Editing Data

"Following the construction of a set of [test questions,] it is customary to test them empirically. From an analysis of the responses, the statements are then either retained, modified or discarded. Thus the idea of editing statements... is not new.

"There is another kind of editing, however, that needs to be considered. Each statement can be expected to operate consistently only in a specific range of the variable. The response Xni to a dichotomously scored statement is 0 or 1, yet the locations of the persons may, theoretically, span an infinite range. Clearly, it would not be expected that any statement would operate successfully across this wide range.

"The range within which a statement may be expected to operate consistently can be constrained by considering the probability of a positive or negative response: if this is very high (say greater than 80%, [>1.4 logit difference]) then the responses should be eliminated. Notice that here the editing is of specific responses of persons to statements... The responses at extremes simply should not be expected to work accurately. This principle is understood in physical measurements - a weighbridge is not used to weigh persons, and a bathroom scale is not used to weigh trucks, because each has its own operating range.

"It is stressed that this elimination of data is taken only for the phase of variable definition and scale construction. The complete response patterns of all persons would be used to measure them and to interpret fully the quality of their estimated locations."

David Andrich (1989) Constructing fundamental measurements in social psychology. p.17-26 in J.A. Keats, et al. (Eds), Mathematical and Theoretical Systems, New York: Elsevier/North Holland.

Educational measurement is in bondage to data. A child must be given credit for every correct answer regardless of cause. Such generosity, mislabelled fairness, disserves child, teacher and society by giving a false report on the child.

Andrich's last paragraph can be continued. Only clearly relevant data contributes to measurement. All other data contaminate measures with muddle. Irrelevant data can serve a host of diagnostic purposes, but not measurement accuracy or precision.

Editing data. Andrich D. … 1993, 7:2 p.297


Editing data. Andrich D. … Rasch Measurement Transactions, 1993, 1993, 7:2 p.297

Please help with Standard Dataset 4: Andrich Rating Scale Model



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