Attribute vs Variable GRR

Understanding the differences and when to use each approach

Quick Comparison

Variable GRR

Continuous Measurements

For measurements that produce continuous numeric values

Examples:
  • Length, width, height
  • Weight, temperature
  • Pressure, voltage
  • Surface roughness
  • Concentrations
Output:
  • Actual measurement values
  • Can be any value within range
  • Infinite possible values

Attribute GRR

Pass/Fail Decisions

For measurements that result in discrete classifications

Examples:
  • Pass/Fail inspections
  • Go/No-Go gages
  • Visual inspections
  • Color matching
  • Defect classifications
Output:
  • Categories or classifications
  • Limited number of possible outcomes
  • Discrete values only

Variable GRR (Continuous Data)

Study Design

Typical Setup:

  • 10 parts (minimum 5)
  • 2-3 operators
  • 2-3 trials per operator/part
  • Randomized measurement order

Data Collection:

  • Each measurement produces a numeric value
  • Operators measure same parts multiple times
  • Blinded to previous measurements
Analysis Methods

Range Method:

  • Calculate ranges for repeatability
  • Use operator averages for reproducibility
  • Apply constants (K1, K2, K3)

ANOVA Method:

  • Two-way ANOVA with interaction
  • Variance component analysis
  • More accurate for complex designs
Key Metrics
Metric Formula Excellent Marginal Unacceptable
%GRR (GRR / Total Variation) × 100 < 10% 10-30% > 30%
NDC 1.41 × (σpart / σGRR) ≥ 5 3-4 < 3
P/T Ratio Precision / Tolerance < 0.1 0.1-0.3 > 0.3

Attribute GRR (Discrete Data)

Study Design

Typical Setup:

  • 50+ parts (minimum 30)
  • 2-3 operators
  • 2-3 trials per operator/part
  • Known reference standard

Data Collection:

  • Each inspection produces a classification
  • Operators classify same parts multiple times
  • Blinded to previous classifications
  • Reference standard established
Analysis Methods

Agreement Analysis:

  • Operator vs. Reference agreement
  • Operator vs. Operator agreement
  • Within operator consistency

Statistical Measures:

  • Kappa statistics
  • Percent agreement
  • Sensitivity and specificity
  • False positive/negative rates
Key Metrics
Metric Description Excellent Marginal Unacceptable
Overall Agreement % of classifications matching reference ≥ 90% 80-90% < 80%
Kappa Value Agreement beyond chance ≥ 0.75 0.40-0.75 < 0.40
Within Operator Operator consistency ≥ 95% 85-95% < 85%

When to Use Which Approach

Use Variable GRR When:
  • Measurement produces continuous numeric values
  • You need to quantify measurement precision
  • Specification limits are numeric
  • Process capability studies are required
  • Statistical process control is used
  • Measurement resolution is adequate
Best for: Dimensional measurements, chemical analysis, physical properties
Use Attribute GRR When:
  • Measurement results in classifications
  • Go/No-Go gages are used
  • Visual inspections are performed
  • Subjective evaluations are made
  • Pass/Fail decisions are required
  • Multiple defect categories exist
Best for: Visual inspections, functional tests, sorting operations

Converting Between Approaches

Variable to Attribute

When you might convert:

  • Production uses Go/No-Go gages
  • Only pass/fail decisions are needed
  • Operators make subjective judgments

Process:

  1. Establish specification limits
  2. Convert measurements to pass/fail
  3. Analyze as attribute data
  4. Focus on classification agreement
Note: You lose information when converting from variable to attribute data.
Attribute to Variable

When you might convert:

  • Need more detailed analysis
  • Want to quantify measurement precision
  • Process capability studies required

Process:

  1. Replace Go/No-Go with variable gage
  2. Collect actual measurement values
  3. Analyze as variable data
  4. Calculate %GRR, NDC, P/T ratio
Benefit: Variable data provides more information and better discrimination.

Practical Examples

Application Measurement Type GRR Approach Key Considerations
Machined part dimensions Caliper measurements Variable Continuous values, tight tolerances
Thread inspection Go/No-Go thread gage Attribute Pass/fail decision, functional requirement
Surface finish Profilometer reading Variable Numeric Ra values, process control
Visual defect inspection Operator judgment Attribute Subjective, multiple defect types
Torque testing Torque wrench reading Variable Continuous values, safety critical
Color matching Visual comparison Attribute Subjective, categorical decisions

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