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 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

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.

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.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document