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Abstract
Figures
Introduction
Pilot Survey Results
Exploratory Factor Analysis
Full Test Results
Confirmatory Factor Analysis
Discussion
Main Findings
Limitations and Future Studies
References
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Computer Science
Information Systems

Development and validation of the Internet Skills Scale (ISS)

Profile image of Ellen HelsperEllen HelsperProfile image of Alexander J A M Van DeursenAlexander J A M Van Deursen
https://doi.org/10.1080/1369118X.2015.1078834
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Abstract

Although a number of instruments have been used to measure Internet skills in nationally representative surveys, there are several challenges with the measures available: incompleteness and over-simplification, conceptual ambiguity, and the use of selfreports. Here, we aim to overcome these challenges by developing a set of reliable measures for use in research, practice, and policy evaluations based on a strong conceptual framework. To achieve this goal, we carried out a literature review of skills related studies to develop the initial Internet skills framework and associated instrument. After the development of this instrument, we used a three-fold approach to test the validity and reliability of the latent skill constructs and the corresponding items. The first step consisted of cognitive interviews held in both the UK and the Netherlands. Based on the cognitive interview results, we made several amendments to the proposed skill items to improve clarity. The second step consisted of a pilot survey of digital skills, both in the UK and in the Netherlands. During the final step, we examined the consistency of the five Internet skill scales and their characteristics when measured in a representative sample survey of Dutch Internet users. The result is a theoretical, empirically and cross-nationally consistent instrument consisting of five types of Internet skills: operational, navigation information, social, creative, and mobile.

Figures (17)
Table 1. Demographic profile (UK: N = 324; NL: N = 306).
Table 1. Demographic profile (UK: N = 324; NL: N = 306).
“The Information Navigation skill was reversed since it contained negatively worded items. Table 2. Scale characteristics (Overall N = 622, UK N= 317, NL N= 305).
“The Information Navigation skill was reversed since it contained negatively worded items. Table 2. Scale characteristics (Overall N = 622, UK N= 317, NL N= 305).
Table 4. Factorial invariance tests (Operational, Information Navigation, Social, and Creative scales). Note: All y* are significant at p <.001. This is not surprising since the factorial model is quite complex. The next step was to test whether the factor structures proposed in the previous section fit similarly in the UK and the Netherlands. We conducted CFA using AMOS with tests for factorial invariance. We tested for configural, metric, scalar, and uniqueness invariance (Table 4). Configural invariance indicates the same factor structure, Metric invariance indicates the same factor loadings, Scalar invariance indicates the same item intercepts, Uniqueness indicates the same unique error terms. For the purposes of scale construction we were interested mostly in configural and metric invariance because we needed, at the very least, the same factors to be identifiable within the Netherlands and the UK and for the items to load similarly on these different constructs. The full model including all factor structures (see Appendix 1 for coefficients and 2 for covariances and correlations) has a moderate to good fit for complex model indicators on the merged database (Kline,
Table 4. Factorial invariance tests (Operational, Information Navigation, Social, and Creative scales). Note: All y* are significant at p <.001. This is not surprising since the factorial model is quite complex. The next step was to test whether the factor structures proposed in the previous section fit similarly in the UK and the Netherlands. We conducted CFA using AMOS with tests for factorial invariance. We tested for configural, metric, scalar, and uniqueness invariance (Table 4). Configural invariance indicates the same factor structure, Metric invariance indicates the same factor loadings, Scalar invariance indicates the same item intercepts, Uniqueness indicates the same unique error terms. For the purposes of scale construction we were interested mostly in configural and metric invariance because we needed, at the very least, the same factors to be identifiable within the Netherlands and the UK and for the items to load similarly on these different constructs. The full model including all factor structures (see Appendix 1 for coefficients and 2 for covariances and correlations) has a moderate to good fit for complex model indicators on the merged database (Kline,
*y* difference significant at p <.01. **y? difference significant at p< .001. Table 3. x’ differences (df = 1) for paired construct test.
*y* difference significant at p <.01. **y? difference significant at p< .001. Table 3. x’ differences (df = 1) for paired construct test.
Table 5. Model fit on CFA for the individual factors. 2005): y* (510) = 1667.93, y*/df = 3.27; CFI = 0.93; RMSEA = 0.06 (ci. 0.06-0.06); AIC = 1977.93.
Table 5. Model fit on CFA for the individual factors. 2005): y* (510) = 1667.93, y*/df = 3.27; CFI = 0.93; RMSEA = 0.06 (ci. 0.06-0.06); AIC = 1977.93.
Table 8. CFA fit (N = 1337; weighted full population).
Table 8. CFA fit (N = 1337; weighted full population).
Table 7. Scale characteristics in Dutch Internet user population.
Table 7. Scale characteristics in Dutch Internet user population.
Table 6. Demographic profile Dutch Internet user sample (N= 1107, weighted N = 1337). GTOZ equEdss 17 Z7'10 & [BEM | TeysPawp ] Aq pepeow~moqd 4.2 Confirmatory factor analysis
Table 6. Demographic profile Dutch Internet user sample (N= 1107, weighted N = 1337). GTOZ equEdss 17 Z7'10 & [BEM | TeysPawp ] Aq pepeow~moqd 4.2 Confirmatory factor analysis
Note: Base. Dutch Internet Users (N = 1337). Table 9. Reliability (a) in different groups. socio-demographic groups, (2) testing for convergent and discriminant validity of the scales, and (3) testing whether the scale characteristics were consistent through random resamples of the population using bootstrapping techniques and whether they relate simi- larly for different socio-demographic groups.
Note: Base. Dutch Internet Users (N = 1337). Table 9. Reliability (a) in different groups. socio-demographic groups, (2) testing for convergent and discriminant validity of the scales, and (3) testing whether the scale characteristics were consistent through random resamples of the population using bootstrapping techniques and whether they relate simi- larly for different socio-demographic groups.
Table 11. Convergent and discriminant validity indicators skills scales.
Table 11. Convergent and discriminant validity indicators skills scales.
Table 12. Correlations between short scales in population survey of Dutch Internet users (N = 1337).
Table 12. Correlations between short scales in population survey of Dutch Internet users (N = 1337).
Table 10. Factor correlation and AVE? (on diagonal).
Table 10. Factor correlation and AVE? (on diagonal).
Note |: Italic items are added to create longer scales; depending on the aim of the research project these can be used to replace those on the short scale. Note Il: There is also a set of Critical (literacy) skills that are not included because they were shown to be individual context dependent and not easy to measure in general population survey research. Note III: The Information Navigation items are all negatively formulated. We recommend that future research use positively formulated items measuring the same skills. Table 13. Proposed items and factors to measure Internet skills.
Note |: Italic items are added to create longer scales; depending on the aim of the research project these can be used to replace those on the short scale. Note Il: There is also a set of Critical (literacy) skills that are not included because they were shown to be individual context dependent and not easy to measure in general population survey research. Note III: The Information Navigation items are all negatively formulated. We recommend that future research use positively formulated items measuring the same skills. Table 13. Proposed items and factors to measure Internet skills.
Factor structure CFA factor analysis
Factor structure CFA factor analysis
Construct and error term covariates and correlations CFA
Construct and error term covariates and correlations CFA
Appendix 2 Continued.
Appendix 2 Continued.

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    Mạnh Toàn Hồ

    Sustainability

    As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digit...

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    Digital Inclusion in Older Adults: A Comparison Between Face-to-Face and Blended Digital Literacy Workshops
    Brenda Rodríguez

    Frontiers in ICT

    As information and services are becoming more and more decentralized and they are often available in the cloud, an increasing number of older adults are expected to use Internet-based services-health, education, finance and others. For this reason, it seems important to plan models and/or strategies to allow the older adult population to acquire and enhance digital competencies more easily. The goal of this research is to show a blended workshop based on a Learning Management System (LMS) as a supporting tool for older adults' digital literacy. This blended workshop was based on the adoption of an instructional model and on prior experiences of the groups of elderly that participated in the face-to-face workshops. This study involved 98 adults aged 60 and above, 72 Females (68.5 ± 6.9) and 26 Males (73.3 ± 7.4). 61 older adults participated in the face-to-face workshop (FFG) on digital literacy and 37 participated in the blended workshop (BLG). Digital literacy increased at the post-evaluation after the workshops but even more for the BLG. Likewise, in the validation of the blended workshop the results were positive regarding ease of use, perceived usefulness, attitude toward using and intention to use, which showed that older adults believe that it is useful to implement this type of supporting systems for developing their digital competencies. Hence, it is possible to conclude that older adults are capable of learning and acquiring digital literacy skills as long as they are strongly motivated or they know the functional benefits related to ICT.

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    Rethinking digital skills in the era of compulsory computing: methods, measurement, policy and theory
    Grant Blank

    Information, Communication & Society

    Around the world, digital platforms have become the firstor only option for many everyday activities. The United Kingdom, for instance, is implementing a 'digital-by-default' e-government agenda, which has steadily digitized vital services such as taxes, pensions, and welfare. This pervasive digitization marks an important shift in the relationship between society and computing; people are compelled to use computers and the internet in order to accomplish the basic tasks. We suggest that this era of compulsory computing demands new ways of measuring and theorizing about digital skills, which remain a crucial dimension of the digital divide. In this article, we reexamine the theory and measurement of digital skills, making three contributions to understanding of how digital skills are encountered, acquired, and conceptualized. First, we introduce a new methodology to research skills: participant-observation of novices in the process of learning new skills along with interviews with the people who help them. Our ethnographically informed method leads us to a second contribution: a different theory of skills, which identifies three primary characteristics: (1) sequence, (2) simultaneity, and, most importantly, (3) path abstraction. Third, we argue that these characteristics suggest the need to change current ways skills are measured, and we also discuss the policy implications of this empirically informed theory.

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