Quality Assured Measurement (eBook)

Preface 6
Acknowledgements 8
Contents 9
Chapter 1: Measurement Challenge: Specification and Design 14
1.1 Processes of Production and Measurement 15
1.2 Measurement, Assessment, Opinions: From Quantitative Observations to Categorization 16
1.2.1 Major Challenges and Interdisciplinary Studies 17
1.2.2 A Way Forward. Man As a Measurement Instrument 18
1.2.3 Categorical Scales: Logistic Regression 19
1.3 Opening the Quality Assurance Loop 22
1.4 Specification of Measurement Problem: Entity Specifications 23
1.4.1 Entity Specifications 23
1.4.2 Definition of Test Problem, Based on Product Description 25
1.4.3 Structural Models and Specifications of Entity Characteristics 26
1.4.4 Construct System and Structural Modelling: Functional and Non-functional Characteristics 29
1.4.5 Uncertainty and Risks: Link to Final Steps in Quality Loop 31
1.5 Case Study: Fit-for-Purpose Measurement Specification 32
1.5.1 Describe the Product [§E1.1] 32
1.5.2 Product Demands [§E1.2] 33
1.5.3 Definition of Test Problem, Based on Product Description §E1.1 34
Exercise 1: The Product 36
E1.1 Describe the Product 36
E1.2 Product Demands 37
E1.3 Definition of Test of Product, Based on Product Description §E1.1 37
References 38
Chapter 2: Measurement Method/System Development 41
2.1 Design of Experiments 42
2.1.1 Uncertainty and Risks. Fit-for-Purpose Measurement 42
2.1.2 Separating Production and Measurement Errors: Variability 44
2.2 Specification of Demands on a Measurement System 45
2.2.1 Different Variables Entering into Conformity Assessment of an Entity (Product) 46
2.2.2 Metrological Characterisation and Specifications 46
2.2.3 Separating Object and Instrument Attribute Estimation. Measurement System 49
2.2.4 Measurement System Specifications 52
MPE Instrument 52
Capability Factors 53
Limits on Capability Factors 53
2.2.5 Examples of Measurement System Demands, Both Quantitative and Qualitative. Pre-packaged Goods 54
2.3 Choice and Development of a Measurement Method 56
2.3.1 Definition of Test Problem, Based on Test Requirements 57
2.4 Measurement System Analysis (MSA) 57
2.4.1 Measurement Model 57
2.4.2 Static Functional Characteristics of Measurement Systems 58
2.4.3 Measurement System Modelling As a Chain of Elements: Signal Propagation in a Measurement System 60
2.4.4 Performance Metrics of Measurement Systems 61
2.4.5 Restitution 63
2.5 Evaluation and Validation of a Measurement Method 64
2.5.1 Design of Interlaboratory Experiment 64
2.5.2 Analysis of Variance in an ILC 66
2.5.3 Qualitative Accuracy Experiments 67
2.5.4 Applications of Method Accuracy 68
2.6 Verification 68
2.7 Case Studies 69
2.7.1 Practical Working of a Measurement System 69
2.7.2 Man As a Measurement Instrument, Psychometry and Product Function 70
Exercise 2 Definition of Measurement Problem 73
E2.1 Demands on Measurement System and Methods, Based on Product Demands §E1.2: 73
E2.2 Non-functional Characteristics of Appropriate Measurement System, Based in Test Demands §E1.3 & §E2.1:
E2.3 Functional Characteristics of Appropriate Measurement Systems, Based on Test Demands §E1.3 & §E2.1:
References 75
Chapter 3: Ensuring Traceability 78
3.1 Quantity Calculus and Calibration 79
3.1.1 Quantity Concepts 80
3.1.2 Introducing Measurement and Calibration. Separating Object and Instrument. Restitution 82
3.2 Units and Symmetry, Conservation Laws and Minimum Entropy 86
3.2.1 Meaningful Messages and Communicating Measurement Information 87
3.2.2 Units, Words and Invariance 88
3.2.3 Symmetry, Conserved Quantities and Minimum Entropy. Maximum Entropy and Uncertainty 90
3.3 Calibration, Accuracy and True Values 92
3.3.1 Trueness and Calibration Hierarchy 92
3.3.2 Objectivity and Calibration of Instruments in the Social Sciences 93
3.4 Politics and Philosophy of Metrology 94
3.4.1 Objective Measurement in the Physical and Engineering Sciences 94
3.4.2 Politics and Trueness 94
3.4.3 Measurement Comparability in Conformity Assessment 95
3.4.4 Objective Measurement in the Social Sciences 96
3.5 Quantitative and Qualitative Scales 98
3.5.1 Counted Fractions 99
Wishful Thinkers 100
Describers 100
3.5.2 Other Ordinal Scales. Pragmatism 101
3.6 New and Future Measurement Units 102
3.6.1 The Revised SI 102
Clear Definitions 102
Quantum Mechanics and Measurement 104
Boltzmann Constant and Elementary Counting 106
Counts and Quantities of Unit One 107
3.6.2 Human Challenges 108
Measurement Units and the Rasch Model 109
References 110
Chapter 4: Measurement 114
4.1 Performing Measurements 114
4.1.1 Measurement Process 115
4.2 Metrological Confirmation 116
4.2.1 Calibration and Metrological Confirmation. Uncertainty and Unknown Errors 117
Repeatability and Reproducibility 119
Convenience Sampling 119
4.2.2 Evaluating Measurement Uncertainty: Physical Measurements 120
4.3 Accurate Measurement across the Disciplines 125
4.3.1 Accurate Measurement: Is It the Domain of the Engineer or the Physicist? 125
4.3.2 Metrology in Physics and Chemistry 125
`Not Always Fully Traceable to the SI´: Only for the Chemist? 126
Little Interest in Traceability? 126
Can the Physicist Learn Anything About Metrological Traceability from the Chemist? 127
4.3.3 Metrology in the Social Sciences 128
4.4 Metrological Concepts in Social Science Measurements 128
4.4.1 Elementary Counting 130
4.4.2 Entropy, Perception and Decision-Making 133
4.4.3 Construct Specification Equations 136
4.4.4 Uncertainty and Ability 137
4.4.5 Separating Object and Instrument Attribute Estimation in Qualitative Measurement 138
4.5 Case Studies: Examples of Measurements 139
4.5.1 Physical Measurements 139
Calibration of a Set of Standards 140
Calibration and Testing of a Measurement System 143
Implementation to Measure 143
4.5.2 Human Challenges 145
(A) Calibration of a Set of Standards, Prestige 146
(B) Calibration and Testing of a Measurement System 148
(C) Implementation to Measure 149
Exercises 4: Presentation of Measurement Results 150
E4.1 Measurement System Analysis 150
E4.2 Expression of Measurement Uncertainty 151
References 151
Chapter 5: Measurement Report and Presentation 154
5.1 Qualitative Measurement, Probability Theory and Entropy 155
5.1.1 Differences in Entity, Response and Measured Values: Entropy and Histogram Distances 155
5.1.2 Differences in Measured Values at Each Stage of the Measurement Process 157
5.2 A: Entity Construct Description and Specification 162
5.2.1 Prioritisation 162
5.2.2 Entity Attributes, Construct Specification and Entropy 163
5.2.3 Formulation of Construct Specification Equations 166
5.2.4 Rasch-Based Construct Specification Equation from Logistic Regression 167
5.2.5 Principal Component Regression 167
5.3 Case Study: Knox Cube Test and Entity Construct Entropy 168
5.3.1 Measurement Uncertainty in Principal Component Regression 170
5.3.2 Measurement Uncertainty in the Construct Specification Equation 172
5.4 B: Instrument Construct Description and Specification 174
5.4.1 Instrument Attributes, Construct Specification and Entropy 174
5.4.2 Multi-Attribute Alternatives: House of Quality 176
Organisational Management Collaborative Entropy 177
Synergy 178
5.4.3 Formulation of Instrument Construct Specification Equations 179
5.5 C: Response, Error and Entropy-Categorical Observations 179
5.5.1 Interhistogram Distances on Categorical Scales: Systematic Errors 180
5.5.2 Response and Entropy 182
Intrahistogram Distances: Measurement Uncertainty 182
5.5.3 Deriving Response 184
5.6 Modelling Measurement System Response 186
5.6.1 Ordinary Factor Analysis 186
5.6.2 Psychometric Factor Analysis 187
5.6.3 Sensitivity of System for Ordinal Data 189
5.7 Metrological Comparability and Uncertainty of Ordinal Data: Scale and Sensitivity Distortions 190
5.7.1 Changes of Entity Scale: Acquiescence 195
5.7.2 Changes of Sensitivity 197
5.7.3 Further Tests of Assumptions 199
Logistic Regression and Estimator Bias 199
References 200
Chapter 6: Decisions About Product 205
6.1 Use of Measurement Results and Conformity Assessment 205
6.2 Closing the Quality Assurance Loop 206
6.3 Response, Restitution and Entropy 207
6.3.1 Restitution and the Rasch and IRT Models 207
6.3.2 Perceptive Choice and Smallest Detectable Change 209
6.3.3 Significance Testing 212
Choice Between Two Measurement Methods 212
Product Tests Under Conditions of Repeatability 212
Product Tests Under Conditions of Repeatability and Reproducibility 213
Example: Product Specification 213
6.3.4 Significance Testing: Case Study of Pre-packaged Goods 214
6.4 Assessing Entity Error and Measurement `Value´: Cost and Impact 215
6.4.1 Uncertainty and Incorrect Estimates of the Consequences of Entity Error 215
6.4.2 Consequence Costs 215
Linearly Priced Goods 215
Example: Conformity Assessment of Pre-packaged Goods 217
6.4.3 Measurement and Testing Costs 217
6.4.4 Consumer (Dis-)satisfaction 218
6.4.5 `Discrete Choice´, `Prospect´ Theory and Costs 219
Trading Uncertainty against Utility: Minimal Important Change 219
Discrete Choice 220
Prospect Theory 222
Decision Weights 222
6.4.6 Pragmatics and the Rasch Model 223
Other Models 224
6.5 Comparing Test Result with Product Requirement 224
6.5.1 Risks of Incorrect Decisions and Relation to Measurement Uncertainty 225
Example: Consumer Attribute Risk 226
6.5.2 Consumer and Supplier Risks 226
Consumer Global Risk (By Variable) 228
6.5.3 Mechanistic Model of Binary Decisions: Man as an Operator and Rating the Rater 228
6.5.4 Multivariate Decision-Making 231
6.6 Optimised Uncertainties, Impact and Measurement Costs, Pragmatic Extensions of Significance Testing 231
6.6.1 Example: Conformity Assessment of Pre-packaged Goods 236
6.6.2 Optimised Uncertainties on an Ordinal Scale 237
Exercises: Measurement and Product Decisions 239
Conformity Assessment 239
Significance Testing 241
References 241
Index 244