Design and Validation of Data Literacy Assessment Tool for Graduate Students

Document Type : Original Article


1 Associate Professor, Department of Knowledge and Information Science, Tabriz University, Tabriz, Iran

2 Master's degree, Department of Knowledge and Information Science, Tabriz University, Tabriz, Iran.

3 Associate Professor, Department of Knowledge and Information Science, Tabriz University, Tabriz, Iran.


Purpose: The present study aimed to develop a standardized assessment tool for measuring the data literacy of postgraduate students using a descriptive survey method.
Method: To develop the initial questionnaire for assessing students' opinions and to create the final data literacy assessment tool, we reviewed relevant sources on students' qualifications and competencies in data literacy. We extracted the most important information from these sources. In the next step, the designed questions were given to professors in the Knowledge and Information Science departments and other experts to measure formal and content validity. After obtaining the minimum content validity indicators for the initial questionnaire, it was distributed to 27 students from the Faculty of Educational Sciences and Psychology to assess its reliability. Cronbach's alpha coefficient calculated for this initial questionnaire was 0.91. After ensuring the questions met the minimum content validity indicators and checking their reliability index, the assessment tool was developed based on the opinions of graduate students. The questionnaire was then administered to 363 post-graduate students at the University of Tabriz, who were selected using stratified random sampling. The exploratory factor analysis test was used to analyze the data and identify important and essential dimensions of the data literacy assessment tool.
Findings: The results of the factor analysis of the research data were used to design and validate the data literacy assessment tool for post-graduate students. This involved reducing 20 indicators or variables to 4 factors or indicators, including: 1) understanding the data, 2) collecting, organizing, and evaluating the data, 3) analyzing and interpreting the data, and 4) ensuring data sharing complies with legal and citation issues.
Conclusion: This model accounts for 58% of the variance in data literacy, suggesting that the factor analysis and the variables studied are satisfactory. Therefore, it is recommended to utilize this assessment tool to gauge the level of data literacy among postgraduate students.


Main Subjects

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