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Data Quality Procedures in Survey Research: An Analysis and Framework for Doctoral Program Curricula

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Title: Data Quality Procedures in Survey Research: An Analysis and Framework for Doctoral Program Curricula
Language: English
Authors: Burleson, James, Bott, Gregory J., Carter, Michelle, Sarabadani, Jalal
Source: Journal of Information Systems Education. 2023 34(4):418-429.
Availability: Journal of Information Systems Education. e-mail: editor@jise.org; Web site: http://www.jise.org
Peer Reviewed: Y
Page Count: 14
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: Doctoral Programs, Curriculum Evaluation, Curriculum Development, Surveys, Research, Research Methodology, Data Collection, Data Interpretation, Best Practices, Validity, Doctoral Students, Student Research, Research Skills, Information Skills
ISSN: 1055-3096
2574-3872
Abstract: To ensure validity in survey research, it is imperative that we properly educate doctoral students on best practices in data quality procedures. A 14-year analysis of 679 studies in the AIS "Basket of 8" journals noted undercommunication in the most pertinent procedures, consistent across journals and time. Given recent calls for improvements in data transparency, scholars must be educated on the importance and methods for ensuring data quality. Thus, to guide the education of doctoral students, we present a "5-C Framework'' of data quality procedures derived from a wide-ranging literature review. Additionally, we describe a set of guidelines regarding enacting and communicating data quality procedures in survey research.
Abstractor: As Provided
Entry Date: 2023
Access URL: https://jise.org/Volume34/n4/JISE2023v34n4pp418-429.pdf
Accession Number: EJ1402339
Database: ERIC
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  Data: Data Quality Procedures in Survey Research: An Analysis and Framework for Doctoral Program Curricula
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  Data: To ensure validity in survey research, it is imperative that we properly educate doctoral students on best practices in data quality procedures. A 14-year analysis of 679 studies in the AIS "Basket of 8" journals noted undercommunication in the most pertinent procedures, consistent across journals and time. Given recent calls for improvements in data transparency, scholars must be educated on the importance and methods for ensuring data quality. Thus, to guide the education of doctoral students, we present a "5-C Framework'' of data quality procedures derived from a wide-ranging literature review. Additionally, we describe a set of guidelines regarding enacting and communicating data quality procedures in survey research.
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      Pagination:
        PageCount: 14
        StartPage: 418
    Subjects:
      – SubjectFull: Doctoral Programs
        Type: general
      – SubjectFull: Curriculum Evaluation
        Type: general
      – SubjectFull: Curriculum Development
        Type: general
      – SubjectFull: Surveys
        Type: general
      – SubjectFull: Research
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Data Collection
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      – SubjectFull: Data Interpretation
        Type: general
      – SubjectFull: Best Practices
        Type: general
      – SubjectFull: Validity
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      – SubjectFull: Doctoral Students
        Type: general
      – SubjectFull: Student Research
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      – SubjectFull: Research Skills
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      – SubjectFull: Information Skills
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      – TitleFull: Data Quality Procedures in Survey Research: An Analysis and Framework for Doctoral Program Curricula
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