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Local and Global Scores in Selective Editing

Local and Global Scores in Selective Editing. Dan Hedlin Statistics Sweden. Local score. Common local (item) score for item j in record k : w k design weight predicted value z kj reported value  j standardisation measure. Global score.

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Local and Global Scores in Selective Editing

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  1. Local and Global Scores in Selective Editing Dan Hedlin Statistics Sweden

  2. Local score • Common local (item) score for item j in record k: • wk design weight • predicted value • zkjreported value • jstandardisation measure

  3. Global score • What function of the local scores to form a global (unit) score? • The same number of items in all records • p items, j = 1, 2, … p • Let a local score be denoted by kj • … and a global score by

  4. Common global score functions In the editing literature: • Sum function: • Euclidean score: • Max function:

  5. Farwell (2004): ”Not only does the Euclidean score perform well with a large number of key items, it appears to perform at least as well as the maximum score for small numbers of items.”

  6. Unified by… • Minkowski’s distance • Sum function if  = 1 • Euclidean  = 2 • Maximum function if   infinity

  7. NB extreme choices are sum and max • Infinite number of choices in between •  = 20 will suffice for maximum unless local scores in the same record are of similar size

  8. Global score as a distance • The axioms of a distance are sensible properties such as being non-negative • Also, the triangle inequality • Can show that a global score function that does not satisfy the triangle inequality yields inconsistencies

  9. Hence a global score function should be a distance • Minkowski’s distance appears to be adequate for practical purposes • Minkowski’s distance does not satisfy the triangle inequality if  < 1 • Hence it is not a distance for  < 1

  10. Parametrised by  • Advantages: unified global score simplifies presentation and software implementation • Also gives structure:  orders the feasible choices…from smallest:  = 1…to largest: infinity

  11. Turning to geometry…

  12. Sum function = City block distance p = 3, ie three items

  13. Euclidean distance

  14. Supremum (maximum, Chebyshev’s) distance

  15. Imagine questionnaires with three items Record k Euclidean distance

  16. The Euclidean function, two items Threshold A sphere in 3D Threshold 

  17. The max function A cube in 3D Same threshold 

  18. The sum function An octahedron in 3D

  19. The sum function will always give more to edit than any other choice, with the same threshold

  20. Three editing situations • Large errors remain in data, such as unit errors • No large errors, but may be bias due to many small errors in the same direction • Little bias, but may be many errors

  21. Can show that if… • Situation 3 • Variance of error is • Local score is • Then the Euclidean global score will minimise the sum of the variances of the remaining error in estimates of the total

  22. Summary • Minkowski’s distance unifies many reasonable global score functions • Scaled by one parameter • The sum and the maximum functions are the two extreme choices • The Euclidean unit score function is a good choice under certain conditions

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