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Data Sharing and Data Integrity


Götz, Martin; Field, James G (2022). Data Sharing and Data Integrity. In: Murphy, Kevin R. Data, Methods and Theory in the Organizational Sciences. New York: Taylor & Francis, 49-72.

Abstract

Given current concerns about lack of replicability (including the reluctance of many journals to publish replications) and challenges regarding the credibility of research, what can be done to enhance data sharing and reduce barriers that impede data sharing? Given the increased sophistication of analytic techniques, the ability to collect data via automated means (e.g., web-based surveys), and tools that enable sophisticated data creation and manipulation, how will we be able to ensure that data are true and accurate and not manipulated or created? What are the long-term implications of manipulated or created datasets, particularly given the strong rewards associated with research publications and the pressure to publish high volumes of research? What can be done to create an infrastructure to ensure the highest integrity in data collection and analysis, detect examples of low data integrity, and effectively act once such examples are found.

Abstract

Given current concerns about lack of replicability (including the reluctance of many journals to publish replications) and challenges regarding the credibility of research, what can be done to enhance data sharing and reduce barriers that impede data sharing? Given the increased sophistication of analytic techniques, the ability to collect data via automated means (e.g., web-based surveys), and tools that enable sophisticated data creation and manipulation, how will we be able to ensure that data are true and accurate and not manipulated or created? What are the long-term implications of manipulated or created datasets, particularly given the strong rewards associated with research publications and the pressure to publish high volumes of research? What can be done to create an infrastructure to ensure the highest integrity in data collection and analysis, detect examples of low data integrity, and effectively act once such examples are found.

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Additional indexing

Item Type:Book Section, not_refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Language:English
Date:11 February 2022
Deposited On:02 Mar 2022 14:10
Last Modified:22 Mar 2024 04:48
Publisher:Taylor & Francis
ISBN:9781003015000
OA Status:Closed
Publisher DOI:https://doi.org/10.4324/9781003015000-4
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