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: • wk design weight • predicted value • zkjreported value • jstandardisation measure

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

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

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.”

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

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

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

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

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

Turning to geometry…

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

Euclidean distance

Supremum (maximum, Chebyshev’s) distance

Imagine questionnaires with three items Record k Euclidean distance

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

The max function A cube in 3D Same threshold 

The sum function An octahedron in 3D

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

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

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

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

Local and Global Scores in Selective Editing