Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model With Fixed Covariates

2005 ◽  
Vol 30 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Sik-Yum Lee ◽  
Xin-Yuan Song
Keyword(s):  
Maximum Likelihood ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Model Comparison ◽  
Real Data ◽  
Equation Model ◽  
Data Set ◽  
Nonlinear Structural Equation ◽  
Monte Carlo Em

In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects of fixed covariate in its various components. Methods for computing the ML estimates, and the Bayesian information criterion (BIC) for model comparison are established on the basis of powerful tools in statistical computing such as the Monte Carlo EM algorithm, Gibbs sampler, Metropolis–Hastings algorithm, conditional maximization, bridge sampling, and path sampling. The newly developed procedures are illustrated by results obtained from a simulation study and analysis of a real data set in education.

scholarly journals The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits

2018 ◽  
Vol 8 (7) ◽  
pp. 1153 ◽  
Author(s):  
José Díaz-Reza ◽  
Jorge García-Alcaraz ◽  
Liliana Avelar-Sosa ◽  
José Mendoza-Fong ◽  
Juan Sáenz Diez-Muro ◽  
...  
Keyword(s):  
Least Squares ◽  
Structural Equation Model ◽  
Preventive Maintenance ◽  
Latent Variables ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Implementation Strategies ◽  
Equation Model ◽  
Total Productive Maintenance

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

Big Data Analytics in the Agribusiness Supply Chain Management

2021 ◽  
Vol 9 (3) ◽  
pp. 32-42
Author(s):  
Marisol Valencia-Cárdenas ◽  
Jorge Anibal Restrepo-Morales ◽  
Francisco Javier Día-Serna
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation Model ◽  
Inventory Management ◽  
Latent Variables ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Equation Model ◽  
Chain Management

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

Constructing Structural Equation Model Rule-Based Fuzzy System with Genetic Algorithm

2016 ◽  
Vol 7 (2) ◽  
pp. 69-88 ◽  
Author(s):  
EnDer Su ◽  
Thomas W. Knowles ◽  
Yu-Gin Fen
Keyword(s):  
Genetic Algorithm ◽  
Risk Factor ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Structural Equation ◽  
The United States ◽  
Equation Model ◽  
Lower State ◽  
Inference System ◽  
United States Dollar

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.

scholarly journals Estimating Three-way Latent Interaction Effects in Structural Equation Modeling

2016 ◽  
Vol 5 (6) ◽  
pp. 73
Author(s):  
Birhanu Worku Urge ◽  
Kepher Makambi ◽  
Anthony Wanjoya
Keyword(s):  
Sample Size ◽  
Structural Equation Model ◽  
Interaction Effect ◽  
Latent Variable ◽  
Structural Equation ◽  
Error Control ◽  
Equation Model ◽  
Equation Modeling ◽  
Type I ◽  
Nonlinear Structural Equation

A Monte Carlo simulation was performed for estimating and testing hypotheses of three-way interaction effect in latent variable regression models. A considerable amount of research has been done on estimation of simple interaction and quadratic effect in nonlinear structural equation. The present study extended to three-way continuous latent interaction in structural equation model. The latent moderated structural equation (LMS) approach was used to estimate the parameters of the three-way interaction in structural equation model and investigate the properties of the method under different conditions though simulations. The approach showed least bias, standard error,and root mean square error as indicator reliability and sample size increased. The power to detect interaction effect and type I error control were also manipulated showing that power increased as interaction effect size, sample size and latent covariance increased.

scholarly journals Assessment of Body Size by Structural Equation Model Using Anthropometric Traits of Fishermen Community: A Methodological Approach

2020 ◽  
Vol 2 (3) ◽  
pp. 10-22
Author(s):  
Baidyanath Pal
Keyword(s):  
Body Size ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Methodological Approach ◽  
Equation Model ◽  
Size Category ◽  
Model Estimate ◽  
Composite Body ◽  
Body Sizes

Objectives Aim of the study was to develop a ‘composite body size score’ (CBSS) using anthropometric traits to estimate body size and to assess the nutritional status of each study individual on the basis of CBSS. Materials and Methods Data on seventeen anthropometric traits were collected from 710 individuals (Male, Female) from fishermen community inhabiting coastal villages of West Bengal, India. For estimating body sizes, Structural Equation Model (SEM) was constructed with Path Analysis (PA). Later, second order Confirmatory Factor Analysis (CFA) was applied on SEM to determine CBSS. It was hypothesized in the models that CBSS is composed with three sets of latent variables viz., linear, circular and skinfold, constructed from anthropometric traits. Applying new derived optimal cut off points of CBSS was used to determine lean, normal and robust body sizes. Individuals with negative values of CBSS were categorised as lean body size,. Positive values of CBSS were categorised into two categories- normal and robust body size. Results On the basis of CBSS, result showed that 50.6%, 48.8% and 0.6% of the individuals were categorised under lean, normal and robust body size respectively. Females showed relatively higher percent of lean body size i.e. under nutrition (73.8%) compared to males (26.2%). Conclusion The hypothesized model estimate more accurate composite body size score, based on anthropometric traits. All the traits are highly significant on the model. The lean body size category can be use in predicting ‘Undernutrition’.

scholarly journals A Structural Equation Modelling Approach to Examine the Relationship between Socioeconomic Status, Diet Quality and Dyslipidaemia in South African Children and Adolescents, 6–18 Years

2021 ◽  
Vol 18 (23) ◽  
pp. 12825
Author(s):  
Gugulethu Moyo ◽  
Esteban Montenegro-Montenegro ◽  
Zachary Stickley ◽  
Abdulkadir Egal ◽  
Wilna Oldewage-Theron
Keyword(s):  
Socioeconomic Status ◽  
Children And Adolescents ◽  
South African ◽  
Structural Equation Model ◽  
Diet Quality ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Model ◽  
Dietary Quality ◽  
The Relationship

This study utilised a structural equation model to examine the relationship between diet quality, socioeconomic status, and cardiovascular disease (CVD) risk in South African learners. Confirmatory factor analysis was used to test the indirect effects model for diet, socioeconomic status, diet quality and cardiovascular risk using pre-existing cross-sectional data. The structural equation model was fit using Lavaan version 0.6–5 in R version 3.6.1. Data were analysed from 178 children and adolescents, aged 6–18 years, from five rural schools in Cofimvaba, South Africa. Latent variables were created for dietary quality, dyslipidaemia and the socioeconomic status of participants. A negative association was observed between socioeconomic status and dyslipidaemia in school-aged children (p = 0.029).

scholarly journals Impact of Sociodemographic Characteristics, Lifestyle, and Obesity on Coexistence of Diabetes and Hypertension: A Structural Equation Model Analysis amongst Chinese Adults

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenwen Wu ◽  
Jie Diao ◽  
Jinru Yang ◽  
Donghan Sun ◽  
Ying Wang ◽  
...  
Keyword(s):  
Structural Equation Model ◽  
Latent Variables ◽  
Structural Equation ◽  
Cross Sectional Study ◽  
Equation Model ◽  
Sociodemographic Characteristics ◽  
Chinese Adults ◽  
Cross Sectional ◽  
Total Prevalence ◽  
Confirmatory Factor

Background. In general, given the insufficient sample size, considerable literature has been found on single studies of diabetes and hypertension and few studies have been found on the coexistence of diabetes and hypertension (CDH) and its influencing factors with a large range of samples. This study aimed to establish a structural equation model for exploring the direct and indirect relationships amongst sociodemographic characteristics, lifestyle, obesity, and CDH amongst Chinese adults. Methods. A cross-sectional study was conducted in a representative sample of 25356 adults between June 1, 2015, and September 30, 2018, in Hubei province, China. Confirmatory factor analysis was initially conducted to test the latent variables. A structural equation model was then performed to analyse the association between latent variables and CDH. Results. The total prevalence of CDH was 2.8%. The model paths indicated that sociodemographic characteristics, lifestyle, and obesity were directly associated with CDH, and the effects were 0.187, 0.739, and 0.353, respectively. Sociodemographic characteristics and lifestyle were also indirectly associated with CDH, and the effects were 0.128 and 0.045, respectively. Lifestyle had the strongest effect on CDH (β = 0.784, P < 0.001 ), followed by obesity (β = 0.353, P < 0.001 ) and sociodemographic characteristics (β = 0.315, P < 0.001 ). All paths of the model were significant ( P < 0.001 ). Conclusion. CDH was significantly associated with sociodemographic characteristics, lifestyle, and obesity amongst Chinese adults. The dominant predictor of CDH was lifestyle. Targeting these results might develop lifestyle and weight loss intervention to prevent CDH according to the characteristics of the population.

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