scholarly journals Products of Variables in Structural Equation Models: Multiplicative Reticular Action Models

2019 ◽  
Author(s):  
Steven M. Boker ◽  
Timo von Oertzen ◽  
Andreas Markus Brandmaier
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Model ◽  
Random Coefficients ◽  
Action Model ◽  
Action Models ◽  
Special Case ◽  
General Method

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Multiplicative Reticular Action Model (XRAM). XRAM can include interactions between latent variables, multilevel random coefficients, latent variable moderators, and novel constructs such as factors of paths and twin genetic decomposition of multilevel random coefficients. The method relies on an assumption that all variance sources in a model can be decomposed into linear combinations of independent normal standardized variables. Although the distribution of a variable that is an outcome of multiplication between other variables is not normal, the assumption is that it can be decomposed into sources that are normal if one takes into account the non-normality induced by the multiplication. The method is applied to an example to show how in a special case it is equivalent to known unbiased and efficient estimators in the statistical literature. Two simulations are presented that demonstrate the precision of the approximation and implement the method to estimate parameters in a multilevel autoregressive framework.

scholarly journals Impact of Market Orientation Components on Organizational Performance of SMEs. The single-industry approach " Food and Beverage Sector"

2021 ◽  
Vol 8 (5) ◽  
pp. 504-516
Author(s):  
Akram Abdulsamad ◽  
Noor Azman ALI ◽  
Anuar Shah Bali Mahomed ◽  
Haslinda Hashim ◽  
Abdulwahab Jandab ◽  
...  
Keyword(s):  
Organizational Performance ◽  
Market Orientation ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Customer Orientation ◽  
Equation Model ◽  
Food And Beverage ◽  
Competitor Orientation ◽  
Main Components

This study examines the direct effect of the three main components of the market orientation on the organizational performance of SMEs in Yemen. Four variables are used in the research, are competitor orientation, customer orientation, and inter-functional coordination, as the exogenous latent variables, whereas organizational performance, as the endogenous latent variable. The quantitative approach is applied in this study with causal and descriptive research. The single-sector method is adopted, is the food and beverage sector, by using random sample sampling, the sample size was 640 managers/owners. only 459 samples were valid to conduct the analysis by using the Structural Equation Model (SEM) in SmartPLS Software version 3.0. The findings of the study reveal that the three main components of the market orientation have a positive and significant impact on SMEs' organizational performance. However, the effect size of customer orientation on organizational performance was more than competitor orientation and inter-functional coordination.

Sequenced Ordered Logit Model Considering Latent Variables for Determining Trip Satisfaction of Metro Passengers

2020 ◽  
Vol 2674 (9) ◽  
pp. 755-766
Author(s):  
Tara Saeidi ◽  
Mahmoud Mesbah ◽  
Meeghat Habibian
Keyword(s):  
Public Transportation ◽  
Latent Variables ◽  
Logit Model ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Model ◽  
Access Time ◽  
Ordered Logit ◽  
Ordered Logit Model ◽  
Passenger Satisfaction

Improving the public transportation system to compete with the private modes requires an understanding of passenger perceptions of the service quality (SQ). In the literature, various models have been developed to identify effective SQ attributes and to assess their relationship with passenger satisfaction. However, most of them either ignore the socioeconomic and trip characteristics or consider them by a market segmentation approach. Since these variables can affect passenger perceptions, it is important to include them in the model. This paper aims to capture the effect of socioeconomic and trip variables by combining them with SQ attributes in a satisfaction analysis. An ordered logit model considering SQ latent variables is calibrated to model passenger satisfaction. The measurement part of a Structural Equation Model (SEM) is applied to construct latent variable structures. The case study was on the Tehran metro. The SQ attributes were used to form five SQ latent variables: “comfort,”“information,”“cleanliness,”“service,” and “safety/security.” The results indicate that socioeconomic and trip characteristics, as well as the SQ latent variables, had a significant effect on passenger satisfaction. From the results of this study, “service” and “comfort” were found to be the most effective contributors to satisfaction levels among the SQ latent variables. Among socioeconomic and trip characteristics, gender, education, driving license, egress mode, access time, and trip origin type (i.e., work, education, etc.) were also important in passenger satisfaction.

scholarly journals Determinants of school attendance in elementary school students in Japan: a structural equation model

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Hiromi Nakamura-Thomas ◽  
Nobuyuki Sano ◽  
Donald Maciver
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
School Attendance ◽  
Subjective Health ◽  
Equation Model ◽  
Positive Perception ◽  
Negative Effects ◽  
School Teachers ◽  
School Students

Abstract Background Managing school nonattendance is a priority worldwide. Frequent school nonattendance in early school years has immediate and long-term negative effects. Although strategies to address nonattendance are being developed and implemented, the number of students with school nonattendance issues is increasing. In this study, we explored students’ feelings and perceptions about attending school and the potential determinants of a positive attitude towards attending school. Methods We hypothesized that a positive perception towards attending school was influenced by relationships, perceptions of current circumstances, subjective health, and having someone to share experiences and thoughts with. For examining the hypothesized model, an original questionnaire with 14 items was developed, including perceptions towards school attendance (an item), relationships with friends and school teachers (5 items), current circumstances (4 items), subjective health (3 items), and the individuals available to share experiences and thoughts with (1 item). In total, 6860 children submitted the questionnaire (85.3% response rate) and 6841 responses were included to examine the model. Children were 10 or 11 years old, and selected from 111 state-run schools in 8 randomly selected school districts. Results The final model demonstrated good fit and showed that the latent variable of relationships with friends and school teachers directly impacted on how children felt about attending school. The latent variable of subjective health also directly impacted on how children felt about attending school but not strongly. Other latent variables were not significant. Conclusions The importance of positive relationships with friends and teachers in overcoming school nonattendance has been emphasized in previous studies. This study has provided evidence that these relationships impacted children’s positive perception about attending school in a large sample of students aged 10–11 years. The latent variable of subjective health may require more items to capture mental health.

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

scholarly journals A protocol for modelling generalised biological responses using latent variables in structural equation models

One Ecosystem ◽  
2021 ◽  
Vol 6 ◽  
Author(s):  
James Grace ◽  
Magdalena Steiner
Keyword(s):  
Latent Variables ◽  
General Property ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Evaluation Process ◽  
Theoretical Development ◽  
Biological Responses ◽  
Internal Integration ◽  
Multiple Species

In this paper, we consider the problem of how to quantitatively characterise the degree to which a study object exhibits a generalised response. By generalised response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modelling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g. performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalisation. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper, we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address: (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine-making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.

scholarly journals Generalized ridge estimators adapted in structural equation models

2020 ◽  
Vol 43 ◽  
pp. e49929
Author(s):  
Gislene Araujo Pereira ◽  
Mariana Resende ◽  
Marcelo Ângelo Cirillo
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Real Data ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Specification Error ◽  
Maximum Likelihood Methods ◽  
Pls Method

Multicollinearity is detected via regression models, where independent variables are strongly correlated. Since they entail linear relations between observed or latent variables, the structural equation models (SEM) are subject to the multicollinearity effect, whose numerous consequences include the singularity between the inverse matrices used in estimation methods. Given to this behavior, it is natural to understand that the suitability of these estimators to structural equation models show the same features, either in the simulation results that validate the estimators in different multicollinearity degrees, or in their application to real data. Due to the multicollinearity overview arose from the fact that the matrices inversion is impracticable, the usage of numerical procedures demanded by the maximum likelihood methods leads to numerical singularity problems. An alternative could be the use of the Partial Least Squares (PLS) method, however, it is demanded that the observed variables are built by assuming a positive correlation with the latent variable. Thus, theoretically, it is expected that the load signals are positive, however, there are no restrictions to these signals in the algorithms used in the PLS method. This fact implies in corrective areas, such as the observed variables removal or new formulations of the theoretical model. In view of this problem, this paper aimed to propose adaptations of six generalized ridge estimators as alternative methods to estimate SEM parameters. The conclusion is that the evaluated estimators presented the same performance in terms of accuracy, precision while considering the scenarios represented by model without specification error and model with specification error, different levels of multicollinearity and sample sizes.

The Impact of Green Attributes From Suppliers on Supply Chain Performance

2019 ◽  
pp. 1216-1232
Author(s):  
Jose Roberto Mendoza Fong ◽  
Jorge Luis García-Alcaraz ◽  
Aidé Aracely Maldonado-Macías ◽  
Cuauhtémoc Sánchez Ramírez ◽  
Valeria Martínez Loya
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Descriptive Analysis ◽  
Equation Model ◽  
Ciudad Juarez ◽  
Senior Managers ◽  
Positive Effects ◽  
The Impact

Nowadays, green supplier selection (GSS) is one of the most important activities for companies. Therefore, this research aims to demonstrate the relationship that exists between GSS and the marketing benefits of companies. The chapter proposes a structural equation model that integrates three latent variables. The two independent latent variables concern preproduction green attributes and process green attributes, and they are associated with a dependent latent variable: marketing indexes. Thus, three hypotheses are proposed to relate these latent variables. To validate such hypotheses, a survey is administered to 253 middle and senior managers from the manufacturing industry of Ciudad Juárez. Similarly, a descriptive analysis of the sample and the items is carried out. Results show direct and positive effects among the analyzed variables. However, the highest impact is caused by preproduction green attributes over production process green attributes.

scholarly journals Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

2019 ◽  
Vol 11 (10) ◽  
pp. 2974 ◽  
Author(s):  
Yang Si ◽  
Hongzhi Guan ◽  
Yuchao Cui
Keyword(s):  
Latent Variables ◽  
Logit Model ◽  
Latent Variable ◽  
Structural Equation ◽  
Choice Behavior ◽  
Online Survey ◽  
Mode Choice ◽  
Explanatory Power ◽  
Equation Model ◽  
Internet Technology

With the development of Internet technology, online car-hailing is booming in China, which has profoundly affected people’s travel structures. In order to seek the sustainable development of taxi and online car-hailing services from the perspective of passenger mode choice behavior, the mechanism of passengers’ decision-making procedures and their travel mode choice behaviors were analyzed. To study the influence of latent variable factors on passenger choice behavior, this paper firstly designed a questionnaire, and a structural equation model (SEM) was established for the preliminary study of the relationship between the latent variables and the behavioral intentions using the online survey data. Then, the latent variables were introduced into the Logit model, setting up the SEM-Logit model to explore the mode choice patterns between taxis and online car services. The results showed that the SEM-Logit model with the latent variables is better than a general Logit model in terms of the model precision and hit ratio. Meanwhile, after introducing the latent variables, it was found that convenience, comfort, and economy factors have a significant influence on the model, and the explanatory power of the model increases accordingly.

scholarly journals ANALISIS KEKAMBUHAN ORANG DENGAN SKIZOFRENIA MENGGUNAKAN METODE PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODEL

2019 ◽  
Vol 8 (3) ◽  
pp. 222
Author(s):  
IRA INDRIYANTI ◽  
G.K. GANDHIADI ◽  
MADE SUSILAWATI
Keyword(s):  
Family Support ◽  
Structural Equation Model ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Model ◽  
Medication Compliance ◽  
Equation Model ◽  
Partial Least Square ◽  
Least Square

Schizophrenia is a psychotic disorder characterized by major disorders in the mind and emotions. People with schizophrenia (ODS) can experience recurrence if they do not receive proper care. The latent variable used in this study was ODS reccurence. One method that can determine the relationship between latent variables and latent variables with the indicator is the partial least square structural equation model (PLS-SEM). This study was conducted to see how the structural model of ODS recurrence data and to know the factors that most influence ODS recurrence. The results of this study concluded that the resulting model was good enough with a large R-square value of 0.8577, but not all variables used in this study had a significant effect on ODS recurrence. ODS recurrence is significantly influenced by family support and community social support variables. While medication compliance and physician control regularity will not have a significant effect without family support. The worse treatment of families and communities around ODS recurrence will occur more often.

Structural Equation Model Tests of Patterns of Family Interaction, Peer Alcohol Use, and Intrapersonal Predictors of Adolescent Alcohol Use and Misuse

1993 ◽  
Vol 23 (3) ◽  
pp. 273-316 ◽  
Author(s):  
T. E. Dielman ◽  
A. T. Butchart ◽  
J. T. Shope
Keyword(s):  
Alcohol Use ◽  
Latent Variable ◽  
Structural Equation ◽  
Alcohol Misuse ◽  
Structural Equation Models ◽  
Peer Pressure ◽  
Equation Model ◽  
Adolescent Alcohol Use ◽  
Adolescent Alcohol ◽  
Self Image

A survey of 1,340 students in grades six through twelve was conducted to test, in the context of structural equation models, the predictive validity of a theoretical model of antecedents of adolescent alcohol use and misuse. Constructs including parents' alcohol use, older siblings alcohol use, parents' approval of students alcohol use, older sibling approval of students alcohol use, peer use and approval of alcohol use (PUA), parental nurturance, parental permissiveness, child's grade in school, susceptibility to peer pressure (SPP), and deviant self-image, were included. The final iterations of the models accounted for more than half of the variance in both alcohol use and alcohol misuse. In a standardized solution, the two largest direct effects on both adolescent alcohol use and misuse were from SPP and PUA. When a seven item measure of SPP, including three items specific to alcohol use was used, the SPP latent variable accounted for a somewhat greater percentage of the variance in adolescent alcohol use and misuse than did the latent variable of PUA. When the three alcohol-specific items were deleted from SPP, however, PUA accounted for more variance than SPP. Other predictors, including parental behaviors, proved to be significant when their indirect effects were evaluated. Models predicting alcohol use and alcohol misuse were similar. SPP, PUA, and other significant predictors should be included in future models predicting adolescent alcohol use and misuse, as well as in future interventions targeting these behaviors. Longitudinal studies should be used to test these findings. Evaluation of prevention should include examination of possible interactions of these predictors with each other and with subject subgroup classifications.

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