Transcription error

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Examples of transcription error

Input : Joseph Miscat
Instead of : Joseph Muscat

Input : 23rd of August
Instead of : 23 August

Input : Jishua
Instead of : Joshua

A transcription error is a specific type of data entry error that is commonly made by human operators or by optical character recognition programs (OCR). Human transcription errors are commonly the result of typographical mistakes, putting fingers in the wrong place during touch typing is the easiest way to ascertain this error.[1] A slang term "stubby fingers" is used for people who commonly make this mistake. Electronic transcription occurs when the results of a scan of printed matter is compromised or in an unusual font – e.g., if the paper is crumpled, or the ink is smudged when wet, the OCR may have a transcription error when reading.

Transposition error[edit]

Examples of transposition error

Input : Gergory
Instead of : Gregory

Input : 23rd of Auguts
Instead of : 23 August

Input : Johsua
Instead of : Joshua

Transposition errors are commonly mistaken for transcription errors, but they should not be confused. As the name suggest, transposition errors occur when characters have “transposed”—that is, they have switched places. Transposition errors are almost always human in origin. The most common way for characters to be transposed is when a user is touch typing at a speed that makes them input one character, before the other. This may be caused by their brain being one step ahead of their body.

Solving transcription and transposition errors[edit]

Transcription and transposition errors are found everywhere, even in professional articles in newspapers or books whether fictional or factual. They can be missed by editors quite simply, just as they can be typed quite simply. The most obvious cure for the errors is for the user to watch the screen when they type, and to proof read. If the entry is occurring in data capture forms, databases or subscription forms, the coder of the forms or the database administrator should use input masks or validation rules.

Transcription and transposition errors may also occur in syntax when Computer programming or programming, within variable declaration or within coding parameters – this should be checked by proof reading, however syntax errors may be picked up by the program the author is using. Common desktop publishing and word processing applications use spell checkers and grammar checkers which may pick up on some transcription/transposition errors – not all errors may be picked up however, as some errors may form new words which fit grammatically. For instance if the user wished to write "The fog was dense", but instead put "The dog was dense", a grammar and spell checker would not notify the user because both phrases are grammatically correct, as is the spelling of the word "dog" and "fog". Unfortunately, the situation regarding these errors is likely to get worse before it gets better, as workload for users and workers regarding manual direct data entry (DDE) devices increases.

Double entry may also be leveraged to minimize transcription or transposition error, but at the cost of a reduced number of entries per unit time.

Mathematical transposition errors are easily identifiable. Add up the numbers that make up the difference and the resultant number will always be evenly divisible by nine. For example, (72-27)/9 = 5.

Auditing transcription errors in medical research databases[edit]

Double data entry is considered to be the gold standard approach. However, as double-entry needs to be carried out by two separate data entry officers, the expenses associated with double data entry are substantial. Moreover, in some institutional set up this could not be possible. Therefore M. Khushi et al. suggest another semi automatic technique called 'eAuditor'. [2] Using eAuditor, it was identified that human entry errors range from 0.01% when entering donor's clinical follow-up details, to 0.53% when entering pathological details, highlighting the importance of an audit protocol tool such as eAuditor in a medical research database.

References[edit]

  1. ^ Doyle, Stephen (1985). Gcse Computer Studies for You. Nelson Thornes. p. 44. ISBN 0-7487-0381-0. 
  2. ^ M. Khushi; J. Carpenter; R. Balleine; C. Clarke (2011). "Development of a data entry auditing protocol and quality assurance for a tissue bank database.". Cell and Tissue Banking 13 (1): 9–13. doi:10.1007/s10561-011-9240-x. PMID 21331789.