Structural Equation Models—Methodological Factors

In this study, Structural Equation Modeling was used to determine the factors that affect reading skills. To determine the factors data of PISA 2009 was used. It assessed students’ capacities to apply knowledge and skills in reading, mathematical and scientific literacy. It describes some wider findings about what lies behind results. Structural Equation Modeling is examined and four independent latent variables as “reading attitudes,” “study habits,” “stimulate,” and “strategies the teacher used” are determined. It was observed that the most important variable was the “strategies the teacher used” (γ=0.33). The second important latent variable that affected the students’ reading comprehension skill was “teacher stimulating students”(γ=0.26). Another latent variable affecting the students’ reading comprehension level was observed as “the students’ study habits” (γ=0.22). The final latent variable was “attitude towards reading” (γ=0.16)

Purpose: This research was conducted with the aim of analyzing the factors that influence the use of the rental payment system (SIMBAH-E) by adopting the UMEGA model as a technology acceptance model. Methodology/approach: The data analysis method in this research used Structural Equation Modeling (SEM) with the Partial Least Square (PLS) approach. processed via the SmartPLS 4.0 application. Findings: The results of this study show that there is a significant positive influence on the six independent latent variables on attitudes, there is one dependent variable that has a significant negative influence on attitudes, and one variable has no influence on attitudes and interest in use. Practical implications: As well as latent variables of accounting information quality as a form of model development. The researcher used a questionnaire as a research measuring tool, and used a Likert scale in his assessment and used cluster random sampling in selecting respondents with a total of 100 respondents. Originality/value: This research was conducted because the UMEGA model is the newest technology acceptance model so not many people have researched it, and this research contributes the latest thinking by adding a latent variable for the quality of accounting information as a development of the UMEGA model. Researchers used latent variables in the UMEGA model, namely performance expectations, effort expectations, social factors, risk of facilitating conditions, perceived risk, attitudes and interest in use.

This article presents the results of a survey with 13 main activities related to human factors that are executed during kaizen implementation process and were integrated in four independent latent variables (management commitment, education, communication and motivation) that are associated to 14 benefits obtained after its implementation that were grouped in three dependent latent variables (process, workers and customers). The survey was applied to persons with responsibilities in continuous improvement programs and projects in companies located in Mexico. Independent and dependent variables were integrated in a structural equation model that was evaluated using partial least squares algorithms WarpPLS® for finding causal relations among them. Results indicate that management commitment and education are the main factors that guarantee the success for kaizen implementation programs, but that is moderated by a good communication for having good operational process performance for better workers and customer satisfaction.

Latent variables are variables that can not be measured directly. In analysis of causal relationship involving three latent variables, one latent variable can be a moderator or mediator variables. Goodness of Fit moderation and mediation model of latent variables is affected by the value of the canonical correlation between moderator/mediator latent variables with the independent latent variables and dependent latent variables. If the value of both canonical correlation is well , so the Goodness of Fit models of mediation is getting better, while the opposite Goodness of Fit models will be better moderation.

Community development operations are important because they help strengthen communities. The objectives of this research were to analyze and examine the consistency of the community development operations (CDO) model with empirical data, and to study the weight of direct, indirect, and total effect of factors influencing on CDO of Ban Nam Sai Community, Thung Benja Sub-district, Tha Mai District, Chanthaburi Province. The population in this study was 173 people who are representing households living in Nam Sai Village. The samples were 121 people, calculating by Taro Yamane’s formula. The research tool in this study was the questionnaire-based survey. Analyzed and examined the consistency of the community development model of Ban Nam Sai community with empirical data based on the model goodness of fit indices used a Structural Equation Model. Analyzed weight of direct, indirect and total effect of causal factors on community development operation of Ban Nam Sai community by used a Structural Equation Model with statistic Path analysis. The results were found that The CDO model of Ban Nam Sai community developed consistent with the empirical data. It was also found that Leadership, public participation and community communication had direct effects on community development operations of Ban Nam Sai community. Besides, community communication was found a direct effect on public participation. Among these studied variables, leadership was found to have an indirect effect on community operations of Ban Nam Sai Community through public participation and community communication. Community communication also had an indirect effect on the community development operation of the Ban Nam Sai community through public participation. The total effect on community development operations of Ban Nam Sai community. The highest total influential factor was public participation, followed by leadership and community communication respectively. Suggestion from the research results, the community development operation model of Ban Nam Sai Community is suitable for use. The model should be developed and applied to use to the similar context of communities/villages for further community development.

Using a national sample of Mexican Americans, the authors developed structural equation models of the effects of four eighth-grade latent variables on educational expectations assessed 6 years later. Models for Mexican American women and men did not differ from one another. The latent variable, parents’ influences, had a strong positive effect on educational expectations at late adolescence. Parents’ influences dominated the effects of the other latent independent variables, socioeconomic status, eighth-grade achievement test scores, and self-perceptions. The four eighth-grade latent variables explained 41% of the variability in educational expectations at late adolescence. The findings reveal consistencies and inconsistencies with previous research findings and models.

The Role of Providers in Mental Health Services Offered to American-Indian Youths

The paper investigates the relation between social capital and life satisfaction focusing on the distinction between bonding and bridging as introduced by Putnam (2000). Using the latest version of the combined World and European Values Surveys, we first address the question of measurement of social capital by means of a multi-step factor analysis. Through this procedure, we find that proxies typically used for social capital tend to polarize around two dimensions interpreted as bonding and bridging. These two dimensions are in fact associated with a single latent variable with opposite signs suggesting that they describe two sides of the same latent variable rather than two independent latent variables. We call this latent variable the locus of socializing and use it to explore the relation between social capital and life satisfaction across world citizens and across groups of similar countries. The results indicate that people with extreme bonding or bridging behavior are less happy than people with more balanced attitudes. Unlike the literature on social capital and economic growth that finds bridging attitudes more desirable than bonding attitudes, we find that bonding attitudes are at least as important as bridging attitudes for life satisfaction. This suggests that the social capital dimensions important for economic growth may not necessarily coincide with the social capital dimensions important for life satisfaction.

Unpacking the parallel effects of parental alcohol misuse and low income on risk of supervisory neglect

The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking.

It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, the choice of which indicator to use for this purpose receives little attention, and many analysts use the first indicator without considering whether there are better choices. When all indicators of the latent variable have essentially the same properties, then the choice matters less. But when this is not true, we could benefit from scaling indicator guidelines. Our article first demonstrates why latent variables need a scale. We then propose a set of criteria and accompanying diagnostic tools that can assist researchers in making informed decisions about scaling indicators. The criteria for a good scaling indicator include high face validity, high correlation with the latent variable, factor complexity of one, no correlated errors, no direct effects with other indicators, a minimal number of significant overidentification equation tests and modification indices, and invariance across groups and time. We demonstrate these criteria and diagnostics using two empirical examples and provide guidance on navigating conflicting results among criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Researchers commonly ask whether relationships between exogenous predictors, X, and outcomes, Y, are mediated by a third set of variables, Z. Simultaneous equations decompose the relationship between X and Y into an indirect component, operating through Z, and a direct component, the relationship between X and Y given Z. Often, X, Y, and/or Z are measured with error. Structural equation modeling is widely used in this scenario. However, sociological data commonly have a nested structure (students within schools, residents within local areas). Hierarchical linear models represent such multilevel data well and can handle errors of measurement, but have not incorporated simultaneous equations for direct and indirect effects. This article incorporates the study of such mediated effects into the hierarchical linear model, naturally extending the analysis to include unbalanced, multilevel designs and missing data. The authors illustrate the approach by examining the extent to which neighborhood social control mediates the relationship between neighborhood social composition and violence in Chicago.

Cross-sectional. To develop a latent structural model to demonstrate the relationship between factor structures of protective health behaviors and pressure ulcer (PrU) outcomes among participants with spinal cord injury (SCI). Data were collected at a large specialty hospital and analyzed at a medical university in the Southeastern USA. A total of 1871 participants with traumatic SCI of at least 1 year duration were included. A latent PrU variable was measured by four observable PrU-related outcomes. Latent variable structural equation modeling was performed to assess the relationship between latent protective behavior (fitness and productive activities) and latent PrU outcome. Several exogenous variables were included: sex, age, race, marital status, injury severity and years since injury. The protective behavior dimension had a significant direct effect on the latent PrU (direct effect=-0.275, P<0.01). All direct relationships between protective behavior dimension and healthy behaviors were also significant (rfit=0.899 and rproductive activity=0.568). Relationships between the latent PrU and fitness (indirect effect=-0.247) and productive activities (indirect effect=-0.156) were mediated through the protective behavior dimension. Participants who were African American, had higher injury levels, and had longer time since SCI were more likely to have worse PrU outcomes. The overall findings of this study suggest the need to enhance healthy behaviors to prevent adverse PrU outcomes, especially among people who are African American, have longer time since SCI and have higher level of SCI.

In an Italian sample (N = 483, 78.23% women, mean age = 27.61 years old), we used structural equation modeling with latent variables and interactions to analyze the direct, indirect, and interactive effects exerted on right-wing authoritarianism by the Big Five factors of personality and by dangerous world beliefs. Openness, Neuroticism, and Conscientiousness exerted direct effects on right-wing authoritarianism; the first two relationships were partially mediated by dangerous world beliefs. Most importantly, the relationship between dangerous world beliefs and right-wing authoritarianism was moderated by Openness: dangerous world beliefs significantly influenced right-wing authoritarianism solely for participants high in Openness. Limitations and possible developments of this research are discussed.

Social and behavioral scientists have long used path analysis and related linear structural equation models (SEMs) to decompose parameters of ordered systems of equations into “direct effects” and “indirect effects” through mediating variables. These decompositions have been used to address substantive questions of fundamental interest, for example, how a person’s social background affects his/her earnings through education. However, in general, the “direct effects” and “indirect effects” defined in and estimated from these models should not be given causal interpretations, even in randomized experiments. To illustrate this, we first define various direct and indirect effects using potential outcomes notation and discuss situations where an investigator might want to consider these. Second, we consider identification of these effects: the required identifying assumptions are more often than not implausible for the kinds of data collected and questions considered in social and behavioral research. Third, we present other identifying assumptions that might be used to identify direct and indirect effects, and then briefly discuss different methods to estimate these effects, including regression, instrumental variables, marginal structural models, and weighting methods.Finally, we introduce an alternative approach to mediation (principal stratification), define several possible effects of interest, and briefly discuss identifying assumptions and estimation.KeywordsStructural Equation ModelCausal ModelControl EffectMarginal Structural ModelCausal ParameterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.