Exogenous Latent Variables Research Articles

Study on HIV prevention related knowledge-motivation-psychological model in men who have sex with men, based on a structural equation model

Knowledge-motivation-psychological model was set up and tested through structural equation model to provide evidence on HIV prevention related strategy in Men who have Sex with Men (MSM). Snowball sampling method was used to recruit a total of 550 MSM volunteers from two MSM Non-Governmental Organizations in Urumqi, Xinjiang province. HIV prevention related information on MSM was collected through a questionnaire survey. A total of 477 volunteers showed with complete information. HIV prevention related Knowledge-motivation-psychological model was built under related experience and literature. Relations between knowledge, motivation and psychological was studied, using a ' structural equation model' with data from the fitting questionnaires and modification of the model. Structural equation model presented good fitting results. After revising the fitting index: RMSEA was 0.035, NFI was 0.965 and RFI was 0.920. Thereafter the exogenous latent variables would include knowledge, motivation and psychological effects. The endogenous latent variable appeared as prevention related behaviors. The standardized total effects of motivation, knowledge, psychological on prevention behavior were 0.44, 0.41 and 0.17 respectively. Correlation coefficient of motivation and psychological effects was 0.16. Correlation coefficient on knowledge and psychological effects was -0.17 (P<0.05). Correlation coefficient of knowledge and motivation did not show statistical significance. Knowledge of HIV and motivation of HIV prevention did not show any accordance in MSM population. It was necessary to increase the awareness and to improve the motivation of HIV prevention in MSM population.

A STRUCTURAL EQUATION MODEL FOR THE DESCRIPTION AND COMPARISON OF COMPLAINT BEHAVIOUR AFTER PURCHASING OF ELECTRONIC, FOOD AND TEXTILE PRODUCTS

This study was undertaken to investigate the complaint behaviour exhibited by university students due to dissatisfactions they experienced after purchasing electronic, food and textile products by using a proposed Structural Equation Modelling (SEM). For this purpose, measurements are performed onan interval scale by using the 5-point Likert in order to measure the agreement level of 329 university students on attitude and behaviour statements oriented at complaints. There are 7 factors in the research model including alienation related with complaint (ALN) and controllability (CON) as exogenous latent variables; perceived value of complaint (VAL), the “complaint will be successful” faith (LKH), complaint intention (CI), explicit complaint behaviour (ECB) and implicit complaint behaviour (ICB) as endogenous latent variables. In this study, the effect of alienation of students from the company they shop and of their belief in controllability of the company on general complaint attitudes and complaint behaviours are tested with 13 hypotheses for electronic, food and textile products separately, and the obtained results are compared. In relation to the three groups, research findings have shown the same results for certain hypotheses and different results for certain other hypotheses.

Comparison of Model Fit Indices Used in Structural Equation Modeling Under Multivariate Normality

The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.

구조식 모형에 대한 단계적 접근

Structural equation modeling (SEM) is a widely used in social sciences such as education, business administration, and psychology. In SEM, the latent variable score is the estimate of the latent variable which cannot be observed directly. This study uses stagewise structural equation modeling(stagewise SEM; SSEM) by partitioning the whole model into several stages. The traditional estimation method minimizes the discrepancy function using the variance-covariance of all observed variables. This method can lead to inappropriate situations where exogenous latent variables may be affected by endogenous latent variables. The SSEM approach can avoid such situations and reduce the complexity of the whole SEM in estimating parameters.

Causal Relationship Model of the Information and Communication Technology Skill Affect the Technology Acceptance Process in the 21ST Century for Undergraduate Students

The objective of this study is to design a framework for a causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process (TAP) for undergraduate students in the 21ST Century. This research uses correlational analysis. A consideration of the research methodology is divided into two sections. The first section involves a synthesis concept framework for process acceptance of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century. The second section proposes the design concept framework of the model. The research findings are as follows:&#x0D; 1) The exogenous latent variables included in the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century are basic ICT skills and self-efficacy.&#x0D; 2) The mediating latent variables of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century are from the TAM Model, these includes three components: 1) perceived usefulness, 2) perceived ease of use and 3) attitudes. &#x0D; 3) The outcome latent variable of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century is behavioural intention.

A path model of smoking behaviour among senior high school students in Taiwan

Objective: The purpose of this study was to explore the ways in which social smoking expectations mediate the relationship between adolescent smoking behaviour and the smoking behaviour of family and peers. Design: Descriptive, cross-sectional survey. Setting: Taiwan, Republic of China. Method: The participants were 921 senior high school students who completed a questionnaire to measure smoking status, family and peer smoking and social smoking expectations. The survey was conducted from November to December in 2009. Structural equation modelling was performed to test a mediation model. Analysis was performed using PRELIS 2 and LISREL 8.7 software. Results: After adjusting for students’ gender, age and school type, it is clear that social expectations mediate the relationship between sibling smoking, peer smoking and adolescent smoking. The path coefficients of sibling and peer smoking on social expectations in the mediator model were moderate, β = .15–.41, p &lt; .05, but the coefficient of social expectations on adolescent smoking was strong, β = .95, p &lt; .001. The total indirect effects between exogenous latent variables and adolescent smoking were .54, indicating a strong association that, in this model, is explained by the mediation of social expectations. Conclusion: Determining the mechanisms that influence adolescent smoking is of scientific interest because knowledge of these mechanisms is a prerequisite for the development of recommendations aimed at prevention. With this knowledge, health professionals can develop smoking-prevention policies and education programmes based on reducing the mediating effect of social smoking expectations on the relationship of family smoking and peer smoking with adolescent smoking.

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.

Interaction between satisfaction and loyalty of Lithuanian tourists: a moderating effect of natural features

The moderating effect of natural features on the effect of Lithuanian tourist satisfaction to loyalty is being analyzed in the article. Experience of foreign countries and scientific researches justify the positive effect of tourist satisfaction to loyalty. Nevertheless, this effect is considered as complex due to the possibility of the endogenous or exogenous latent variables’ moderating effect. Natural features of the country may be regarded as such variable. Taking into account that there is a lack of scientific researches about the moderating effect of natural features in the case of Lithuania, it is worth analyzing the interaction effect between tourist satisfaction and loyalty. The aim of the article is to determine the interaction effect between Lithuanian tourist satisfaction and loyalty regarding natural features as a moderator variable. While achieving the aim of the article, the analysis of scientific literature is provided. Furthermore, quantitative research and the analysis of the research results using structural equation modelling are applied. As a research results, the interaction effect of Lithuanian tourist satisfaction to loyalty is determined and the moderating effect of natural features is assessed.

A Spline Regression Model for Latent Variables

Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the relation between endogenous and exogenous latent variables with a spline regression model, where each latent variable is measured by multiple observed indicators. Consequently, the spline regression model is modified to include a measurement model that explicitly expresses the relation of the observed variables to the latent constructs. Maximum likelihood estimation of the model is developed and executed on educational data to illustrate the utility of the model.

Structural Equation Modeling in a Near-Highway Exposure Study: Ultrafine Particles and Association with Cardiovascular Disease Risk

Background: Structural equation modeling (SEM) is a statistical method that has not been used widely in epidemiological studies of ambient air pollution. Aims: Assess the association between individually-calculated exposure to UFP (&lt;100 nm, estimated as particle number concentration or PNC) and cardiovascular disease risk. Methods: Air pollution data based on 55 days of mobile monitoring was used to build a regression model for assigning residential ambient hourly PNC for participants of the Community Assessment of Freeway Exposure and Health (CAFEH) study (N=140, a subset of CAFEH). Participants completed questionnaires on residential and occupational exposure, income, education, smoking, diet, physical activity and stress. Blood samples were analyzed for C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-? receptor II (TNF?); fibrinogen. Ankle brachial pressure index (ABI) as well as systolic, diastolic and pulse pressures were also measured. SEM was conducted using Proc Calis in SAS to determine path loadings, p-values between latent variables, and the individual variables contributing to the latent variables. Results: CRP, IL-6 and TNF? were not normally distributed and had population median values of 1.62mg/L for CRP; 1.61pg/mL for IL-6; and 2256pg/mL for TNF?. Fibrinogen was normally distributed with a mean of 475mg/dL. Initial SEM model development for the dependent latent variable construct cardiovascular disease risk produced the largest standardized estimates with the indicator variables CRP, TNF?, BMI, hypertension, and IL-6. PNC and the independent latent variable construct other combustion exposures displayed a positive association with cardiovascular disease risk, while and socio-economic status displayed a negative association. Conclusion: Initial analysis indicates that our model may have one endogenous latent variable, cardiovascular disease risk, and two or more exogenous latent variables related to air pollution and lifestyle.

Customer Retention Structural Model of Direct Selling Employees

Customer orientation of service employees is seen as important success factor for gaining customer retention in Gano Excel, a direct selling company in Malaysia. The objective of this study is to understand how customer orientation (CO) of service employee (SE) can affect the commitment (COM) and customer retention (CR). A structural model of all the variables was developed for the study. Each variable is measured using 7-point interval scale which is grouped into 3 exogenous latent variables: technical skills (3 items), social skills (3 items), and decision making authority (3 items); and two endogenous latent variables: commitment (3 items) and customer retention (3 items). The responses collected were 119 completed questionnaires whereby, 2 respondents were deleted due to outliers problem (D 2 ). The data was analyzed using Structural Equation Modeling (SEM) through AMOS7. Confirmatory factor analysis of measurement models indicates adequate goodness of fit when 1 item was eliminated through modification indices verifications. Goodness of fit for the revised structural model shows adequate fit a(GFI=0.918, P-VALUE=0.359, RMSEA=0.021 and ratio (cmin/df) =1.053). This study has established four direct significant and positive causal effects: (1) technical skill and commitment; (2) social skill and commitment; (3) technical skill and customer retention and (4) commitment and customer retention. The findings are discussed in details.

P02.130. The relationship of client expectations of massage to changes in pain and affect: results from a practice-based research study

Methods Practice-based research was used to collect client data (N=321) before and after a massage provided by one of 24 licensed massage therapists in Iowa. The pre-massage survey included items regarding their chief complaint, the client expectations of massage scale, the numeric rating scale for pain, and the Positive and Negative Affect Schedule Expanded form (PANAS-X). The post-massage survey included the same measures of pain and affect as well as demographics. Paired t-tests were used to test for significant changes in pain and affect. A structural equation model with outcome, interpersonal, clinical, and educational expectations as latent exogenous variables and changes in serenity, negative and positive affect, and pain as endogenous variables was tested using Mplus.

Bayesian Lasso for Semiparametric Structural Equation Models

There has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables. A basis representation is used to approximate these nonparametric functions in the structural equation and the Bayesian Lasso method coupled with a Markov Chain Monte Carlo (MCMC) algorithm is used for simultaneous estimation and model selection. The proposed method is illustrated using a simulation study and data from the Affective Dynamics and Individual Differences (ADID) study. Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model.

Intangible advertising and its effect on consumer choice

This study examines a new concept, “intangible advertising,” in advertising theory. Unresolved questions on advertising effectiveness and obstacles such as, TV’s high costs make it important to study various factors defining and affecting intangible advertising. Accordingly, this study builds a comprehensive theoretical model explaining the effect of intangible advertising of brand on consumer choice. We examined four exogenous latent variables (advertisement type, product involvement, media and demographics) and one endogenous latent variable (intangible advertising of brand). We found that the exogenous variables successfully explain intangible advertising of brand and all of them collectively affect advertising effectiveness. Then we examined an empirical study on the impact of intangible advertising of brand on consumer choice. The results are reported. Key words: Intangible advertising, brand, advertising effectiveness, scanning electron microscope (SEM).

Measuring Influence of Accessibility on Accident Severity with Structural Equation Modeling

Structural equation modeling (SEM) is a confirmatory, multivariate technique used to examine causal relationships between variables. Related to path analysis with a goal of selecting a model that best explains underlying relationships between variables, SEM is a useful tool for traffic safety research. This study examined the severity of crashes in terms of factors commonly attributed to accidents. These factors included human, vehicle, and roadway factors, along with accessibility measures that were considered relevant in previous studies. In this study, SEM was used to test an a priori model of crash severity. The analysis was carried out in a two-step process. The measurement model was first tested with a confirmatory factor analysis. After the validity of the measurement model was established, a four-latent-factor structural model was run. With an acceptable model fit, the magnitude of standardized path coefficients from the exogenous latent variables provided a means to assess the relative importance of the latent factors on crash severity. The results showed that the human latent factor was the most influential. Although a positive statistical relationship existed between roadway factors and crash severity, accessibility factors had the opposite effect on crash severity, that is, increased accessibility was shown to reduce crash severity.

Residents’ Satisfaction With Community Attributes and Support for Tourism

This study develops and tests a structural model of residents’ support tourism development, with the social exchange theory as its theoretical base. The model incorporates three exogenous latent variables, namely, community satisfaction with neighborhood conditions, community commitment, and satisfaction with community services. Perceived positive and negative impacts of tourism are proposed as the mediating variables between the exogenous latent variables and the ultimate dependent variable, given as support for tourism. Eight path hypotheses are proposed and tested using a sample of 363 residents. Perceptions of tourism impacts were found to influence support for tourism development. Findings also suggest that residents’ satisfaction with neighborhood conditions and community services are important determinants of perceived positive and negative impacts of tourism. Community commitment was found not to be an antecedent of attitudes to tourism. The practical implications of the study are discussed, together with its limitations and scope for future research.

Bayesian analysis for finite mixture in non-recursive non-linear structural equation models

This paper considers finite mixtures of structural equation models with non-linear effects of exogenous latent variables and non-recursive relations among endogenous latent variables. A Bayesian approach is developed to analyse this kind of model. In order to cope with the label switching problem, the permutation sampler is used to choose an appropriate identification constraint. Furthermore, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler, Metropolis-Hastings algorithm, and Langevin-Hastings algorithm is implemented to produce the Bayesian outputs. Finally, the proposed approach is illustrated by a simulation study and a real example.

Pendekatan Integratif terhadap Faktor yang Mempengaruhi Intensi Buruh untuk Mengikuti Aksi Kolektif

This research attempts to build a structural model based on an integrative approacht o explain labor intention to participate in collective action. This research is relevant because most of the existing theories and approaches explained collective action phenomena partially. The main objective of this research ist o analyst the integrative approach of psychological social psychology (individual factors), sociological social psychology (inter-group relation factors) and social constructionism (societal factor) an explaining labor intention to participate incollective action such as demonstrations and labor strikes. This integrative approach research tested a theoretically derived pattern of specific relationship between individual level of analysis, inter-group relation and societal or ideological level of analysis where each level of analysis wasre presented by one or more theories. The research model proposes three exogenous latent variables namely: social representation, organizational commitment and union commitment and four endogenous latent variables that are: social identity, relative deprivation, expectancy-value motives and intention to participate in collective action. These research findings proved that an integrative approach model which was represented by expectancy-value motives (individual level), both relative deprivation and social identity (inter-group level) and social representation (societal level) explain labor intention to participate in collective action significantly in non participant sample. On the other hand, in participant sample there were only two factors namely inter-group relation and socilc ontext explain the emerging of the labor intention to participate incollective action. keywords: collective action, labor, individual factors, inter-group relation factors, and societal factor

Bayesian Analysis for Finite Mixture in Nonrecursive Nonlinear Structural Equation Models

This paper considers finite mixtures of structural equation models with nonlinear effects of exogenous latent variables and nonrecursive relations among endogenous latent variables. A Bayesian approach is developed to analyze this kind of models. In order to cope with the label switching problem, the permutation sampler is used to choose an appropriate identification constraint. Furthermore, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler, Metropolis-Hastings algorithm an Langevin-Hastings algorithm is implemented to produce the Bayesian outputs. At last, the proposed approach is illustrated by a simulation study and a real example.

Bayesian Analysis of Structural Equation Models With Nonlinear Covariates and Latent Variables

In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the proposed model. Markov chain Monte Carlo methods for obtaining Bayesian estimates and their standard error estimates, highest posterior density intervals, and a PP p value are developed. Results obtained from two simulation studies are reported to respectively reveal the empirical performance of the proposed Bayesian estimation in analyzing complex nonlinear SEMs, and in analyzing nonlinearSEMs with the normal assumption of the exogenous latent variables violated. Theproposed methodology is further illustrated by a real example. Detailed interpretation about the interaction terms is presented.