scholarly journals Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessia Spada ◽  
Francesco Antonio Tucci ◽  
Aldo Ummarino ◽  
Paolo Pio Ciavarella ◽  
Nicholas Calà ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Population Density ◽  
Structural Equation ◽  
Climatic Factors ◽  
Demographic Data ◽  
Factor Loading ◽  
Statistical Technique ◽  
Global Scale ◽  
Equation Modeling ◽  
Relevant Role

AbstractClimate seems to influence the spread of SARS-CoV-2, but the findings of the studies performed so far are conflicting. To overcome these issues, we performed a global scale study considering 134,871 virologic-climatic-demographic data (209 countries, first 16 weeks of the pandemic). To analyze the relation among COVID-19, population density, and climate, a theoretical path diagram was hypothesized and tested using structural equation modeling (SEM), a powerful statistical technique for the evaluation of causal assumptions. The results of the analysis showed that both climate and population density significantly influence the spread of COVID-19 (p < 0.001 and p < 0.01, respectively). Overall, climate outweighs population density (path coefficients: climate vs. incidence = 0.18, climate vs. prevalence = 0.11, population density vs. incidence = 0.04, population density vs. prevalence = 0.05). Among the climatic factors, irradiation plays the most relevant role, with a factor-loading of − 0.77, followed by temperature (− 0.56), humidity (0.52), precipitation (0.44), and pressure (0.073); for all p < 0.001. In conclusion, this study demonstrates that climatic factors significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect the transmission, and their influence may overcome the protective effect of climate, where favourable.

scholarly journals Loneliness, Emotion Dysregulation, and Internalizing Symptoms During Coronavirus Disease 2019: A Structural Equation Modeling Approach

2021 ◽  
Vol 11 ◽  
Author(s):  
Patrizia Velotti ◽  
Guyonne Rogier ◽  
Sara Beomonte Zobel ◽  
Rosetta Castellano ◽  
Renata Tambelli
Keyword(s):  
Structural Equation Modeling ◽  
Los Angeles ◽  
Internalizing Symptoms ◽  
Emotion Dysregulation ◽  
Structural Equation ◽  
Demographic Data ◽  
Clinical Intervention ◽  
Equation Modeling ◽  
Emotional States ◽  
Cross Sectional

Background: Our study aimed to test the hypotheses that an increased level of loneliness experienced during coronavirus disease 2019 (COVID-19) confinement was predictive of internalizing symptoms and that this pathway was mediated by emotion dysregulation levels.Methods: To reach this aim, we performed an online longitudinal survey recruiting 1,330 participants at Time 1 (at the beginning of the lockdown) and 308 participants at Time 2 (few days before the end of the lockdown). All filled out a set of questionnaires: demographic data, University of California, Los Angeles Loneliness scale, Difficulties in Emotion Regulation Scale−18 items, and Depression Anxiety and Stress Scale−21 items. Hypotheses were tested using structural equation modeling in two steps and controlling for age. First, hypotheses were tested on cross-sectional data. Then, a cross-lagged panel analysis was performed on longitudinal data.Results: Models obtained a good fit and evidenced the predictive role of loneliness levels on the three outcomes (i.e., depression, anxiety, and stress). Moreover, we found that emotion dysregulation levels partially mediated the longitudinal relationship between loneliness and both depression and stress but not between loneliness and anxiety levels.Conclusions: This study points out that a central goal of clinical intervention could be the ability to regulate negative emotional states.

scholarly journals E-Lifestyle And Internet Advertising Avoidance

2018 ◽  
Vol 22 (3) ◽  
pp. 435
Author(s):  
Widarto Rachbini, Iha Haryani Hatta
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Sampling Technique ◽  
Factor Loading ◽  
Internet Advertising ◽  
Equation Modeling ◽  
The Internet ◽  
Lifestyle Behavior ◽  
Internet Users ◽  
Advertising Avoidance

E-lifestyle behavior towards avoiding internet advertising needs to be known to design an advertisement on the internet, so that the tendency of avoiding internet advertising can be reduced.Therefore, research on e-lifestyle and internet advertising avoidance is needed which aims to find out the influence of each e-lifestyle on Internet advertising avoidance and the behavior of each e-lifestyle towards Internet advertising avoidance activities. The population of this study is Internet users. A sample of 200 Jakarta residents were selected by purposive sampling technique. Data were analyzed using Structural Equation Modeling (SEM). The results obtained after the data were analyzed show that need-driven, interest-driven, entertainment-driven, sociability-driven, and novelty-driven influence the avoidance of Internet advertising. But Importance-driven and concern-driven e-lifestyle have no influence. In addition, entertainment-driven e-lifestyle has the largest factor loading value on average. This indicates that the majority of respondents are entertainment-driven in using the Internet everyday.

scholarly journals A Modified Algorithm for the Computation of the Covariance Matrix Implied by a Structural Recursive Model with Latent Variables Using the Finite Iterative Method

2020 ◽  
Vol 8 (2) ◽  
pp. 359-373 ◽  
Author(s):  
M'barek Iaousse ◽  
Amal Hmimou ◽  
Zouhair El Hadri ◽  
Yousfi El Kettani
Keyword(s):  
Structural Equation Modeling ◽  
Iterative Method ◽  
Covariance Matrix ◽  
Latent Variables ◽  
Structural Equation ◽  
Causal Model ◽  
Statistical Technique ◽  
Equation Modeling ◽  
Modified Algorithm ◽  
Finite Iterative Method

Structural Equation Modeling (SEM) is a statistical technique that assesses a hypothesized causal model byshowing whether or not, it fits the available data. One of the major steps in SEM is the computation of the covariance matrix implied by the specified model. This matrix is crucial in estimating the parameters, testing the validity of the model and, make useful interpretations. In the present paper, two methods used for this purpose are presented: the J¨oreskog’s formula and the finite iterative method. These methods are characterized by the manner of the computation and based on some apriori assumptions. To make the computation more simplistic and the assumptions less restrictive, a new algorithm for the computation of the implied covariance matrix is introduced. It consists of a modification of the finite iterative method. An illustrative example of the proposed method is presented. Furthermore, theoretical and numerical comparisons between the exposed methods with the proposed algorithm are discussed and illustrated

scholarly journals Analisis Gamification Shopee.com Sebagai Media Pemasaran Terhadap EWOM, Positive Emotion, & Repurchase Intention (Pada Aplikasi Shopee.com)

2021 ◽  
Vol 6 (1) ◽  
pp. 15-32
Author(s):  
Hendy Chrisnathaniel ◽  
Sri Hartini ◽  
Sari Puji Rahayu
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Positive Emotion ◽  
Word Of Mouth ◽  
Statistical Technique ◽  
Equation Modeling ◽  
Electronic Word Of Mouth ◽  
Repurchase Intention ◽  
Repurchase Intentions ◽  
Positive Effect

This study aims to explore the influence of Shopee.com gamification as a marketing media for EWOM, positive emotion, and repurchase intention. Researchers emphasize on Shopee as one of the e-commerce that is first ranked with the number of monthly web visitors, and first ranked on the Appstore and Playstore in Indonesia. This study uses a questionnaire distributed online to 170 respondents, with criteria as the "Y" generation, and has used Shopee and has the experience to use the loyalty games program of the Shopee.com application. The results of this study were further processed and analyzed with the Structural Equation Modeling (SEM) statistical technique with the smartPLS 3.0 program. The results showed that Shopee.com gamification had a positive effect on positive emotion, repurchase intentions, and electronic word of mouth (EWOM). Then positive emotion has a positive effect on repurchase intentions and electronic word of mouth (EWOM).

scholarly journals Prior Sensitivity in Bayesian Structural Equation Modeling for Sparse Factor Loading Structures

2020 ◽  
Vol 80 (6) ◽  
pp. 1025-1058 ◽  
Author(s):  
Xinya Liang
Keyword(s):  
Structural Equation Modeling ◽  
Variable Selection ◽  
Structural Equation ◽  
Factor Loading ◽  
Model Fit ◽  
Equation Modeling ◽  
Model Parameters ◽  
Shrinkage Priors ◽  
Bayesian Structural Equation Modeling ◽  
The Impact

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both variable selection and model estimation. The prior sensitivity in BSEM-N was explored in factor analysis models with sparse loading structures through a simulation study (Study 1) and an empirical example (Study 2). Study 1 examined the prior sensitivity in BSEM-N based on the model fit, population model recovery, true and false positive rates, and parameter estimation. Seven shrinkage priors on cross-loadings and five noninformative/vague priors on other model parameters were examined. Study 2 provided a real data example to illustrate the impact of various priors on model fit and parameter selection and estimation. Results indicated that when the 95% credible intervals of shrinkage priors barely covered the population cross-loading values, it resulted in the best balance between true and false positives. If the goal is to perform variable selection, a sparse cross-loading structure is required, preferably with a minimal number of nontrivial cross-loadings and relatively high primary loading values. To improve parameter estimates, a relatively large prior variance is preferred. When cross-loadings are relatively large, BSEM-N with zero-mean priors is not recommended for the estimation of cross-loadings and factor correlations.

Variance-Based Structural Equation Modeling

2012 ◽  
pp. 193-221 ◽  
Author(s):  
José L. Roldán ◽  
Manuel J. Sánchez-Franco
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Structural Equation ◽  
Structural Model ◽  
Measurement Model ◽  
Statistical Technique ◽  
Equation Modeling ◽  
Systems Research ◽  
Information Systems Research ◽  
Systems Models

Partial Least Squares (PLS) is an efficient statistical technique that is highly suited for Information Systems research. In this chapter, the authors propose both the theory underlying PLS and a discussion of the key differences between covariance-based SEM and variance-based SEM, i.e., PLS. In particular, authors: (a) provide an analysis of the origin, development, and features of PLS, and (b) discuss analysis problems as diverse as the nature of epistemic relationships and sample size requirements. In this regard, the authors present basic guidelines for the applying of PLS as well as an explanation of the different steps implied for the assessment of the measurement model and the structural model. Finally, the authors present two examples of Information Systems models in which they have put previous recommendations into effect.

scholarly journals Analysis of Product Quality and Service Quality on Customer Satisfaction at the Surya Mart Mart Business Center using the Structural Equation Modeling Method

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Yannu Prasetyo ◽  
Atikha Sidhi Cahyana
Keyword(s):  
Structural Equation Modeling ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Product Quality ◽  
Measurement Errors ◽  
Structural Equation ◽  
Business Performance ◽  
Statistical Technique ◽  
Equation Modeling ◽  
A Value

Service quality is a superior value for customers to improve business performance or company marketing represents a level of excellence to meet consumer expectations. Therefore, it is not necessary to know what factors affect product quality and service quality at the Surya Mart Business Center Surya Mart Business Center. Surya Mart Business Center Surya Mart's Business Center is located on campus 2 of the Muhammadiyah University of Sidoarjo (UMSIDA), the market is for students, lecturers, UMSIDA employees and the surrounding community. To find out the factors that affect product quality and service quality, research uses Structural Equation Modeling, which is ia statistical technique that is able to analyze the pattern of relationships between data constructs and indicators, one data construct and another, and direct measurement errors. The results showed that the highest loading factor value was 1.329 in the discount indicator, for the term indicator, namely the additional factor with a value of 0.664. Test structural modeling on the relationship between constructs that have a casual or causal relationship. The results of this study, Variable Product Quality and Service Quality, the second variable has a significant influence on the Customer Satisfaction variable.

scholarly journals Structural Equation Modeling (SEM) of Performance Evaluation Indices in General Directorate of Youth and Sport of Guilan Province with Partial Least Squares (PLS)

2016 ◽  
Vol 20 (5) ◽  
pp. 49-56
Author(s):  
Goharrostami Hamidreza ◽  
Mollaei Nejad Mustafa ◽  
Ramezani Nejad Rahim ◽  
Abdollahi Azam
Keyword(s):  
Performance Evaluation ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fitting ◽  
Factor Loading ◽  
Equation Modeling ◽  
Validity And Reliability ◽  
Learning And Development ◽  
Financial Factors ◽  
Guilan Province

Purpose : to evaluate the performance evaluation the indexes of general directorate of youth and sport of Guilan province by using the BSC approach. Material : This was a descriptive and field -based survey. The population included managers and experts from the general directorate of youth and sport of Guilan province. The purposive sampling was used. A questionnaire was used to collect data. Content validity and reliability were approved by experts Cronbach's alpha test (0.89) respectively. For data analyzing and model fitting the structural equation modeling (SEM) with PLS software was used. Results : performance evaluation model of general directorate of youth and sport of Guilan province has four factors, 12 dimensions and 55 indicators. So that learning and development factor has 4 dimensions and 13 indicators, internal processes have 4 dimensions and 23 indicators, financial factor has 2 dimensions and 7 indicators and customer and sport results have 2 dimensions 12 indicators. Internal processes, customer and sporting results, learning and development and financial factors had coefficients of factor loading of 0.91, 0.83, 0.81 and 0.80 respectively. Conclusion : We concluded that, in evaluating the performance of the organization, special attention should be paid on four studied terms and their confirmed dimensions and indicators. Based on the factor loading priority of activities and evaluation should be allocated to internal processes, customer and sporting results, learning and development and financial factors. So this index can be used to design a model to evaluate the performance of the general directorate of youth and sport of Guilan province.

scholarly journals Structural Equation Modeling in Motivation Analysis for Millennial Participation Related to General Elections in Indonesia

2021 ◽  
pp. 29-37
Author(s):  
Zalfaa Nur Amalia ◽  
Rosyida Widadina Ulya ◽  
Disty Ridha Hastuti ◽  
M. Fariz Fadillah Mardianto
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Millennial Generation ◽  
Structural Equation ◽  
Voting Rights ◽  
Statistical Technique ◽  
General Election ◽  
Equation Modeling ◽  
Indicator Variables ◽  
General Elections

Structural Equation Modeling (SEM) is a statistical technique used to build and test the statistical models are usually in the form of causal models. SEM is a combination from factor analysis, path analysis, and regression. This method is a statistical approach that serves to test hypotheses about the relationship between observed variables and latent variables. In this paper, SEM is applied to determine the motivation of the millennial generation for the general election 2019 in Indonesia. Data was obtained by distributing questionnaires online according to procedures which were then analyzed using SEM. Millennial’s motivation is seen from the knowledge of the millennial generation on voting rights commitments in the 2019 general election in Indonesia. Based on the result, millennial generation is committed to using voting rights in the 2019 general election. All indicator variables from this study are significant to the millennial generation’s commitment to use their voting rights

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