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This study examines whether the Internet-based questionnaire is psychometrically equivalent to the paper-based questionnaire. A random sample of 2,400 teachers in Taiwan was divided into experimental and control groups. The experimental group was invited to complete the electronic form of the Chinese version of Center for Epidemiologic Studies Depression Scale (CES-D) placed on the Internet, whereas the control group was invited to complete the paper-based CES-D, which they received by mail. The multisample invariance approach, derived from structural equation modeling (SEM), was applied to analyze the collected data. The analytical results show that the two groups have equivalent factor structures in the CES-D. That is, the items in CES-D function equivalently in the two groups. Then the equality of latent mean test was performed. The latent means of "depressed mood," "positive affect," and "interpersonal problems" in CES-D are not significantly different between these two groups. However, the difference in the "somatic symptoms" latent means between these two groups is statistically significant at alpha = 0.01. But the Cohen's d statistics indicates that such differences in latent means do not apparently lead to a meaningful effect size in practice. Both CES-D questionnaires exhibit equal validity, reliability, and factor structures and exhibit a little difference in latent means. Therefore, the Internet-based questionnaire represents a promising alternative to the paper-based questionnaire.

Coronavirus disease 2019 (COVID-19) has caused a global pandemic and exerted a profound physiological and mental impact on the public. Due to anxiety from being bombarded by information from the news and social media, people may constantly read and repost, with a fear of missing out (FOMO), information about COVID-19 on social media. So far, there has been little research on COVID-19 FOMO. We therefore compiled the COVID-19 information fear of missing out scale (CIFS) and administered it to 1178 adults in Taiwan to identify the possible factors influencing CIFS scores. We demonstrated that the CIFS had good reliability, factor validity, and criterion validity. With regard to demographic variables, we found that gender, marital status, travel time to the nearest hospital, and educational background influenced CIFS scores. In contrast, the participant age and whether he or she lived in an urban area did not affect the CIFS scores. With regard to social media usage, social media usage time (r = 0.025) and the numbers of COVID-19-related posts read on social media (r = 0.117) or instant messaging (r = 0.169) were not highly correlated with CIFS scores. Rather, CIFS scores were found to be significantly correlated to the frequency of reposting COVID-19-related information on social media (r = 0.497) and on instant messaging (r = 0.447). These results indicate that CIFS scores are closely associated not with passive browsing on social media but with the frequency at which an individual actively reposts information. In other words, what creates CIF is not an overabundance of information (i.e., an infodemic) but the active reposting and interpretation of information. Individual autonomy for interpretation of the received information and self-determination about reposting are key factors for COVID-19 information FOMO. When facing the COVID-19-related news on social media, it is the active information-related FOMO, not the passive infodemic, that influences our social media usage.

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