Data collection

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There are two approaches that may protect data integrity and secure scientific validity of study results invented by Craddick, Crawford, Rhodes, Redican, Rukenbrod and Laws in 2003:

Its main focus is prevention which is primarily a cost-effective activity to protect the integrity of data collection. Standardization of protocol best demonstrates this cost-effective activity, which is developed in a comprehensive and detailed procedures manual for data collection. The risk of failing to identify problems and errors in the research process is evidently caused by poorly written guidelines. Listed are several examples of such failures:

Since quality control actions occur during or after the data collection all the details are carefully documented. There is a necessity for a clearly defined communication structure as a precondition for establishing monitoring systems. Uncertainty about the flow of information is not recommended as a poorly organized communication structure leads to lax monitoring and can also limit the opportunities for detecting errors. Quality control is also responsible for the identification of actions necessary for correcting faulty data collection practices and also minimizing such future occurrences. A team is more likely to not realize the necessity to perform these actions if their procedures are written vaguely and are not based on feedback or education.