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Data collection or data gathering is the process of gathering and measuringinformation on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, 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 evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed.
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]
Regardless of the field of or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintain research integrity. The selection of appropriate data collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood of errors.
This article is missing information about experiment, sampling, measurement and preprocessing. Please expand the article to include this information. Further details may exist on the talk page.(July 2023)
Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling.[3]
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.[4] The process provides both a baseline from which to measure and in certain cases an indication of what to improve.
Data management platforms (DMP) are centralized storage and analytical systems for data, mainly used in marketing. DMPs exist to compile and transform large amounts of demand and supply data into discernible information. Marketers may want to receive and utilize first, second and third-party data.[clarification needed] DMPs enable this, because they are the aggregate system of DSPs (demand side platform) and SSPs (supply side platform). DMPs are integral for optimizing and future advertising campaigns.
Data integrity issuesedit
The main reason for maintaining data integrity is to support the observation of errors in the data collection process. Those errors may be made intentionally (deliberate falsification) or non-intentionally (random or systematic errors).[5]
There are two approaches that may protect data integrity and secure scientific validity of study results:[6]
Quality assurance – all actions carried out before data collection
Quality control – all actions carried out during and after data collection
QA's focus is prevention, which is primarily a cost-effective activity to protect the integrity of data collection. Standardization of protocol, with comprehensive and detailed procedure descriptions for data collection, are central for prevention. The risk of failing to identify problems and errors in the research process is often 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
User privacy issuesedit
There are serious concerns about the integrity of individual user data collected by cloud computing, because this data is transferred across countries that have different standards of protection for individual user data.[7] Information processing has advanced to the level where user data can now be used to predict what an individual is saying before they even speak.[8]
Since QC actions occur during or after the data collection, all the details can be 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:
^Data Collection and Analysis By Dr. Roger Sapsford, Victor Jupp ISBN0-7619-5046-X
^Northern Illinois University (2005). "Data Collection". Responsible Conduct in Data Management. Retrieved June 8, 2019.
^Most, Marlene M.; Craddick, Shirley; Crawford, Staci; Redican, Susan; Rhodes, Donna; Rukenbrod, Fran; Laws, Reesa (October 2003). "Dietary quality assurance processes of the DASH-Sodium controlled diet study". Journal of the American Dietetic Association. 103 (10): 1339–1346. doi:10.1016/s0002-8223(03)01080-0. PMID 14520254.
^Wang, Faye Fangfei (10 January 2014). Law of Electronic Commercial Transactions: Contemporary Issues in the EU, US and China. Routledge. p. 154. ISBN978-1-134-11522-8.
^"Data, not privacy, is the real danger". NBC News. 4 February 2019.
External linksedit
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This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these template messages This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources Data collection news newspapers books scholar JSTOR April 2017 Learn how and when to remove this template message This article is in list format but may read better as prose You can help by converting this article if appropriate Editing help is available August 2021 Learn how and when to remove this template message Data collection or data gathering 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 research component in all study fields 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 evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed Example of data collection in the biological sciences Adelie penguins are identified and weighed each time they cross the automated weighbridge on their way to or from the sea 1 Regardless of the field of or preference for defining data quantitative or qualitative accurate data collection is essential to maintain research integrity The selection of appropriate data collection instruments existing modified or newly developed and delineated instructions for their correct use reduce the likelihood of errors Contents 1 Methodology 1 1 Tools 1 1 1 Data collection system 1 1 2 Data management platform 1 2 Data integrity issues 1 2 1 Quality assurance QA 1 2 2 User privacy issues 1 2 3 Quality control QC 2 See also 3 References 4 External linksMethodology editThis article is missing information about experiment sampling measurement and preprocessing Please expand the article to include this information Further details may exist on the talk page July 2023 See also scientific method Data collection and validation consist of four steps when it involves taking a census and seven steps when it involves sampling 3 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 4 The process provides both a baseline from which to measure and in certain cases an indication of what to improve Tools edit Data collection system edit Main article Data collection system Data management platform edit Main article Data management platform Data management platforms DMP are centralized storage and analytical systems for data mainly used in marketing DMPs exist to compile and transform large amounts of demand and supply data into discernible information Marketers may want to receive and utilize first second and third party data clarification needed DMPs enable this because they are the aggregate system of DSPs demand side platform and SSPs supply side platform DMPs are integral for optimizing and future advertising campaigns Data integrity issues edit The main reason for maintaining data integrity is to support the observation of errors in the data collection process Those errors may be made intentionally deliberate falsification or non intentionally random or systematic errors 5 There are two approaches that may protect data integrity and secure scientific validity of study results 6 Quality assurance all actions carried out before data collection Quality control all actions carried out during and after data collection Quality assurance QA edit Further information Quality assurance QA s focus is prevention which is primarily a cost effective activity to protect the integrity of data collection Standardization of protocol with comprehensive and detailed procedure descriptions for data collection are central for prevention The risk of failing to identify problems and errors in the research process is often 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 User privacy issues edit There are serious concerns about the integrity of individual user data collected by cloud computing because this data is transferred across countries that have different standards of protection for individual user data 7 Information processing has advanced to the level where user data can now be used to predict what an individual is saying before they even speak 8 Quality control QC edit Further information Quality control Since QC actions occur during or after the data collection all the details can be 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 Shadow effectSee also editControlled experiment Data acquisition Data curation Data management Observational study Sampling statistics Scientific data archiving Statistical survey Survey data collection Qualitative method Quantitative method Quantitative methods in criminology Data miningReferences edit Lescroel A L Ballard G Gremillet D Authier M Ainley D G 2014 Descamps Sebastien ed Antarctic Climate Change Extreme Events Disrupt Plastic Phenotypic Response in Adelie Penguins PLOS ONE 9 1 e85291 Bibcode 2014PLoSO 985291L doi 10 1371 journal pone 0085291 PMC 3906005 PMID 24489657 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 Bibcode 2018NatSD 580188V doi 10 1038 sdata 2018 188 PMC 6154282 PMID 30251992 Ziafati Bafarasat A 2021 Collecting and validating data A simple guide for researchers Advance Preprint https doi org 10 31124 advance 13637864 v1 Data Collection and Analysis By Dr Roger Sapsford Victor Jupp ISBN 0 7619 5046 X Northern Illinois University 2005 Data Collection Responsible Conduct in Data Management Retrieved June 8 2019 Most Marlene M Craddick Shirley Crawford Staci Redican Susan Rhodes Donna Rukenbrod Fran Laws Reesa October 2003 Dietary quality assurance processes of the DASH Sodium controlled diet study Journal of the American Dietetic Association 103 10 1339 1346 doi 10 1016 s0002 8223 03 01080 0 PMID 14520254 Wang Faye Fangfei 10 January 2014 Law of Electronic Commercial Transactions Contemporary Issues in the EU US and China Routledge p 154 ISBN 978 1 134 11522 8 Data not privacy is the real danger NBC News 4 February 2019 External links edit nbsp Wikimedia Commons has media related to Data collection All about data collection TechTarget com Portals nbsp Business and economics nbsp Science nbsp Mathematics nbsp Contents Retrieved from https en wikipedia org w index php title Data collection amp oldid 1213243808, wikipedia, wiki, book, books, library,
