Quality of Students Register Statistics: A Case Study University of Kurdistan

Document Type : Original Article

Authors

1 PhD. Student in Higher Education Development Planning, Department of Education, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.

2 Assistant Professor, Department of Economics, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.

3 Professor, Department of Education, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, Iran.

10.48301/kssa.2021.287020.1539

Abstract

Nowadays, majority of organizations rely to a large extent on information systems in conducting their affairs. Consequently, the quality of information obtained from these systems has a crucial effect on the work process of organizations. Low-quality data results in incurring high costs in organizations leading to lower efficiency and productivity. Improving data quality is a suitable and cost-effective solution to this problem. To improve the information quality, one must consider improving the design and performance of these information systems, and these demands knowing the sense and concept of data quality as a product, knowing methods of measuring it, and taking measures in properly designing and operating the system. Consequently, this study aimed to evaluate the quality of student registration data of University of Kurdistan in general and also in individual work areas of the registration statistics system. For this purpose, a combination of quantitative-qualitative and exploratory research methods were applied. In the qualitative part of the research, employing a library research method, theoretical concepts were presented, the data quality indicators were identified and the most important indicators selected. Accordingly, the “registration statistics quality evaluation checklist” was developed using expert opinion. In the quantitative part of the research, Golestan Educational System was monitored as a field study, and the checklist was completed and analyzed. The results indicated that students’ registration statistics quality was 79 percent. The quality of statistics in identifying, registering, transferring and presenting areas were 76, 85, 92, 54, and 63 percent, respectively.

Keywords

References

Abate, M. L., Diegert, K. V., & Allen, H. W. (1998). A Hierarchical Approach to Improving Data Quality. Data Quality, 4(1), 365-369. https://www.semanticscholar.org/paper/ A-Hierarchical-Approach-to-Improving-Data-Quality-Abate-Diegert/4548003c9c8 9d50b1a3045bfa9970097b7c3b6c9
Almasifard, M., Bakhsham, M., Karimi, H., & Behvar, S. (2021). Designing a pathology model of academic entrepreneurship development and knowledge commercialization with a structural interpretive approach. Karafan Quarterly Scientific Journal, 17(5), 87-110. https://doi.org/10.48301/kssa.2021.128449
Batini, C., & Scannapieco, M. (2016). Data and Information Quality: Dimensions, Principles and Techniques. Springer Cham. https://doi.org/10.1007/978-3-319-24106-7
Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1-10. https://doi.org/10.5334/dsj-2015-002
Ghafari, M., Azar, A., & Shabbak, A. (2018). Causal Mapping in Managing Statistical Outputs with Data Quality Approach. Iranian Journal Of Information Processing & Management 33(3), 1053-1076. https://www.sid.ir/paper/376968/en
Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management 4(2), 168-193. https://www.econstor.eu/ handle/10419/188448
Kahn, B. K., Strong, D. M., & Wang, R. Y. (1997, October 24-26). A Model for Delivering Quality Information as Product and Service. International Conference on Information Quality., Cambridge, Massachusetts, USA. https://www.researchgate.net/publication/220918643 _A_Model_for_Delivering_Quality_Information_as_Product_and_Service
Kent, W. (2012). Data and Reality: A Timeless Perspective on Perceiving and Managing Information (3 ed.). Technics publications. https://books.google.com/books?id=4hv gBgAAQBAJ&printsec=frontcover#v=onepage&q&f=false
Kiani, K., & Rashidi, S. (2009). Determining the quality indicators of the register data to evaluate the quality of mentioned data. Report of the research plan approved by the Statistical Research and Training Cente.
Lukyanenko, R., Wiggins, A., & Rosser, H. K. (2020). Citizen science: An information quality research frontier. Information Systems Frontiers, 22(4), 961-983. https://doi.org/10.100 7/s10796-019-09915-z
Nourmohammadi, M., & Navvabpour, H. (2005). Introduction to register statistics systems. Statistical Center of Iran.
Orr, K. (1998). Data quality and systems theory. Communications of the Association for Computing Machinery, 41(2), 66-71. https://dl.acm.org/doi/pdf/10.1145/269012.269023
Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications of the Association for Computing Machinery, 45(4), 211-218. https://doi.org/10.1145/5052 48.506010
Rahimi, H., & Aghababaei, R. (2020). The effect of authenic leadership on human resource creativity: Mediating role of internal motivation (Case study: staff in University of Kashan). Karafan Quarterly Scientific Journal, 16(2), 121-140. https://karafan.tvu.a c.ir/article_105317.html?lang=en
Redman, T. C. (2016, September 22). Bad Data Costs the U.S. $3 Trillion Per Year. Harvard Business School Publishing. https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year
Shankaranarayanan, G., & Blake, R. (2017). From content to context: The evolution and growth of data quality research. Journal of Data and Information Quality, 8(2), 1-28. https://doi. org/10.1145/2996198
Shanks, G. G., & Darke, P. (1998, October 25). Understanding Data Quality and Data Warehousing: A Semiotic Approach. Information Quality, Cambridge, Massachusetts, USA. http://18.4.38.158/ICIQ/Documents/IQ%20Conference%201998/Papers/Un derstandDQinDataWarehouseSemioticAppr.pdf
Slone, J. P. (2006). Information quality strategy: An empirical investigation of the relationship between information quality improvements and organizational outcomes [Doctoral, Capella University]. Minneapolis, Minnesota. https://web.mit.edu/smadnick/www/IQ% 20Dissertations/Skip.Slone.Dissertation.pdf
Timmerman, Y., & Bronselaer, A. (2019). Measuring data quality in information systems research. Decision Support Systems, 126, 113138. https://doi.org/10.1016/j.dss.2019.113138
Torres, R., & Sidorova, A. (2019). Reconceptualizing information quality as effective use in the context of business intelligence and analytics. International Journal of Information Management, 49(3), 316-329. https://doi.org/10.1016/j.ijinfomgt.2019.05.028
Wahyudi, A., Kuk, G., & Janssen, M. (2018). A Process Pattern Model for Tackling and Improving Big Data Quality. Information Systems Frontiers, 20(3), 457-469. https:// doi.org/10.1007/s10796-017-9822-7
Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the Association for Computing Machinery, 39(11), 86-95. https://doi .org/10.1145/240455.240479
Wand, Y., & Weber, R. (1995). On the deep structure of information systems. Information Systems Journal, 5(3), 203-223. https://doi.org/10.1111/j.1365-2575.1995.tb00108. x
Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.or g/10.1080/07421222.1996.11518099
Zakeri, B. (2007). Structured methods of analysis and design of information systems (10 ed.). Industrial Management Organization. https://www.gisoom.com/book/1425354/
Karafan Quarterly Scientific Journal
Volume 19, Special Issue
Humanities
August 2022
Pages 255-278

Files

History

  • Receive Date: 15 June 2021
  • Revise Date: 21 September 2021
  • Accept Date: 02 October 2021

Share

How to cite

Statistics