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Amanda M. Y. Chu
Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong

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Journal article
Published: 20 August 2021 in Education Sciences
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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.

ACS Style

Jenny T. Y. Tsang; Mike K. P. So; Andy C. Y. Chong; Benson S. Y. Lam; Amanda M. Y. Chu. Higher Education during the Pandemic: The Predictive Factors of Learning Effectiveness in COVID-19 Online Learning. Education Sciences 2021, 11, 446 .

AMA Style

Jenny T. Y. Tsang, Mike K. P. So, Andy C. Y. Chong, Benson S. Y. Lam, Amanda M. Y. Chu. Higher Education during the Pandemic: The Predictive Factors of Learning Effectiveness in COVID-19 Online Learning. Education Sciences. 2021; 11 (8):446.

Chicago/Turabian Style

Jenny T. Y. Tsang; Mike K. P. So; Andy C. Y. Chong; Benson S. Y. Lam; Amanda M. Y. Chu. 2021. "Higher Education during the Pandemic: The Predictive Factors of Learning Effectiveness in COVID-19 Online Learning." Education Sciences 11, no. 8: 446.

Journal article
Published: 18 August 2021 in Sustainability
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Illegal waste dumping has become a threat to human health and the global environment. In Hong Kong, the government has proposed a quantity-based municipal solid waste charging scheme to reduce waste. However, individuals may still dispose of waste improperly, even if such a scheme has been implemented. In this study, the neutralization theory was adopted and an online survey with 273 respondents was conducted to examine the reasons for improper dumping intentions. A principal component analysis identified two types of neutralization: intrinsic neutralization (including denial of responsibility, denial of injury, and defense of necessity) and extrinsic neutralization (including condemnation of the condemners and appeal to higher loyalties). A regression analysis showed that intrinsic neutralization and gender were significant factors for illegal waste dumping intentions when attitude toward illegal waste dumping was controlled.

ACS Style

Amanda M. Y. Chu. Illegal Waste Dumping under a Municipal Solid Waste Charging Scheme: Application of the Neutralization Theory. Sustainability 2021, 13, 9279 .

AMA Style

Amanda M. Y. Chu. Illegal Waste Dumping under a Municipal Solid Waste Charging Scheme: Application of the Neutralization Theory. Sustainability. 2021; 13 (16):9279.

Chicago/Turabian Style

Amanda M. Y. Chu. 2021. "Illegal Waste Dumping under a Municipal Solid Waste Charging Scheme: Application of the Neutralization Theory." Sustainability 13, no. 16: 9279.

Special issue paper
Published: 16 July 2021 in Stat
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The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

ACS Style

Amanda M. Y. Chu; Lupe S. H. Chan; Mike K. P. So. Stochastic actor‐oriented modelling of the impact of COVID‐19 on financial network evolution. Stat 2021, 10, e408 .

AMA Style

Amanda M. Y. Chu, Lupe S. H. Chan, Mike K. P. So. Stochastic actor‐oriented modelling of the impact of COVID‐19 on financial network evolution. Stat. 2021; 10 (1):e408.

Chicago/Turabian Style

Amanda M. Y. Chu; Lupe S. H. Chan; Mike K. P. So. 2021. "Stochastic actor‐oriented modelling of the impact of COVID‐19 on financial network evolution." Stat 10, no. 1: e408.

Advanced review
Published: 22 June 2021 in WIREs Computational Statistics
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Since the introduction of ARCH models close to 40 years ago, a wide range of models for volatility estimation and prediction have been developed and integrated into asset allocation, financial derivative pricing, and financial risk management. Research has also been very active in extending volatility modeling to dependence modeling and in developing our understanding of risk and uncertainty in financial systems. This paper presents a review on the statistical modeling on volatility and dynamic dependence of financial returns. In addition, we present a real data example using a time-varying copula model to estimate the dynamic dependence of stock returns. Research on volatility and dynamic dependence modeling will continue to encounter statistical and computational challenges; it is necessary to persist in dealing with the 3H (high dimension, high frequency, high complexity) paradigm in modeling. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Models > Nonlinear Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

ACS Style

Mike K. P. So; Amanda M. Y. Chu; Cliff C. Y. Lo; Chun Yin Ip. Volatility and dynamic dependence modeling: Review, applications, and financial risk management. WIREs Computational Statistics 2021, e1567 .

AMA Style

Mike K. P. So, Amanda M. Y. Chu, Cliff C. Y. Lo, Chun Yin Ip. Volatility and dynamic dependence modeling: Review, applications, and financial risk management. WIREs Computational Statistics. 2021; ():e1567.

Chicago/Turabian Style

Mike K. P. So; Amanda M. Y. Chu; Cliff C. Y. Lo; Chun Yin Ip. 2021. "Volatility and dynamic dependence modeling: Review, applications, and financial risk management." WIREs Computational Statistics , no. : e1567.

Journal article
Published: 30 April 2021 in Sustainability
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The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instructors in an unprecedented way. The majority of educational establishments were forced to take their courses online within a very short period of time, and both instructors and students had to learn to navigate the digital array of courses without much training. Our study examined factors that affect students’ attitude toward online teaching and learning during the COVID-19 pandemic. It is different from other online learning studies where online courses are mostly a method of choice, with suitable support from institutions and expectation from instructors and students, rather than a contingency. Under this specific environment, we utilized an online survey to collect students’ feedback from eleven universities across Hong Kong. Using partial least squares for analysis on the 400 valid samples we received, we found that peer interactions and course design have the most salient impact on students’ attitude, whereas interactions with instructors has no effect at all on students’ attitude. Furthermore, we also provide suggestions on using the existing technologies purchased during COVID-19 for a more sustainable learning environment going forward.

ACS Style

Amanda Chu; Connie Liu; Mike So; Benson Lam. Factors for Sustainable Online Learning in Higher Education during the COVID-19 Pandemic. Sustainability 2021, 13, 5038 .

AMA Style

Amanda Chu, Connie Liu, Mike So, Benson Lam. Factors for Sustainable Online Learning in Higher Education during the COVID-19 Pandemic. Sustainability. 2021; 13 (9):5038.

Chicago/Turabian Style

Amanda Chu; Connie Liu; Mike So; Benson Lam. 2021. "Factors for Sustainable Online Learning in Higher Education during the COVID-19 Pandemic." Sustainability 13, no. 9: 5038.

Journal article
Published: 19 March 2021 in International Journal of Environmental Research and Public Health
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In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.

ACS Style

Amanda Chu; Thomas Chan; Mike So; Wing-Keung Wong. Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model. International Journal of Environmental Research and Public Health 2021, 18, 3195 .

AMA Style

Amanda Chu, Thomas Chan, Mike So, Wing-Keung Wong. Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model. International Journal of Environmental Research and Public Health. 2021; 18 (6):3195.

Chicago/Turabian Style

Amanda Chu; Thomas Chan; Mike So; Wing-Keung Wong. 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model." International Journal of Environmental Research and Public Health 18, no. 6: 3195.

Correspondence
Published: 11 March 2021 in Travel Medicine and Infectious Disease
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ACS Style

Amanda M.Y. Chu; Agnes Tiwari; Jacky N.L. Chan; Mike K.P. So. Are travel restrictions helpful to control the global COVID-19 outbreak? Travel Medicine and Infectious Disease 2021, 41, 102021 -102021.

AMA Style

Amanda M.Y. Chu, Agnes Tiwari, Jacky N.L. Chan, Mike K.P. So. Are travel restrictions helpful to control the global COVID-19 outbreak? Travel Medicine and Infectious Disease. 2021; 41 ():102021-102021.

Chicago/Turabian Style

Amanda M.Y. Chu; Agnes Tiwari; Jacky N.L. Chan; Mike K.P. So. 2021. "Are travel restrictions helpful to control the global COVID-19 outbreak?" Travel Medicine and Infectious Disease 41, no. : 102021-102021.

Preprint content
Published: 21 January 2021
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UNSTRUCTURED Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

ACS Style

Amanda My Chu; Jacky Nl Chan; Jenny Ty Tsang; Agnes Tiwari; Mike Kp So. Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis (Preprint). 2021, 1 .

AMA Style

Amanda My Chu, Jacky Nl Chan, Jenny Ty Tsang, Agnes Tiwari, Mike Kp So. Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis (Preprint). . 2021; ():1.

Chicago/Turabian Style

Amanda My Chu; Jacky Nl Chan; Jenny Ty Tsang; Agnes Tiwari; Mike Kp So. 2021. "Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis (Preprint)." , no. : 1.

Journal article
Published: 21 January 2021 in JMIR Public Health and Surveillance
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Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

ACS Style

Amanda M.Y. Chu; Jacky N.L. Chan; Jenny T.Y. Tsang; Agnes Tiwari; Mike K.P. So. Analyzing cross-country pandemic connectedness in COVID-19: Network analysis using a spatial-temporal database (Preprint). JMIR Public Health and Surveillance 2021, 7, e27317 .

AMA Style

Amanda M.Y. Chu, Jacky N.L. Chan, Jenny T.Y. Tsang, Agnes Tiwari, Mike K.P. So. Analyzing cross-country pandemic connectedness in COVID-19: Network analysis using a spatial-temporal database (Preprint). JMIR Public Health and Surveillance. 2021; 7 (3):e27317.

Chicago/Turabian Style

Amanda M.Y. Chu; Jacky N.L. Chan; Jenny T.Y. Tsang; Agnes Tiwari; Mike K.P. So. 2021. "Analyzing cross-country pandemic connectedness in COVID-19: Network analysis using a spatial-temporal database (Preprint)." JMIR Public Health and Surveillance 7, no. 3: e27317.

Research letter
Published: 24 September 2020 in Journal of Travel Medicine
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We analyze the COVID-19 pandemic development in Latin America by network analysis to demonstrate the effectiveness of air travel restriction in reducing pandemic risk and provide risk analysis for air travel reopening in Latin America. We reinforce the importance of restricting air travel before and during local transmission of COVID-19.

ACS Style

Amanda M Y Chu; Jenny T Y Tsang; Jacky N L Chan; Agnes Tiwari; Mike K P So. Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness. Journal of Travel Medicine 2020, 27, 1 .

AMA Style

Amanda M Y Chu, Jenny T Y Tsang, Jacky N L Chan, Agnes Tiwari, Mike K P So. Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness. Journal of Travel Medicine. 2020; 27 (8):1.

Chicago/Turabian Style

Amanda M Y Chu; Jenny T Y Tsang; Jacky N L Chan; Agnes Tiwari; Mike K P So. 2020. "Analysis of travel restrictions for COVID-19 control in Latin America through network connectedness." Journal of Travel Medicine 27, no. 8: 1.

Other
Published: 18 September 2020
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The spread of coronavirus disease 2019 (COVID-19) has caused more than 24 million confirmed infected cases and more than 800,000 people died as of 28 August 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of ‘co-movement’ of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pandemic control. The preparedness risk score contributed by countries in Asia is between 25% to 50% most of the time after February and America contributes close to 50% recently. The high preparedness risk contribution implies the importance of travel restrictions between those countries. The severity risk score of America is greater than 50% after May and even exceeds 75% in July, signifying that the control of COVID-19 is still worrying in America. We can keep track of the pandemic situation in each country using an online dashboard to update the pandemic risk scores and contributions.

ACS Style

Mike K.P. So; Amanda M.Y. Chu; Agnes Tiwari; Jacky N.L. Chan. On Topological Properties of COVID-19: Predicting and Controling Pandemic Risk with Network Statistics. 2020, 1 .

AMA Style

Mike K.P. So, Amanda M.Y. Chu, Agnes Tiwari, Jacky N.L. Chan. On Topological Properties of COVID-19: Predicting and Controling Pandemic Risk with Network Statistics. . 2020; ():1.

Chicago/Turabian Style

Mike K.P. So; Amanda M.Y. Chu; Agnes Tiwari; Jacky N.L. Chan. 2020. "On Topological Properties of COVID-19: Predicting and Controling Pandemic Risk with Network Statistics." , no. : 1.

Journal article
Published: 28 May 2020 in Journal of Travel Medicine
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A novel use of network analysis in public healthDeveloping a quantitative assessment method for the COVID-19 pandemic riskExploring the time series of network density for early warning signals of pandemic riskTracking the evolution of pandemic risk through the degree of connectedness

ACS Style

Amanda M Y Chu; Agnes Tiwari; Mike K P So. Detecting early signals of COVID-19 global pandemic from network density. Journal of Travel Medicine 2020, 27, 1 .

AMA Style

Amanda M Y Chu, Agnes Tiwari, Mike K P So. Detecting early signals of COVID-19 global pandemic from network density. Journal of Travel Medicine. 2020; 27 (5):1.

Chicago/Turabian Style

Amanda M Y Chu; Agnes Tiwari; Mike K P So. 2020. "Detecting early signals of COVID-19 global pandemic from network density." Journal of Travel Medicine 27, no. 5: 1.

Journal article
Published: 14 April 2020 in Sustainability
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This article examines the occurrences of four types of unethical employee information security behavior—misbehavior in networks/applications, dangerous Web use, omissive security behavior, and poor access control—and their relationships with employees’ information security management efforts to maintain sustainable information systems in the workplace. In terms of theoretical contributions, this article identifies and develops reliable and valid instruments to measure different types of unethical employee information security behavior. In addition, it investigates factors affecting different types of such behavior and how such behavior can be used to predict employees’ willingness to report information security incidents. In terms of managerial contributions, the article suggests that information security awareness programs and perceived punishment have differential effects on the four types of unethical behavior and that certain types of unethical information security behavior exert negative effects on employees’ willingness to report information security incidents. The findings will help managers to derive better security rules and policies, which are important for business continuity.

ACS Style

Amanda M. Y. Chu; Mike K. P. So. Organizational Information Security Management for Sustainable Information Systems: An Unethical Employee Information Security Behavior Perspective. Sustainability 2020, 12, 3163 .

AMA Style

Amanda M. Y. Chu, Mike K. P. So. Organizational Information Security Management for Sustainable Information Systems: An Unethical Employee Information Security Behavior Perspective. Sustainability. 2020; 12 (8):3163.

Chicago/Turabian Style

Amanda M. Y. Chu; Mike K. P. So. 2020. "Organizational Information Security Management for Sustainable Information Systems: An Unethical Employee Information Security Behavior Perspective." Sustainability 12, no. 8: 3163.

Journal article
Published: 19 March 2020 in Emerging Markets Review
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We investigate growth determinants for Mongolia as a small emerging economy with respect to China as its large neighbor. Our causality analysis during 1992 to 2017 reveals significant linear as well as nonlinear relationships in growth explanation. China's GDP and coal prices, together with some of their linear and nonlinear lagged components, predict Mongolia's GDP, where a 1 % increase in China's GDP relates to a 1.5% increase in that of Mongolia. Current exchange rates and the nonlinear components of lagged consumer prices also explain growth. Our results underline the role of macroeconomic drivers of growth in emerging economies.

ACS Style

Amanda M.Y. Chu; Zhihui Lv; Niklas F. Wagner; Wing-Keung Wong. Linear and nonlinear growth determinants: The case of Mongolia and its connection to China. Emerging Markets Review 2020, 43, 100693 .

AMA Style

Amanda M.Y. Chu, Zhihui Lv, Niklas F. Wagner, Wing-Keung Wong. Linear and nonlinear growth determinants: The case of Mongolia and its connection to China. Emerging Markets Review. 2020; 43 ():100693.

Chicago/Turabian Style

Amanda M.Y. Chu; Zhihui Lv; Niklas F. Wagner; Wing-Keung Wong. 2020. "Linear and nonlinear growth determinants: The case of Mongolia and its connection to China." Emerging Markets Review 43, no. : 100693.

Journal article
Published: 15 November 2019 in International Journal of Environmental Research and Public Health
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Patient data or information collected from public health and health care surveys are of great research value. Usually, the data contain sensitive personal information. Doctors, nurses, or researchers in the public health and health care sector do not analyze the available datasets or survey data on their own, and may outsource the tasks to third parties. Even though all identifiers such as names and ID card numbers are removed, there may still be some occasions in which an individual can be re-identified via the demographic or particular information provided in the datasets. Such data privacy issues can become an obstacle in health-related research. Statistical disclosure control (SDC) is a useful technique used to resolve this problem by masking and designing released data based on the original data. Whilst ensuring the released data can satisfy the needs of researchers for data analysis, there is high protection of the original data from disclosure. In this research, we discuss the statistical properties of two SDC methods: the General Additive Data Perturbation (GADP) method and the Gaussian Copula General Additive Data Perturbation (CGADP) method. An empirical study is provided to demonstrate how we can apply these two SDC methods in public health research.

ACS Style

Amanda M. Y. Chu; Benson S. Y. Lam; Agnes Tiwari; Mike K. P. So. An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research. International Journal of Environmental Research and Public Health 2019, 16, 4519 .

AMA Style

Amanda M. Y. Chu, Benson S. Y. Lam, Agnes Tiwari, Mike K. P. So. An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research. International Journal of Environmental Research and Public Health. 2019; 16 (22):4519.

Chicago/Turabian Style

Amanda M. Y. Chu; Benson S. Y. Lam; Agnes Tiwari; Mike K. P. So. 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research." International Journal of Environmental Research and Public Health 16, no. 22: 4519.

Journal article
Published: 29 October 2019 in International Journal of Environmental Research and Public Health
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Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO2 emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO2 emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO2 emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO2 emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO2 emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

ACS Style

Zhihui Lv; Amanda M. Y. Chu; Michael McAleer; Wing-Keung Wong; Lv; Chu; Wong. Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality. International Journal of Environmental Research and Public Health 2019, 16, 4176 .

AMA Style

Zhihui Lv, Amanda M. Y. Chu, Michael McAleer, Wing-Keung Wong, Lv, Chu, Wong. Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality. International Journal of Environmental Research and Public Health. 2019; 16 (21):4176.

Chicago/Turabian Style

Zhihui Lv; Amanda M. Y. Chu; Michael McAleer; Wing-Keung Wong; Lv; Chu; Wong. 2019. "Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality." International Journal of Environmental Research and Public Health 16, no. 21: 4176.

Journal article
Published: 18 March 2019 in International Journal of Environmental Research and Public Health
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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.

ACS Style

Andy C. Y. Chong; Amanda M. Y. Chu; Mike K. P. So; Ray S. W. Chung. Asking Sensitive Questions Using the Randomized Response Approach in Public Health Research: An Empirical Study on the Factors of Illegal Waste Disposal. International Journal of Environmental Research and Public Health 2019, 16, 970 .

AMA Style

Andy C. Y. Chong, Amanda M. Y. Chu, Mike K. P. So, Ray S. W. Chung. Asking Sensitive Questions Using the Randomized Response Approach in Public Health Research: An Empirical Study on the Factors of Illegal Waste Disposal. International Journal of Environmental Research and Public Health. 2019; 16 (6):970.

Chicago/Turabian Style

Andy C. Y. Chong; Amanda M. Y. Chu; Mike K. P. So; Ray S. W. Chung. 2019. "Asking Sensitive Questions Using the Randomized Response Approach in Public Health Research: An Empirical Study on the Factors of Illegal Waste Disposal." International Journal of Environmental Research and Public Health 16, no. 6: 970.