PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy Among University Students

2026-01-02

Behaviour & Information Technology

AI Summary

This study addresses the current lack of effective tools for assessing data privacy literacy among university students in digital environments. It proposes and empirically validates a novel psychometric scale—the PDPL Metric—comprising 24 items that integrate six core privacy constructs into a unified higher-order structure. Through survey data, principal component analysis, and second-order confirmatory factor analysis, the scale demonstrates strong structural validity and internal consistency. Findings reveal no significant differences in overall PDPL levels across gender or academic year; however, a significant disparity emerges between domestic and international students. These results underscore the framework’s applicability and innovative contribution to evaluating digital literacy in higher education contexts.

Technology Category

• Machine Learning: Privacy
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Personal data privacy literacy (PDPL) refers to a collection of digital literacy skills related to an individuals ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometric scale (PDPL Metric) designed to measure data privacy literacy among university students, focusing on six key privacy constructs: perceived risk of data misuse, expectations of informed consent, general privacy concern, privacy management awareness, privacy-utility trade-off acceptance, and perceived importance of data security. A 24-item questionnaire was developed and administered to students at U.S.-based research universities. Principal components analysis confirmed the unidimensionality and internal consistency of each construct, and a second-order analysis supported the integration of all six into a unified PDPL construct. No differences in PDPL were found based on basic demographic variables like academic level and gender, although a difference was found based on domestic/international status. The findings of this study offer a validated framework for assessing personal data privacy literacy within the higher education context and support the integration of the core constructs into higher education programs, organizational policies, and digital literacy initiatives on university campuses.

Problem

• Personal data privacy literacy
• Measurement scale
• University students
• Digital literacy
• Privacy constructs

Innovation

• Personal Data Privacy Literacy
• Psychometric Scale Validation
• Privacy Constructs
• Digital Literacy Assessment
• Higher Education