The application of structural equation model in psychological research

In this paper, we have discussed and analyzed the impact on the university library service image using questionnaire data of the college students in Zhejiang Area based on the Structural Equation Model (SEM). The determinants include the service time, the service consciousness, the service space and the service attitude. And not only that, but this study put forward tactics for the improve university library service image based on this paper's result.

A method identifying key optimisation points for aircraft seat comfort

This article presents a short and non-technical introduction to Structural Equation Modeling or SEM. SEM is a powerful technique that can combine complex path models with latent variables (factors). Using SEM, researchers can specify confirmatory factor analysis models, regression models, and complex path models. We present the basic elements of a structural equation model, introduce the estimation technique, which is most often maximum Likelihood (ML), and discuss some problems concerning the assessment and improvement of the model fit, and model extensions to multigroup problems including factor means. Finally, we discuss some of the software, and list useful handbooks and Internet sites. What is Structural Equation Modeling? Structural Equation Modeling, or SEM, is a very general statistical modeling technique, which is widely used in the behavioral sciences. It can be viewed as a combination of factor analysis and regression or path analysis. The interest in SEM is often on theoretical constructs, which are represented by the latent factors. The relationships between the theoretical constructs are represented by regression or path coefficients between the factors. The structural equation model implies a structure for the covariances between the observed variables, which provides the alternative name covariance structure modeling. However, the model can be extended to include means of observed variables or factors in the model, which makes covariance structure modeling a less accurate name. Many researchers will simply think of these models as ‘Lisrel-models,’ which is also less accurate. LISREL is an abbreviation of LInear Structural RELations, and the name used by Joreskog for one of the first and most popular SEM programs. Nowadays structural equation models need not be linear, and the possibilities of SEM extend well beyond the original Lisrel program. Browne (1993), for instance, discusses the possibility to fit nonlinear curves. Structural equation modeling provides a very general and convenient framework for statistical analysis that includes several traditional multivariate procedures, for example factor analysis, regression analysis, discriminant analysis, and canonical correlation, as special cases. Structural equation models are often visualized by a graphical path diagram. The statistical model is usually represented in a set of matrix equations. In the early seventies, when this technique was first introduced in social and behavioral research, the software usually required setups that specify the model in terms of these matrices. Thus, researchers had to distill the matrix representation from the path diagram, and provide the software with a series of matrices for the different sets of 1 Note: The authors thank Alexander Vazsonyi and three anonymous reviewers for their comments on a previous version. We thank Annemarie Meijer for her permission to use the quality of sleep data. Introduction Structural Equation Modeling 2 parameters, such as factor loadings and regression coefficients. A recent development is software that allows the researchers to specify the model directly as a path diagram. This works well with simple problems, but may get tedious with more complicated models. For that reason, current SEM software still supports the commandor matrix-style model specifications too. This review provides a brief and non-technical review of the basic issues involved in SEM, including issues of estimation, model fit, and statistical assumptions. We include a list of available software, introductory books, and useful Internet resources. Examples of SEM-Models In this section, we set the stage by discussing examples of a confirmatory factor analysis, regression analysis, and a general structural equation model with latent variables. Structural equation modeling has its roots in path analysis, which was invented by the geneticist Sewall Wright (Wright, 1921). It is still customary to start a SEM analysis by drawing a path diagram. A path diagram consists of boxes and circles, which are connected by arrows. In Wright’s notation, observed (or measured) variables are represented by a rectangle or square box, and latent (or unmeasured) factors by a circle or ellipse. Single headed arrows or ‘paths’ are used to define causal relationships in the model, with the variable at the tail of the arrow causing the variable at the point. Double headed arrows indicate covariances or correlations, without a causal interpretation. Statistically, the single headed arrows or paths represent regression coefficients, and double-headed arrows covariances. Extensions of this notation have been developed to represent variances and means (cf. McArdle, 1996). The first example in Figure 1 is a representation of a confirmatory factor analysis model, with six observed variables and

Why physically active people report lower anxiety than those who are inactive is not well understood. This study examined whether physical self-concept and self-esteem would mediate associations of self-reported physical activity with anxiety disorder symptoms in young women, a population with elevated risk for developing an anxiety disorder. College women (N = 1036, mean ± SD = 19.7 ± 2.9 yr) completed a physical activity recall, the Psychiatric Diagnostic Screening Questionnaire, and the Physical Self-Description Questionnaire. Structural equation modeling was used to test hypotheses. Physical activity had inverse, indirect associations with symptoms of social phobia, generalized anxiety disorder, and obsessive-compulsive disorder that were expressed through its positive association with specific and global physical self-concept and self-esteem. The results were independent of similar relations with symptoms of major depressive disorder as well as the estimates of body fatness and use of psychotropic medications. These correlational findings provide initial evidence to warrant experimental efficacy trials of whether physical activity will reduce the risk of anxiety disorders in young women by positive influences on physical self-concept and self-esteem.

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.

Today consumers’ demands and choices of products or services are constantly changing rapidly in the internet environment of information technology. The markets emphasize quality, service, and customization, which has been changing the consumption patterns in the decision-making process or companies’ production patterns. The enterprises are essential to respond the changes of the consumption side and production side in order to achieve sustainable development. Moreover, it is important to focus on the relationship of interaction with consumers. This study was intended to explore the influence of brand as symbol on consumer behavior to understand the relations between brand as symbol, perceived transaction value, perceived acquisition value, message-response involvement, and customer loyalty. The empirical analysis is performed by using Structural Equation Modeling (SEM), including measurement and structural models. The measurement models are examined with Confirmatory Factor Analysis (CFA) to identify the relations between latent variables and observed variables whereas the structural models are used to find out the relations between latent variables. Meanwhile, path analysis is adopted to understand the influence between the variables. A questionnaire survey was carried out on customers of chain beverage stores in the Chiayi area through convenient sampling 400 copies were administered and 387 valid samples were retrieved. The response rate was 97%. The results indicate that brand as symbol has significant positive influence on perceived transaction value, perceived acquisition value, and customer loyalty. This means brand as symbol alone can have an effect on customer loyalty and will not be influenced by the level of message-response involvement. This conclusion can be applied to help brand managers to establish close relations with consumers when performing brand design.

The severity of traffic barrier crashes has been modeled in the literature based on human, road, and traffic barrier factors. However, all these factors interact in a complicated way so the relationship between these factors still remains unclear. A structural equation model (SEM) can be used to capture the intricate relationships between the contributory factors and the latent or unobservable factors. This study was conducted using the SEM to model the complicated relationships between the confounding factors and traffic barrier crash severity. Due to possible differences across different road classifications, the SEM was applied separately to the highway and interstate systems. The SEM involves the measurement or confirmatory factor analysis (CFA) model and the structural model involving the interrelationships between the factors. This study evaluates traffic barrier crash severity in terms of numbers of death, injury, and severity of crashes. This study examined the nature and causes of severe traffic barrier crashes in Wyoming. The results indicated that road conditions, traffic barrier types, risky driving, and force are factors that need to be considered in addressing the severity of traffic barrier crashes. The methodology in this study also addresses categorical predictors and model selection techniques for specifying the measurement model and the structural model which make up the SEM. A careful sequential modeling framework was used to build the structural model characterizing the interrelationships among the latent variables as well as evaluation of the fitted SEM based on goodness of fit indices.

This research aimed to establish the best-fit structural model for learning the Filipino language. Specifically, the study intended to determine the relationship between the exogenous variables: classroom management, academic locus of control, communication competence, and the endogenous variable: language learning attitudes. Four hundred respondents, chosen through stratified random sampling, took the survey. The mean, Pearson r, regression analysis, and structural equation modeling (SEM) analyzed the data. These are the results: both exogenous and endogenous variables have high levels, the relationship between classroom management, academic locus of control, and language learning attitudes are moderate, positive, and significant. However, communication competence did not substantially affect language learning attitudes. Of the three exogenous variables, the academic locus of control has the most influence on language learning attitudes. The structural equation modeling (SEM) result showed all three exogenous variables as predictors of language learning attitudes, with their manifest variables. For example, classroom management manifested by specific teaching strategy and planning and support; academic locus of control demonstrated by being hopeful, being positive, and better planning; communication competence exemplified by communicating with acquaintances and friends. The findings of this study have insinuations for the overall teaching and learning environment. KEYWORDS: structural equation modeling, classroom management, academic locus of control, communication competence, language learning attitudes

Previous work of tourism competitiveness has focused more on integrative destinations, such as nations, states (provinces), cities, or destinations having similar attractions. However, appropriate research perspectives and methods havenot been found for evaluating tourism destinations of different types and different sizes.Moreover, early work of Structural Equation Model applied to tourism science has generally limited to a given destination and has rarely compared among different tourism destinations. Therefore, the main thrust of the paper will be concerned with comparing the tourism competitiveness between Jiuzhaigou and Lushan from tourists' perception perspective by applying structural equation model. Based on the Confirmatory Factor Analysis, Cross-validation, and Invariance Measurement, the theoretical model has stability and validity between different samples. Jiuzhaigou has more advantages than Lushan by comparing tourist perception in resources, community attitudes, integral satisfaction and loyalty except service through the Mean Model of Structural Equation. Structural Equation Models, especially Mean structural Model, are applied to and validated in this study, which is a step forward toward the quantitive research methods of destination competitiveness. It is the first time that latent variable of community attitudes has been introduced into the tourist perception model, which is testified to be positively correlated with the tourists' satisfaction and loyalty.

Adaptation and Validation of the German Sensitivity to Befallen Injustice Scales into French

The purpose of this study was to characterize psychological distress in collegiate music students via analysis of related latent constructs. The relationships between psychological distress, perceived stress, perception of learning environment, financial stress, social support, and resilience were examined simultaneously via structural equation modeling with psychological distress as the primary endogenous variable. A structural model was developed a priori based on established relationships between the latent variables in the extant literature. Each construct was quantified via indicators drawn from appropriate psychometric inventories. Each inventory, and the total measurement model, was assessed for model fit via confirmatory factor analysis. Following this, structural equation modeling was run with the weighted least square mean and variance adjusted (WLSMV) estimator to test the a priori structural model. Significant direct relationships were found between perception of learning environment and perceived stress and between perceived stress and psychological distress. Significant correlational relationships were found among perception of learning environment, financial stress, social support, and resilience. In this study, perception of learning environment predicted perceived stress in a sample of college music students. Additionally, perceived stress was the primary predictor of psychological distress in this sample.

In the social sciences, relations among latent variables are of great interest. These associations are not necessarily linear, and selecting the correct model is of significant importance; otherwise, the inferences drawn from these models might be arbitrarily wrong. Latent variables cannot be measured directly but are operationalized indirectly via multiple indicators. Structural Equation Modeling (SEM) is used to examine the associations between latent variables through the correlational structure in multiple measurements (Bollen, 1989). The model selection process is guided by (robust) model fit tests, such as the (robust) χ2-test (e.g., Satorra & Bentler, 2010) in linear SEM analysis. Although there are extensions for specific quadratic and interaction SEM (QISEM, Büchner & Klein, 2020), these model fit tests are not generally applicable to nonlinear SEM (NLSEM) due to the lack of a saturated comparison model (Büchner & Klein, 2020; Mooijaart & Satorra, 2009). In regression analysis, non-parametric trend estimates and scatterplots can be used to examine the structural form and guide model selection. However, these are not suitable for NLSEM due to measurement errors in the observations. Latent variables cannot be observed directly, but estimates for these are available by using factor scores. Factor scores are transformations of the data that estimate the latent variables with approximation error. Still, factor scores are indeterminate (Grice, 2001), meaning that several approaches will result in different results. However, as the number of measurements increases, the approximation error diminishes, and factor scores become close estimates of the latent variables. In this thesis in Manuscript A, a simple set of assumptions is derived to identify the trends in NLSEM using linear factor scores. These trends are described by the conditional expectation of endogenous variables given exogenous variables. Identification means that the trends can be estimated from observed data. Linear factor scores are simple linear transformations of the data and therefore are straightforward to compute. However, due to the approximation error in linear factor scores, these assumptions are asymptotic in nature, as they are only applicable for a large number of measurements per latent variable, as then the prediction error becomes negligible or can be explicitly modeled. In contrast to previous identification results (Kelava et al., 2017), these asymptotic assumptions used in this thesis allow for cross-relations between the measurements within the exogenous part of the model and within the endogenous part of the model. This implies that cross-loadings and residual covariances are permitted within measurements of the exogenous latent variables (ξ) and within measurements of the endogenous latent variables (η), while cross-relations between measurements of the exogenous part and the endogenous part of the model are disallowed. Independence assumptions on the measurement errors of the exogenous and endogenous parts of the model, along with independence from the latent variables, imply that the Bartlett (1937) factor score (BFS) has a specific structure that connects it to the literature on non-parametric regression with measurement error (Delaigle et al., 2009; Huang & Zhou, 2017). Although, in this literature, the distribution of the measurement error is assumed to be known, this similarity, combined with continuity assumptions on the nonlinear trend, can be used to identify the conditional expectation. This is shown mathematically in Manuscript A. Consequently, trends in NLSEM can be estimated using BFS under the given assumptions either by ignoring the approximation error or by explicitly modeling the approximation error. For a large number of measurements in the exogenous part of the model, the approximation error variance of the BFS on the exogenous part of the model is small. Therefore, this approximation error can be ignored by inputting BFS into non-parametric regression methods such as the LOESS (locally estimated scatterplot smoothing, Cleveland, 1979, 1981). Since BFS are linear transformations of the data, a central limits effect occurs on the approximation error.

The Recurring Deposit (RD) is a type of term deposit, where a customer invests a fixed amount of money at a regular interval for a predetermined period. This encourages a saving habit and the mobilization supports the economy of our nation too. According to the study, predicting and moderating factors significantly improve saving behaviours. In this context, confirmatory factor analysis (CFA) been employed to employ two-phase structural equation modelling (SEM) to validate the proposed measurement model. In order to draw logical conclusions, the structural model validates the proposed relationship between exogenous and endogenous variables in the following stage. Furthermore, it is seen that interaction factors and predictors have a positive effect. The study concluded with an important suggestion for the investment manager and other intermediaries: integrate the financial engineering process while suitably giving investors' requirements first priority. Keywords: Recurring Deposit, financial consultant, Confirmatory Factor Analysis [CFA], Structural Equation Modelling [SEM] and Exogenous and Endogenous variables

Modeling and measuring attributes influencing DevOps implementation in an enterprise using structural equation modeling

Structural Equation Model (SEM) is a combination of two separate statistical methods, namely factor analysis developed in psychology and psychometry and simultaneous equation model developed in econometrics. Factor analysis was first introduced by Galton in 1869 and Pearson (Pearson and Lee, 1904). Spearman's (1904) research is the development of a general factor analysis model in his research relating to the structure of mental abilities, Spearman stated that the intercorrelation test between mental abilities can determine general ability factors and special ability factors. SEM is a combination of factor analysis and path analysis into one comprehensive statistical method. Path analysis itself is the forerunner of the structural equation of Sewwl Wright's research in the field of biometrics. Wright's contribution is to be able to show that the correlation between variables is related to the parameters of a model described by a path (path diagram). In SEM there are 2 variables, namely latent variables (exogenous and endogenous) and indicator variables. SEM has 2 equation models, namely the measurement equation model and the structural equation model. SEM also has 2 errors, namely the error for the measurement equation model and the error for the structural equation model. In general, SEM is formed from the relationship between latent variables and their respective indicator variables. To test whether the existing indicator variables are valid indicators for measuring the latent construct, Confirmatory Factor Analysis (CFA) is used. Data analysis with SEM must meet the existing SEM assumptions. The model feasibility test is carried out based on the goodness of fit criteria. The stages in SEM analysis are theoretical model development, flow chart drawing, flow chart conversion into equation form, input matrix and model parameter estimation techniques, model problem identification, evacuating model parameter estimates, model interpretation and model modification.