The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of perceived learning outcomes whereas instructor–student dialogue was a determinant of student initiative. University support had no significant relationship with either perceived learning outcomes or student initiative. In terms of learning effectiveness, both perceived learning outcomes and student initiative determined student satisfaction. The results identified that student–student dialogue, course design, and instructor–student dialogue were the key predictive factors of CoOL learning effectiveness, which may determine the ultimate success of CoOL.
This study aimed to establish the translation adequacy and examine the psychometric properties of Face Mask Use Scale (FMUS). Methods: This methodological study employed a cross-sectional design with repeated measures. Phase 1 examined the equivalence and relevance of English and Chinese versions of FMUS. Phase 2 examined the internal consistency, stability and construct validity. Different sample batches (213 university students and 971 general public) were used appropriately for psychometric testing. The 2-phase data were collected between January and April 2017. Results: In Phase 1, the semantic equivalence and relevance (item-and scale-level content-validity-index=100%) was satisfactory. Furthermore, from 133 paired test-retest responses, the quadratic weighted kappa (.53~.73, p<.001) and Intraclass Correlation Coefficient (ICC=.81) between the English and Chinese version of FMUS were satisfactory. In Phase 2, FMUS demonstrated satisfactory internal consistency (Cronbach's ⍺=.80~.81; corrected item-total correlation coefficients=.46~.67) and two-week test-retest stability (ICC=.84). The known-groups method (t=3.08, p<.001), exploratory (71.10% of total variance in two-factor model) and confirmatory factory analysis (x 2 /df=4.02, Root Mean Square Residual=.03, Root Mean Square Error of Approximation=.06, Goodness of Fit Index=.99, Comparative Fit Index=.99) were all satisfactory for establishing the construct validity. Conclusion: The FMUS has an equivalence Chinese and English versions, satisfactory reliability and validity for measuring the practice of face mask use. This poses clinical and research implications for those community health nurses who works on respiratory protection. Further research should be conducted on the 'negligent practice' of FMU.
Background and aimsCompulsive buying (CB) is a behavioral addiction that is conceptualized as an obsessive–compulsive and impulsive–control disorder. The Richmond Compulsive Buying Scale (RCBS), a six-item self-reporting instrument that has been validated worldwide, was developed based on this theoretical background. This study aimed to adapt RCBS to the Chinese population (RCBS-TC) to guide future national and international prevalence studies.MethodsThis methodological study was conducted in two phases. Phase 1 involved the forward and backward translation of RCBS, the content and face validation of the RCBS, and the evaluation of its translation adequacy. Phase 2 involved the psychometric testing of RCBS-TC for its internal consistency, stability, and construct validity using confirmatory factor analysis (CFA).ResultsIn Phase 1, RCBS-TC obtained satisfactory item-level (I-CVI = 83.3%–100%) and scale-level content validity index (CVI/AVE = 97.2%), comprehensibility (100%), and translation adequacy [intraclass correlation coefficient (ICC) = 0.858]. In Phase 2, based on data collected from 821 adults, RCBS-TC demonstrated a satisfactory internal consistency (Cronbach’s α = .88; corrected item-total correlation coefficients = 0.61–0.78) 2-week test–retest reliability (ICC = 0.82 based on 61 university students). For construct validation, the CFA results indicated that the corrected first-order two-factor models were acceptable with the same goodness-of-fit indices (χ2/df = 8.56, CFI = 0.99, NFI = 0.98, IFI = 0.99, and RMSEA = 0.09). The 2-week test–retest reliability of RCBS-TC (n = 61) was also satisfactory (ICC = 0.82).Discussion and conclusionsThis methodological study adopted appropriate and stringent procedures to ensure that the translation and validation of RCBS-TC was of quality. The results indicate that this scale has a satisfactory reliability and validity for the Chinese population.
Highlights Time series plot of network density can serve as early detection of pandemic development. Pandemic progression can be tracked through the association of network density and air travel data. The application of network density on detection of the pandemic risk and its association with the air travel data may help optimize timely containment strategies to mitigate the outbreak of infectious diseases.
A survey study is a research method commonly used to quantify population characteristics in biostatistics and public health research, two fields that often involve sensitive questions. However, if answering sensitive questions could cause social undesirability, respondents may not provide honest responses to questions that are asked directly. To mitigate the response distortion arising from dishonest answers to sensitive questions, the randomized response technique (RRT) is a useful and effective statistical method. However, research has seldom addressed how to apply the RRT in public health research using an online survey with multiple sensitive questions. Thus, we help fill this research gap by employing an innovative unrelated question design method. To illustrate how the RRT can be implemented in a multivariate analysis setting, we conducted a survey study to examine the factors affecting the intention of illegal waste disposal. This study demonstrates an application of the RRT to investigate the factors affecting people’s intention of illegal waste disposal. The potential factors of the intention were adopted from the theory of planned behavior and the general deterrence theory, and a self-administered online questionnaire was employed to collect data. Using the RRT, a covariance matrix was extracted for examining the hypothesized model via structural equation modeling. The survey results show that people’s attitude toward the behavior and their perceived behavioral control significantly positively affect their intention. This paper is useful for showing researchers and policymakers how to conduct surveys in environmental or public health related research that involves multiple sensitive questions.
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