Factor Structure, Reliability and Validity of Attitudes of Biostatistics Scale

Harun, Mohd.; Sabri, Ahmad

doi:10.9734/bjmcs/2016/25707

Cited by 3 publications

(3 citation statements)

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“…We examined the validity of construct and latent variables using convergent and discriminant validity. Convergent validity was confirmed if the average variance extracted (AVE) value was ≥0.5 [ 54 ]. Discriminant validity was confirmed if the correlations between the 2 constructs were less than the square root of the AVE value [ 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

Ha¹,

Park²,

Lee³

et al. 2023

JMIR Form Res

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Background Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. Objective We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. Methods We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. Results The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation. Conclusions The results suggest the importance of the users’ performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users’ health motivation, thereby reducing users’ effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users’ habitual use behaviors.

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Section: Methodsmentioning

confidence: 99%

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

Ha¹,

Park²,

Lee³

et al. 2023

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…The five constructs of NC had Composite Reliability (CR) and Average Variance Extracted (AVE) values ranging between 0.551 and 0.811, and 0.292 and 0.589, respectively. This was a satisfactory indicator for convergent validity and reliability of the constructs, except for the construct relational skills which had a low AVE of 0.292 and CR of 0.551 that is, lower than the recommended AVE>0.5 and CR>0.6; hence was exempted in further analysis (Harun and Ahmad, 2016). The scores for each of the NC latent variable were generated using the weighted means then used for further analysis in the ordered logistic regression.…”

Section: Methodsmentioning

confidence: 99%

“…The mean VIF was 1.45, not exceeding 5.0 as shown in Table 3, implying absence of correlation among the explanatory variables (Jamal, 2017). Harun and Ahmad (2016) postulate that correlation >0.75 indicates high correlation. The pair-wise correlation for the categorical independent variable did not exceed 0.75 (Table 5), signifying absence of correlation.…”

Section: Statistical and Specification Diagnostic Tests Prior To Runmentioning

confidence: 99%

Role of networking capability, socio-economic and institutional characteristics on adoption tendencies of clean seed potato agri-enterprises in central Rift Valley, Kenya

Ong’ayo¹,

Gido²,

Ayuya³

et al. 2020

Afr. Crop Sci. J.

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Even though quality of seed is a major yield determinant in potato (Solanum tuberosum L.) production and global food security, inadequate availability of and access to high quality (certified) seed is a major challenge to potato producers in Sub-Saharan Africa. The objective of this study was to examine farmer’s adoption tendency towards decentralised clean seed potato multiplication agri-enterprise (CSPMAE) in Central Rift Valley, Kenya, which aids in implementation of stage matched entrepreneurial interventions. A cross-sectional survey was conducted using a semi-structured questionnaire on 54 seed potato producers and 192 non-seed potato producers, through multistage sampling. Ordered logistic regression analysis, based on the trans-theoretical model, revealed that household head, partner knowledge, gender, land size allocated for potato production, level of education, ownership of transport and/or storage facilities, frequency of interaction with certified seed potato producers and agricultural extension officers influenced adoption tendencies of CSPMAE, positively and significantly. Seed potato value chain stakeholder platforms, development workers and donors should prioritise their support for clean seed agri-enterprises to farmers with such traits. This is likely to lead to increased supply of access to clean seed, thereby improving potato yields in Kenya.

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Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study (Preprint)

Ha¹,

Park²,

Lee³

et al. 2022

Preprint

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BACKGROUND Prevention of the risk factors of metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. OBJECTIVE We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. METHODS We proposed a combined theoretical model based on the Health Belief Model, Unified Technology of the Acceptance and use of Technology 2, and perceived risk. We conducted an online survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. RESULTS The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable influencing the habitual use of wearable devices (β=.537, P<.001) and intention to continue the use of wearable devices (β=.848, P<.001). Habitual use was positively influenced by intention to continue the use of wearable devices (β=.439, P<.001). The indirect effects of performance expectancy were partially mediated by the intention to continue the use of wearable devices (β=.372, P=.03). Performance expectancy was influenced by health motivation (β=0.497, P<.001) and effort expectancy (β=.558, P<.001). Perceived vulnerability contributed more to health motivation (β=.562, P<.001) than perceived severity (β=.243, P=.008). CONCLUSIONS The habitual use of wearable devices was influenced by users’ expectation that the device productivity exceeded its required effort. Additionally, health motivation positively influenced the performance expectancy. The healthcare needs of middle-aged individuals with MetS risk factors should be explored to enhance the performance expectancy of managing health with wearable health devices and to increase their habitual use.

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Factor Structure, Reliability and Validity of Attitudes of Biostatistics Scale

Cited by 3 publications

References 0 publications

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study

Role of networking capability, socio-economic and institutional characteristics on adoption tendencies of clean seed potato agri-enterprises in central Rift Valley, Kenya

Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study (Preprint)

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