Dataset Information

Confirmatory factor analysis of celebrity worship, digital literacy, and nostalgia: Dataset of Indonesians.

ABSTRACT: This dataset is a measurement of Celebrity Worship (CWS), Digital Literacy (DL), and Nostalgia (NA). The participants were (1) For CWS, N?=?3,223 people (181 males, 3042 females; M age?=?19.64 years old; SD age?=?3.13 years), (2) For DL, N?=?482 people (225 males, 257 females; M age?=?25.16 years old; SD age?=?8.54 years), (3) For NA, N?=?658 people (140 males, 518 females; M age?=?21.26 years old; SD age?=?1.93 years). The data was obtained using a survey via Google Forms in June 2018 and April-July 2020 in Indonesia. The analysis techniques were confirmatory factor analyses/CFA using LISREL and test of differences using JASP. The data could be used by the Indonesian Ministry of Communication and Informatics (KEMENKOMINFO), Indonesian Ministry of Education and Culture (KEMENDIKBUD), Indonesian Ministry of Youth and Sports (KEMENPORA), as well as social marketers to map the three constructs in Indonesian youth and to prevent the adverse effects and impacts of celebrity worship, digital illiteracy, and inappropriate mode of nostalgia.

SUBMITTER: Abraham J

PROVIDER: S-EPMC7586072 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Confirmatory factor analysis of celebrity worship, digital literacy, and nostalgia: Dataset of Indonesians.

Abraham Juneman J Ali Moondore Madalina MM Andangsari Esther Widhi EW Hartanti Lisa Esti Puji LEP

Data in brief 20201017

This dataset is a measurement of Celebrity Worship (CWS), Digital Literacy (DL), and Nostalgia (NA). The participants were (1) For CWS, N = 3,223 people (181 males, 3042 females; M age = 19.64 years old; SD age = 3.13 years), (2) For DL, N = 482 people (225 males, 257 females; M age = 25.16 years old; SD age = 8.54 years), (3) For NA, N = 658 people (140 males, 518 females; M age = 21.2 ...[more]

PMID: 33134446

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Project description:BACKGROUND:Electronic health (eHealth) literacy is needed to effectively engage with Web-based health resources. The 8-item eHealth literacy scale (eHEALS) is a commonly used self-report measure of eHealth literacy. Accumulated evidence has suggested that the eHEALS is unidimensional. However, a recent study by Sudbury-Riley and colleagues suggested that a theoretically-informed three-factor model fit better than a one-factor model. The 3 factors identified were awareness (2 items), skills (3 items), and evaluate (3 items). It is important to determine whether these findings can be replicated in other populations. OBJECTIVE:The aim of this cross-sectional study was to verify the three-factor eHEALS structure among magnetic resonance imaging (MRI) and computed tomography (CT) medical imaging outpatients. METHODS:MRI and CT outpatients were recruited consecutively in the waiting room of one major public hospital. Participants self-completed a touchscreen computer survey, assessing their sociodemographic, scan, and internet use characteristics. The eHEALS was administered to internet users, and the three-factor structure was tested using structural equation modeling. RESULTS:Of 405 invited patients, 87.4% (354/405) were interested in participating in the study, and of these, 75.7% (268/354) were eligible. Of the eligible participants, 95.5% (256/268) completed all eHEALS items. Factor loadings were 0.80 to 0.94 and statistically significant (P<.001). All reliability measures were acceptable (indicator reliability: awareness=.71-.89, skills=.78-.80, evaluate=.64-.79; composite reliability: awareness=.89, skills=.92, evaluate=.89; variance extracted estimates: awareness=.80, skills=.79, evaluate=.72). Two out of three goodness-of-fit indices were adequate (standardized root mean square residual (SRMR)=.038; comparative fit index (CFI)=.944; root mean square error of approximation (RMSEA)=.156). Item 3 was removed because of its significant correlation with item 2 (Lagrange multiplier [LM] estimate 104.02; P<.001) and high loading on 2 factors (LM estimate 91.11; P<.001). All 3 indices of the resulting 7-item model indicated goodness of fit (?211=11.3; SRMR=.013; CFI=.999; RMSEA=.011). CONCLUSIONS:The three-factor eHEALS structure was supported in this sample of MRI and CT medical imaging outpatients. Although further factorial validation studies are needed, these 3 scale factors may be used to identify individuals who could benefit from interventions to improve eHealth literacy awareness, skill, and evaluation competencies.

Project description:BackgroundAccurate and precise measures of health literacy (HL) is supportive for health policy making, tailoring health service design, and ensuring equitable access to health services. According to research, valid and reliable unidimensional HL measurement instruments explicitly targeted at young people (YP) are scarce. Thus, this study aims at assessing the psychometric properties of existing unidimensional instruments and developing an HL instrument suitable for YP aged 16-25 years.MethodsApplying the HLS19-Q47 in computer-assisted telephone interviews, we collected data in a representative sample comprising 890 YP aged 16-25 years in Norway. Applying the partial credit parameterization of the unidimensional Rasch model for polytomous data (PCM) and confirmatory factor analysis (CFA) with categorical variables, we evaluated the psychometric properties of the short versions of the HLS19-Q47; HLS19-Q12, HLS19-SF12, and HLS19-Q12-NO. A new 12-item short version for measuring HL in YP, HLS19-YP12, is suggested.ResultsThe HLS19-Q12 did not display sufficient fit to the PCM, and the HLS19-SF12 was not sufficiently unidimensional. Relative to the PCM, some items in the HLS19-Q12, the HLS19-SF12, and the HLS19-Q12-NO discriminated poorly between participants at high and at low locations on the underlying latent trait. We observed disordered response categories for some items in the HLS19-Q12 and the HLS19-SF12. A few items in the HLS19-Q12, the HLS19-SF12, and the HLS19-Q12-NO displayed either uniform or non-uniform differential item functioning. Applying one-factorial CFA, none of the aforementioned short versions achieved exact fit in terms of non-significant model chi-square statistic, or approximate fit in terms of SRMR ≤ .080 and all entries ≤ .10 that were observed in the respective residual matrix. The newly suggested parsimonious 12-item scale, HLS19-YP12, displayed sufficiently fit to the PCM and achieved approximate fit using one-factorial CFA.ConclusionsCompared to other parsimonious 12-item short versions of HLS19-Q47, the HLS19-YP12 has superior psychometric properties and unconditionally proved its unidimensionality. The HLS19-YP12 offers an efficient and much-needed screening tool for use among YP, which is likely a useful application in processes towards the development and evaluation of health policy and public health work, as well as for use in clinical settings.

Dataset Information

Confirmatory factor analysis of celebrity worship, digital literacy, and nostalgia: Dataset of Indonesians.

Publications

Confirmatory factor analysis of celebrity worship, digital literacy, and nostalgia: Dataset of Indonesians.

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