In today's digital era, digital competence and digitalization are poised to play transformative roles in shaping Europe’s economic and social future. Youth, as the future workforce, need to be equipped with the necessary digital skills, such as problem-solving, critical thinking, and digital communication, to meet the challenges posed by rapid technological advancements, changing job markets, and the increasing demand for digital literacy in everyday life. Recognizing this, the European Council Recommendation on Key Competences for Lifelong Learning (European Parliament and the Council of the European Union, 2006; Council of the European Union, 2018) has identified digital competence as a fundamental skill essential for personal well-being, employability, active citizenship, and social inclusion.
In line with this, the European Commission’s Joint Research Centre established the Digital Competence Framework (DigComp) as a key step toward standardizing the understanding and assessment of digital competence (Ferrari, 2012). The DigComp framework defines digital competence as a combination of skills, knowledge, and attitudes that enable individuals to engage responsibly in the digital environment and use digital technologies in a critical, collaborative, and creative way (Ferrari, 2013). From 2013 to subsequent updates (Vuorikari et al., 2016, 2022), the framework outlines 21 competencies grouped into five areas: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety, and Problem Solving.
Digital skills, integral to digital competence, in alignment with the European Qualifications Framework (European Commission, 2017), mean the ability to apply knowledge and use know-how to solve problems. In other words, skills are described as cognitive (involving the use of logical, intuitive, and creative thinking) and practical (involving manual dexterity and the use of methods, materials, tools, and instruments). While acknowledging other frameworks of digital competence, the development of the DigComp conceptual reference model reflects the EU's strategic effort to standardize digital skills assessment and to promote their use across educational, training, and employment contexts. To date, the DigComp framework has been implemented in practice in many European countries in various ways and for different purposes, most frequently in the formal education context (Kluzer & Pujol Priego, 2018). In the Serbian context, DigComp has been implemented as a framework for defining digital competence in national policy documents and as a foundation for the current strategy to enhance teachers' digital competence within formal education (The Ministry of Education, Science and Technological Development, 2021). However, the application of the DigComp framework for assessing students' digital competence remains underexplored, which is essential for identifying digital skill gaps and promoting targeted student training and education. To our knowledge, only one study empirically validated and created a performance-based assessment tool among Serbian primary school students based on DigComp’s framework (Kuzmanović, 2017). Adolescents and young adults aged 14 to 25 represent a key demographic, as their digital skills play a crucial role in their social engagement and preparation for future work challenges. However, there is currently a lack of an instrument tailored to assess the digital competencies of this target group. This study aims to develop and validate a tool to assess digital skills among Serbian youth, based on the DigComp conceptual framework.
Research Questions:
1. What is the latent structure of the developed digital skills instrument for Serbian youth based on the DigComp framework?
2. What is the factor structure of the digital skills instrument and how consistent are the obtained factors?
By constructing and exploring the factor structure and internal consistency of a tailored digital skills questionnaire for Serbian youth, the study seeks to contribute to theoretical and practical domains, as well as to the national digital education policy.

Sample and Procedure The sample consisted of 452 participants (48.7% male), including secondary school students and undergraduate students from the University of Belgrade, aged between 14 and 27 years (M = 18.16; SD = 2.79). University students (44.2%) were enrolled in humanities or technical sciences programs, while secondary school students (55.8%) attended vocational or academic high schools. All students and one parent of each underage student completed an informed written consent form before starting the survey. Participants completed questionnaires online using the Google platform during class time and were supervised by the study authors. The study was approved by the Ethics Committee for Research of the Faculty of Special Education and Rehabilitation, University of Belgrade. Development of Instrument and Content Validation Drawing on the DigComp framework (Vuorikari et al., 2022), the draft version of the questionnaire consisted of 54 items representing 21 competencies across five major categories: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety, and Problem-Solving. The content validation process involved experts and focused on assessing the items' relevance, comprehensiveness, and alignment with the definition of digital skills. Focus groups of students provided feedback on the clarity and understanding of the items. Based on feedback from experts and students, some items were modified, while others were removed, resulting in a final set of 45 items. Data Analyses To identify the latent structure of the questionnaire, Exploratory Factor Analysis (EFA) was conducted. Principal and common factor analysis methods were employed in SPSS. Sampling adequacy was assessed initially to ensure it was appropriate to proceed with the EFA (Henson & Roberts, 2006). To determine the number of factors to retain, various standard rules were applied: (a) eigenvalues of the unrotated factors greater or equal to 1 by the Kaiser–Guttman rule (Kaiser, 1960); (b) Cattell’s scree test (Cattell, 1966); (c) parallel analysis (Horn, 1965). Next, rotation strategies (varimax and promax) were applied to the retained factors, and the results were evaluated based on the following criteria: (a) retaining at least three items per factor with salient loadings (> .40); (b) excluding items that loaded above |.40| on more than one factor; (c) ensuring the proposed factor solution was meaningful and consistent with existing DigComp frameworks; (d) yielding acceptable internal consistency for each factor as indicated by Cronbach’s alpha coefficients ≥ .70 (Fabrigar et al., 1999).

EFA was conducted using the Principal Axis Factoring method due to the non-normal distribution of the data. Sample adequacy was met (KMO = 0.923), and Bartlett’s test of sphericity was significant (χ2(990) = 9208.815, p < .0001). Regarding the number of factors to be retained, the applied standard rules suggested five and seven factor solutions. Both options were assessed based on the above-mentioned criteria, with varimax and promax rotations. The seven-factor solution was considered a potentially better fit and more compatible with the most parsimonious and comprehensiveness factor solution. EFA with promax rotations led to the elimination of 12 out of 45 items, resulting in a final factor structure with 33 items distributed into seven factors. Corresponding to the DigComp conceptual model, with some modifications, the obtained factors were named as: Digital Content Creation and Editing (F1); Problem-Solving (F2); Critical Knowledge for Online Behavior and Communication (F3); Managing Data and Collaborating (F4); Know-How to Protect Health and Environment (F5); Device Protection and Security (F6); Evaluating Information and Digital Content (F7). The retained factors accounted for 50% of the total variance. Acceptable inter-factor correlations ranging from 0.17 to 0.60 indicated that the factors represented distinct but interconnected digital skill constructs. The factors were found to be highly reliable, with Cronbach’s alphas ranging from .70 to .85, and the overall Cronbach α was .92. The results provide insights into the structure of digital skills among youth, revealing distinct cognitive and practical skills. These findings contribute to understanding the latent factors underlying digital skills and suggest a comprehensive assessment framework aligned with the DigComp framework. Further validation through confirmatory factor analysis is needed to confirm the robustness, generalizability, and model fit across different contexts. These results lay the groundwork for future research and the development of a refined instrument to assess digital skills.

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