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Development of a scale for data quality assessment in automated library systems
A credible scale based on the opinions of system users was developed to evaluate and assess data quality in automated library systems (ALS). Development and testing were carried out in two stages. In the first stage, 77 dimensions for data quality which had been previously identified through a syste...
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Published in: | Library & information science research 2019-01, Vol.41 (1), p.78-84 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A credible scale based on the opinions of system users was developed to evaluate and assess data quality in automated library systems (ALS). Development and testing were carried out in two stages. In the first stage, 77 dimensions for data quality which had been previously identified through a systematic literature review were used to develop scale items. The first draft of the scale was then distributed among a target population of ALS experts to solicit their opinions on the scale and the items. In the second stage, a revised version of the scale was distributed among the main study population, which included end users of the target systems. This stage used factor analysis to determine the final draft of the scale, which consists of 4 factors and 62 items. The 4 factors were named after the qualities of their associated items: Data Content Quality, Data Organizational Quality, Data Presentation Quality, and Data Usage Quality. This scale can help system managers identify and resolve potential problems in the systems they manage and can also aid in evaluating the quality of data sources based on the opinions of end users.
•A scale was developed for assessing data quality in automated library systems.•Expert and user opinions formed the basis for scale development.•The scale helps system managers determine and resolve potential systems problems.•The scale also helps to evaluate the quality of data sources. |
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ISSN: | 0740-8188 1873-1848 |
DOI: | 10.1016/j.lisr.2019.02.005 |