scholarly journals Restructuring State Intervention Strategies towards Chickpea Production Development in India—Application of Structural Equation Modeling (SEM)

2021 ◽  
Vol 13 (18) ◽  
pp. 10283
Author(s):  
K. Nirmal Ravi Kumar ◽  
Srinivasa Konduru ◽  
P. Chandrasekhara ◽  
Suresh Chandra Babu
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Convergent Validity ◽  
Structural Equation ◽  
State Intervention ◽  
Endogenous Variable ◽  
Equation Modeling ◽  
Infrastructure Development ◽  
Exogenous Variables ◽  
Production Development

Structural equation modeling (SEM) was employed to analyze the influence of exogenous variables (research and extension (RE), marketing aspects (MA), and infrastructure development (ID)) on the endogenous variable chickpea production development (CPD) to restructure policy interventions in India. Results of the measurement model revealed that all the latent variables have construct validity (both convergent validity and discriminant validity) and composite reliability. Confirmatory factor analysis revealed that all indicators of both exogenous and endogenous variables are significant. Yield-increasing production technologies (PT), minimum support prices (MSP), and storage structures (SS) and the three exogenous variables (research and extension, marketing, and infrastructure development) are the strongest indicators. For the endogenous variable CPD, remunerative prices (RP) is the strongest indicator and also serves as a driving force for other indicators. The results of the structural model revealed that RE is the most effective construct followed by ID and MA, and they cumulatively explained 89 percent of the total variation in CPD. Among these three constructs, MSP is the key indicator of MA with the highest loading factor (0.799), and hence it should be given the highest priority for promoting CPD in India.

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.

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.

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.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals MODEL PERSAMAAN STRUKTURAL UNTUK MENGETAHUI PERSEPSI KONSUMEN TERHADAP BUAH IMPOR

2020 ◽  
Vol 9 (3) ◽  
pp. 149
Author(s):  
I KADEK TEGUH PRADANA ◽  
NI KETUT TARI TASTRAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Sem Analysis ◽  
Consumer Perception ◽  
Equation Modeling ◽  
Consumer Knowledge

This research is aimed to determine the factors that significantly influence consumer perception in buying imported fruits using structural equation modeling (SEM) analysis. The study use 164 data obtained from questionnaire, which respondents were aged 18 years old or above, from Gianyar Regency, had bought and had felt imported fruits. The study use 4 latent variables (perception, product, personal, culture) with 19 measured variables. The results showed that consumer knowledge about imported fruits (product) and culture about the use of imported fruits in traditional ceremonies (culture) were significantly influence consumer perception about consumption of imported fruit (perception).

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.

Psychometric Characteristics of Strengths Knowledge Scale and Strengths Use Scale Among Adolescents

2017 ◽  
Vol 36 (7) ◽  
pp. 756-760 ◽  
Author(s):  
Wenjie Duan ◽  
Jinxia Li ◽  
Wenlong Mu
Keyword(s):  
Structural Equation Modeling ◽  
Convergent Validity ◽  
Structural Equation ◽  
Internal Consistency Reliability ◽  
Equation Modeling ◽  
Psychometric Characteristics ◽  
Strengths Use ◽  
Good Internal Consistency ◽  
Current Sample ◽  
Knowledge Scale

This study examined the psychometric properties of Strengths Knowledge Scale (SKS) and Strengths Use Scale (SUS) in a sample of 442 adolescents by the exploratory structural equation modeling. Correlations between strength use/strength knowledge and flourish, positive affect, negative affect, depression, anxiety, stress, and GPA were examined to illustrate criterion-related validities. Results showed good internal consistency reliability, expected structure factor, and convergent validity of the SKS and the SUS in the current sample.

Using WarpPLS in E-collaboration Studies

2010 ◽  
Vol 6 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Research Methods ◽  
Latent Variables ◽  
Structural Equation ◽  
Software Tools ◽  
Equation Modeling ◽  
Modeling Analysis ◽  
Modeling Software ◽  
S Curve ◽  
Complex Phenomena

Most relationships between variables describing natural and behavioral phenomena are nonlinear, with U-curve and S-curve relationships being particularly common. Yet, structural equation modeling software tools do not estimate coefficients of association taking nonlinear relationships between latent variables into consideration. This can lead to misleading results, particularly in multivariate and complex phenomena like those related to e-collaboration. One notable exception is WarpPLS (available from: warppls.com), a new structural equation modeling software currently available in its first release. The discussion presented in this paper contributes to the literature on e-collaboration research methods by providing a description of the main features of WarpPLS in the context of an e-collaboration study. The focus of this discussion is on the software’s features and their use and not on e-collaboration study itself. Particular emphasis is placed on the five steps through which a structural equation modeling analysis is conducted through WarpPLS.

Effect of Spiritually Dimensions on Martial Satisfaction

2017 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Kamal Khurana
Keyword(s):  
Structural Equation Modeling ◽  
Marital Satisfaction ◽  
Marital Quality ◽  
Latent Variables ◽  
Structural Equation ◽  
Positive Impact ◽  
Well Being ◽  
Equation Modeling ◽  
Relationship With God ◽  
Cross Sectional

Spiritually is an essential part of our physical and mental well-being. Spirituality dimensions also effects marital quality. This paper presents evidences that there exists a positive effect of spirituality dimensions on marital satisfaction. The results are proved through structural equation modeling among the latent variables- direct relationship with God, relationship with God through others and martial satisfaction- in a cross-sectional study of 382 married adults aged 18 to 48 years comprising of 225 males and 157 female respondents. Data was collected in Delhi NCR region through self-administrated questionnaire formulated through marital quality index (Norton, 1983)15 and daily spiritual scale (Underwood, 2002)19. Results of structural equation modeling suggest that there is a positive impact of spirituality dimensions on marital satisfaction.

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