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Example of data collection in the biological sciences: Adélie penguins are identified and weighed each time they cross the automated weighbridge on their way to or from the sea.[1]

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a component of research in all fields of study including physical and social sciences, humanities,[2] and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed.

Contents

ImportanceEdit

Regardless of the field of study or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintaining the integrity of research. The selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors.

A formal data collection process is necessary as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid data.[3] The process provides both a baseline from which to measure and in certain cases an indication of what to improve.

Impact of faulty dataEdit

Consequences from improperly collected data include:

  • Inability to answer research questions accurately;
  • Inability to repeat and validate the study.

Distorted findings result in wasted resources and can mislead other researchers into pursuing fruitless avenues of investigation; it may also compromise decisions, for example for public policy, which may cause disproportionate harm.[4]

Data integrity issues [5]Edit

The main reason for maintaining data integrity is to support the observation of errors in the data collection process. Those errors may me made intentionally (deliberate falsification) or non-intentionally (random or systematic errors).

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:

  • Quality assurance – all actions carried out before data collection
  • Quality control – all actions carried out during and after data collection

Quality assuranceEdit

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:

  • Uncertainty of timing, methods and identification of the responsible person
  • Partial listing of items needed to be collected
  • Vague description of data collection instruments instead of rigorous step-by-step instructions on administering tests
  • Failure to recognize exact content and strategies for training and retraining staff members responsible for data collection
  • Unclear instructions for using, making adjustments to, and calibrating data collection equipment
  • No predetermined mechanism to document changes in procedures that occur during the investigation

Quality controlEdit

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.

Data collection problems that necessitate prompt action:

  • Systematic errors
  • Violation of protocol
  • Fraud or scientific misconduct
  • Errors in individual data items
  • Individual staff or site performance problems

See alsoEdit

ReferencesEdit

  1. ^ Lescroël, A. L.; Ballard, G.; Grémillet, D.; Authier, M.; Ainley, D. G. (2014). Descamps, Sébastien (ed.). "Antarctic Climate Change: Extreme Events Disrupt Plastic Phenotypic Response in Adélie Penguins". PLoS ONE. 9 (1): e85291. doi:10.1371/journal.pone.0085291. PMC 3906005. PMID 24489657.
  2. ^ Vuong, Quan-Hoang; La, Viet-Phuong; Vuong, Thu-Trang; Ho, Manh-Toan; Nguyen, Hong-Kong T.; Nguyen, Viet-Ha; Pham, Hiep-Hung; Ho, Manh-Tung (September 25, 2018). "An open database of productivity in Vietnam's social sciences and humanities for public use". Scientific Data. 5: 180188. doi:10.1038/sdata.2018.188. PMC 6154282. PMID 30251992. Retrieved September 26, 2018.
  3. ^ Data Collection and Analysis By Dr. Roger Sapsford, Victor Jupp ISBN 0-7619-5046-X
  4. ^ Weimer, J. (ed.) (1995). Research Techniques in Human Engineering. Englewood Cliffs, NJ: Prentice Hall ISBN 0-13-097072-7
  5. ^ Northern Illinois University (2005). "Data Collection". Responsible Conduct in Data Management. Retrieved June 8, 2019.

External linksEdit