Abstract
BACKGROUND: The progression into the Digital Age has brought an array of novel skill requirements. Unlike traditional literacy, there are currently few measures that can reliably measure eHealth literacy. The Transactional Model of eHealth Literacy and subsequent Transactional eHealth Literacy Instrument may provide a feasible option for measuring eHealth literacy. OBJECTIVE: This instrument has yet to be validated, which is the aim of this study. In particular, this article was conducted to validate the TeHLI to see which components of the tool (how many and which components included) would be the best fit statistically and whether the tool applies to groups of different characteristics. METHODS: We conducted an online cross-sectional study among 236 Vietnamese young people. A exploratory factor analysis was used to identify the best fit model of the Transactional eHealth Literacy Instrument. A confirmatory factor analysis tested measurement invariance at four levels: configural, metric, scalar, and strict invariance. Only metric invariance was partially invariant, while the rest tested fully invariant. Even with partial metric invariance, there is reason to assume that functional, communicative, critical, and translational eHealth literacy (the four levels according to the transactional model) are consistently measured when deploying the Transactional eHealth Literacy Instrument across groups. RESULTS: The study findings substantiate that the most optimal composition of the TeHLI consists of four factors: functional, communicative, critical, and translational eHealth literacy, with RMSEA = 0.116; CFI = 0.907, and the highest internal consistency (Cronbach's α = 0.91, 0.92, 0.88, and 0.92 for each factor respectively). After using measurement invariance, that gender, education, marital status, age, location, and household economy do not influence the way participants to respond to the TeHLI to the point that would introduce measurement bias. In other word, using TeHLI across population groups should not produce error margins that substantially differ from each other. CONCLUSIONS: This study suggests the instrument can be used for comparisons across groups and has the potential to generate high-quality data usable for informing change agents as to whether a particular population is proficient enough to adopt novel eHealth innovations.