scholarly journals COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

2021 ◽  
pp. 395-405
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Support Vector ◽  
Economic Factors ◽  
Negative Effects ◽  
The Impact

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

scholarly journals Evaluation of Preventive, Supportive and Awareness Building Measures among International Students in China in Response to COVID-19: A Structural Equation Modeling Approach

2020 ◽  
Author(s):  
Tanwne Sarker ◽  
Apurbo Sarkar ◽  
Md. Ghulam Rabbany ◽  
Milon Barmon ◽  
Rana Roy ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
International Students ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Convergent Validity ◽  
Structural Equation ◽  
Equation Modeling ◽  
Local Authorities ◽  
The Impact

Abstract Background The Coronavirus Disease 2019 (COVID-19) with its high mortality, stigma and panic has compelled many cities and countries to complete lockdown. The worldwide student group is one of the most affected and vulnerable communities in this situation. Our current study aimed to assess the impact of the behavior change communication among international students in China in current COVID-19 crisis.Methods In this paper, we have utilized partial least squares-structural equation modeling (PLS-SEM) to understand the health behaviour changes of international students in China in response to novel Coronavirus outbreak. We mainly analyzed the relationship among the three selected latent variables (preventive, supportive and awareness building) based on a survey among the international students (n=467) in China in February 2020. We obtained their valuable responses regarding level of awareness, satisfaction and trust in authorities (i.e., government, local authorities and institutions) during this emergency period. Results We utilized 22 indicators in the conceptual framework model with the help of Smart PLS 2.0 version software. The lowest average variance extracted (AVE) for all the constructs of our paper exceeded the minimum accepted value of 0.5, representing the adequate convergent validity. Prediction of students’ satisfaction, the key outcome degree of the model, was nearly moderate, with an R2 = 0.507 whereas the prediction of trust in authorities was above substantial, with an R2 = 0.797. Therefore, our PLS-SEM model showed a strong and significant positive association between preventive and supportive measures taken for the study population and gaining trust, awareness and satisfaction in authorities. Conclusions Integrated partial least squares-structural equation modeling (PLS-SEM) can be a great way to measure the satisfaction and trust level of various population groups over government, local authorities, and institutions in public health emergency like COVID-19 crisis. We believe that our findings are important for travel and global health perspectives. Other countries can learn and take necessary initiatives for their international students and general public to halt this deadly epidemic with gaining their satisfaction and trust as well.

scholarly journals Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach

2016 ◽  
Vol 7 (04) ◽  
pp. 559-565 ◽  
Author(s):  
Linda Jayne Nichols ◽  
Seana Gall ◽  
Christine Stirling
Keyword(s):  
Risk Factors ◽  
Structural Equation Modeling ◽  
Conceptual Framework ◽  
Structural Equation ◽  
System Level ◽  
Equation Modeling ◽  
Modifiable Risk Factors ◽  
Cumulative Impact ◽  
The Impact ◽  
The Relationship

ABSTRACTAn aneurysmal subarachnoid hemorrhage (aSAH) carries a high disability burden. The true impact of rurality as a predictor of outcome severity is unknown. Our aim is to clarify the relationship between the proposed explanations of regional and rural health disparities linked to severity of outcome following an aSAH. An initial literature search identified limited data directly linking geographical location, rurality, rural vulnerability, and aSAH. A further search noting parallels with ischemic stroke and acute myocardial infarct literature presented a number of diverse and interrelated predictors. This a priori knowledge informed the development of a conceptual framework that proposes the relationship between rurality and severity of outcome following an aSAH utilizing structural equation modeling. The presented conceptual framework explores a number of system, environmental, and modifiable risk factors. Socioeconomic characteristics, modifiable risk factors, and timely treatment that were identified as predictors of severity of outcome following an aSAH and within each of these defined predictors a number of contributing specific individual predictors are proposed. There are considerable gaps in the current knowledge pertaining to the impact of rurality on the severity of outcome following an aSAH. Absent from the literature is any investigation of the cumulative impact and multiplicity of risk factors associated with rurality. The proposed conceptual framework hypothesizes a number of relationships between both individual level and system level predictors, acknowledging that intervening predictors may mediate the effect of one variable on another.

scholarly journals PENERAPAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS PADA KEPUASAN KONSUMEN (Studi Kasus: Pasien Klinik Q)

2020 ◽  
Vol 9 (4) ◽  
pp. 454-463
Author(s):  
Farisiyah Fitriani ◽  
Agus Rusgiyono ◽  
Tatik Widiharih
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Random Sampling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Least Square ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
The Impact

Customer satisfaction is used by a company to evaluate products or services whether it is sufficient with customer’s expectations. Satisfaction is influenced by factors that cannot be measured directly are called latent variables and can be measured through indicators used to measure satisfaction with Structural Equation Modeling (SEM). Generalized Structured Component Analysis (GSCA) method is part of a SEM based on a variant that does not require the assumption of a multivariate normal distribution and has a measure overall goodness of fit. The parameters used are factor loading, coefficients parameter, and weight of indicators and estimated with alternating least square. The type of data used primary data from the results of the questionnaire with stratified proportional random sampling and number of samples 286. This research using indicators as measurable variables as many 32 indicators and 8 latent variable. Considering to the evaluation of the structural model, it is found there are 5 variables that influence satisfaction, they are prices, quality yield, cleanliness, doctor's services, and employee services with a large influence of 77.18% and the impact of satisfaction on loyalty is 48.63 %. For the overall goodness of fit measure, it is known that the FIT value is 63.75% and the adjusted FIT (AFIT) value is 63.47%. The goodness of fit (GFI) produced the value in the amount of 96.43%, indicating that the general model has the good level of compatibility.Keywords: Generalized Structured Component Analysis, Structural Equation Modeling, Overall goodness of fit, Alternating Least Square, Stratified Proportional Random Sampling

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

How Community and Individual Risk Factors Mutually Impact Youth’s Perceived Safety: A Syndemic Analysis Using Structural Equation Modeling

2021 ◽  
pp. 088626052110283
Author(s):  
Yingwei Yang ◽  
Karen D. Liller ◽  
Martha Coulter ◽  
Abraham Salinas-Miranda ◽  
Dinorah Martinez Tyson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Community Safety ◽  
Equation Modeling ◽  
Individual Risk ◽  
Latent Factor ◽  
Community Factors ◽  
Individual Risk Factors ◽  
Syndemic Theory

The purpose of this study was to evaluate the mutual impact of community and individual factors on youth’s perceptions of community safety, using structural equation modeling (SEM) conceptualized by syndemic theory. This study used survey data collected from a county wide sample of middle and high school students (N=25,147) in West Central Florida in 2015. The outcome variable was youth’s perceptions of community safety. Predictors were latent individual and community factors constructed from 14 observed variables including gun accessibility, substance use, depressive symptoms, and multiple neighborhood disadvantage questions. Three structural equation models were conceptualized based on syndemic theory and analyzed in Mplus 8 using weighted least squares (WLS) estimation. Each model’s goodness of fit was assessed. Approximately seven percent of youth reported feeling unsafe in their community. After model modifications, the final model showed a good fit of the data and adhered to the theoretical assumption. In the final SEM model, an individual latent factor was implied by individual predictors measuring gun accessibility without adult’s permission (β=0.70), sadness and hopelessness (β=0.52), alcohol use (β=0.79), marijuana use (β=0.94), and illegal drug use (β=0.77). Meanwhile, a community latent factor was indicated by multiple community problems including public drinking (β=0.88), drug addiction (β=0.96), drug selling (β=0.97), lack of money (β=0.83), gang activities (β=0.90), litter and trash (β=0.79), graffiti (β=0.91), deserted houses (β=0.86), and shootings (β=0.93). A second-order syndemic factor that represented the individual and community factors showed a very strong negative association with youth’s safe perception (β=-0.98). This study indicates that individual risk factors and disadvantaged community conditions interacted with each other and mutually affected youth’s perceptions of community safety. To reduce these co-occurring effects and improve safe perceptions among youth, researchers and practitioners should develop and implement comprehensive strategies targeting both individual and community factors.

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

Mobile Business Effects on Employees' Social Life

2021 ◽  
Vol 13 (2) ◽  
pp. 1
Author(s):  
Husam Alfahl
Keyword(s):  
Social Life ◽  
Structural Equation ◽  
Equation Modeling ◽  
Work Family Conflict ◽  
Negative Effects ◽  
Mobile Business ◽  
Business Services ◽  
Work Related ◽  
The Social ◽  
The Impact

The use of mobile devices and smartphones is increasingly becoming a critical part of many people’s lifestyle. Such usage can vary from playing games to accomplishing work-related tasks. Being able to use organizations’ persuasive technologies via mobile business services or to achieve work-related tasks ubiquitously at any time means that such devices provide a valuable service, especially for employees who are working online. This paper explores the impact of mBusiness on the social life of employees. In the research, structural equation modeling was applied to validate the research model. Employees in Saudi organizations were surveyed to test the research hypotheses. The research results confirmed that there are some negative effects of using mBusiness technologies on the social life of employees. Based on the analysis, the findings revealed that addiction to mBusiness technologies significantly increases the perceived work overload, which also significantly increases work-family conflict. The paper concludes with some implications of this research.

Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazzini Muda ◽  
Muhammad Iskandar Hamzah
Keyword(s):  
Social Media ◽  
Structural Equation Modeling ◽  
Purchase Intention ◽  
Structural Equation ◽  
Word Of Mouth ◽  
Source Credibility ◽  
Equation Modeling ◽  
Content Type ◽  
The Social ◽  
The Impact

PurposeIn spite of the increasing organic and interactive marketing activities over social media, a general understanding of the source credibility of voluntary user-generated content (UGC) is still limited. In line with the social identity theory, this paper examines the effects of consumers' perceived source credibility of UGC in YouTube videos on their attitudes and behavioral intentions. Additionally, source homophily theory is included to predict the antecedent of source credibility.Design/methodology/approachThree hundred and seventy two Generation Y respondents were interviewed using snowball sampling. Data were analyzed with component-based structural equation modeling technique of partial least squares-structural equation modeling (PLS-SEM).FindingsFindings confirmed that perceived source credibility indirectly affects purchase intention (PI) and electronic word-of-mouth via attitude toward UGC. Besides, perceived source credibility mediates the effect of perceived source homophily on attitude toward UGC.Practical implicationsSince today's consumers have begun to trust and rely more on UGC than company-generated content on social media when making purchase decisions, companies may reconsider democratizing certain aspects of their branding strategies. Firms may fine-tune their marketing communication budgets – not only just by sponsoring public figures and celebrities but also by nurturing coproductive engagements with independent content creators who are ordinary consumers. Endowed with their imposing credibility, these micro-influencers and prosumers have high potentials to be uplifted to brand ambassadors.Originality/valueWhile consumers' purchase outcome can be measured easily using metrics and analytics, the roles of source homophily in stages leading up to the purchase is still elusive. Drawing on the rich theoretical basis of source homophily may help researchers to understand not only how credibility and attitude are related to PI but also how this nexus generates positive word of mouth among UGC followers within the social media circles.

Parenting quality, adversity, and conduct problems in adolescence: Testing process-oriented models of resilience

1993 ◽  
Vol 5 (4) ◽  
pp. 663-682 ◽  
Author(s):  
Scott D. Gest ◽  
Jennifer Neemann ◽  
Jon J. Hubbard ◽  
Ann S. Masten ◽  
Auke Tellegen
Keyword(s):  
Structural Equation Modeling ◽  
Conduct Problems ◽  
Structural Equation ◽  
Extended Family ◽  
Equation Modeling ◽  
Childhood And Adolescence ◽  
Moderating Effects ◽  
Negative Effects ◽  
Effective Parenting ◽  
Parenting Quality

AbstractStructural equation modeling was used (a) to determine the extent to which parent-related and non-parent-related adversity were associated with increases in conduct problems between childhood and adolescence and (b) to evaluate the possible preventive, compensatory, and moderating effects of parenting quality in this regard. Subjects were 180 boys and girls from the Project Competence longitudinal study of adversity, competence, and resilience (Garmezy & Tellegen, 1984). Conduct problems, parenting quality, and socioeconomic status were assessed when subjects were in the third through sixth grades, and adversity and conduct problems were assessed again 7 years later. Results were consistent with the view that parentrelated adversity experienced between the two assessment times was associated with a small increase in conduct problems. Adversity involving siblings, extended family, and friends was not associated with changes in conduct. Effective parenting was associated with less parent-related adversity during adolescence. Effective parenting, however, did not directly compensate for the negative effects of adversity; nor did it moderate the effects of adversity. Structural equation modeling was helpful in testing for several of these effects simultaneously. Short-term longitudinal studies with baseline measures, more frequent assessments, and adequate sample size are necessary to clarify the processes suggested by these results.

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