Evaluating the quality of data in articles by faculty members of the Faculty of Pharmacy, Kerman University of Medical Sciences, based on the DQA model

Document Type : Original Article

Authors

1 Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran.

2 Scientometrics and Information Analysis Research Institute, Information Science and Technology Research Institute, Iran

3 Department of Information Science and Knowledge, Shahid Bahonar University of Kerman. Kerman. Iran

Abstract

Objective: The main purpose of this study is to evaluate the data quality of research articles authored by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences, based on the Data Quality Assessment (DQA) model.
Methodology: This applied research employs a descriptive-evaluative survey design. The statistical population consists of 340 articles published by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences between 2018 and 2022. A sample of 181 articles was randomly selected from the PubMed database using the Morgan table. Data extraction was conducted by first identifying relevant keywords, then applying key filters to extract data based on the research components of the DQA model: validity, reliability, timeliness/up-to-dateness, accuracy, integrity, and consistency. Data quality was evaluated using the Bazargan table scale and a one-sample t-test.
Findings: The findings indicate that, among the dimensions of data quality, "data validity" most frequently (13) received a "completely undesirable" rating, while "data integrity" least frequently (7) received a "completely desirable" rating. Conversely, "data up-to-dateness" (42) and "data consistency" (40) were identified as having the highest level of quality, categorized as "completely desirable." The results of the primary hypothesis test, with a mean data quality score of $\mu_0 = 3.83$, indicate that the articles are generally in a desirable state.
Conclusion: The study demonstrates that the data quality of the examined articles is generally desirable according to the DQA model. To further enhance the quality of scientific papers, it is recommended that researchers prioritize the collection of data from valid, reliable, and up-to-date sources, employ advanced statistical methods, and ensure the rigorous documentation of all data collection and processing stages. Additionally, efforts such as removing invalid data, correcting missing values, mitigating human errors, and utilizing machine learning algorithms for predictive analysis are essential for continuous improvement in research quality

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Main Subjects

References

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Sciences and Techniques of Information Management
Volume 12, Issue 1
March 2026
Pages 84-93

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