3: Attribute Measurement
Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems Analysis
3.2 Conducting an Attribute MSA
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Objectives
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Examine the requirements for an attribute MSA.
Perform an attribute MSA in JMP.
Sample Size
To conduct an attribute MSA, the minimum recommended
sample sizes are given as follows:
Number of
Raters
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Minimum Number
of Parts
Number of
Evaluations
1
40
3
2
30
3
3+
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Attribute MSA Example
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Suppose three inspectors, Henry, Matt, and Tom, are
independently going to classify each of 30 parts as
defective or non-defective in a random order. They will
evaluate each part three different times. Of the 30
parts, 13 are defective and 17 are non-defective.
The classification will be based on a predetermined
operational definition of defective and non-defective.
Attribute MSA
This demonstration illustrates the concepts
discussed previously.
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Exercise
This exercise reinforces the concepts discussed
previously.
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