In most business surveys, it is reasonable to assume that a relatively small number of observations are affected by errors with a significant effect on the estimates to be published (so-called influential errors), while the other observations are either correct or contain only minor errors. For the purpose of statistical data editing, attention should be focused on treating the influential errors. Macro-editing (also known as output editing or selection at the macro level) is a general approach to identify the records in a data set that contain potentially influential errors. It can be used when all the data, or at least a substantial part thereof, have been collected.
Macro-editing has the same purpose as selective editing (see “Statistical Data Editing – Selective Editing”): to increase the efficiency and effectiveness of the data editing process. This is achieved by limiting the costly manual editing to those records for which interactive treatment is likely to have a significant effect on the quality of the estimates. The main difference between these two approaches is that selective editing selects units for manual follow-up on a record-by-record basis, whereas macro-editing selects units by considering all the data at once. It should be noted that in macro-editing all actual adjustments to the data take place at the micro level (i.e., for individual units), not the macro level. Methods that perform adjustments at the macro level are discussed in the topic “Macro-Integration”.
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