**Structural** **equation** **modeling** (**SEM**) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for **SEM** for general pedigree data. We explored different **SEM** techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates. The single nucleotide polymorphism (SNP) genotype information from genome-wide association studies (GWAS) data was used for the test of association. The adjustment for hypertension treatment effect was done by the censored regression approach. Two different longitudinal data models, autoregressive model and **latent** growth curve model, were used to fit the longitudinal BP traits. The test of association for SNP was done using a novel score test within the **SEM** framework of strum. We found the 10 SNPs within the GWAS suggestive P value level, and among those 10, the most significant top 3 SNPs agreed in rank in both analysis models. The general **SEM** framework in strum is very useful to model and test for the association with massive genotype data and complex systems of multiple phenotypes with general pedigree data.

**SEM** is mainly a confirmatory technique rather than exploratory and is more likely to be used to determine whether a certain model is valid, rather than to find a suit- able model. However, **SEM** analysis often involves a cer- tain degree of exploratory analysis. By convention, when graphically representing the model the observed **variables** are enclosed by rectangles or squares and **latent** **variables** are enclosed by ovals or circles. Residuals are always unobserved and are represented by ovals or circles. In this work to evaluate the goodness-of-fit of a model the root mean square error of approximation (RMSEA) statistic and the comparative fit index (CFI) were used as these are the most commonly used indices [26]. The RMSEA esti- mates the lack of fit in a model compared to a saturated model. The estimated RMSEA is given by:

[2] **SEM** uses simultaneous **equation** models in which **variables** (both observed and **latent**) may influence one another reciprocally. This makes **SEM** a very suitable method for analyzing tourism demand (Song & Li, 2008). [3] Critically documented that how **SEM** has been applied from a technical perspective. The paper focused on how **SEM** has been used in published papers and provides guidance for future users. The paper then evaluates the methodological quality of applications by assessing how they conform to formal statistical assumptions required for the valid use of these techniques while identifying problem areas and suggesting avenues for improvement. Finally, the paper concludes by summarizing the findings and results and providing a checklist of technical issues to consider when using **SEM** methodology in tourism demand **modeling**.

Measuring an abstract concept, such as “climate change,” “ecosystem structure and/or composition,” “resistance and resilience,” and “ecosystem service,” can pose a problem for ecological research. While direct measurements or units for these abstract concepts may not exist, statistical methods can derive these values from other related **variables**. **SEM** applies a confirmatory factor analysis to estimate **latent** constructs. The **latent** variable or construct is not in the dataset, as it is a derived common factor of other **variables** and could indicate a model’s cause or effect (Hoyle 1995, 2011; Grace 2006; Kline 2010; Byrne 2013). For example, **latent** **variables** were applied to conclude the natural and social effects on grassland productivity in Mongolia and Inner Mongolia, China (Chen et al. 2015). When examining the potential contributions of land use, demographic and economic changes on urban expansion (i.e., green spaces) in the city of Shenzhen, China, Tian et al. (2013) treated land cover change (LCC), population, and economy as three **latent** **variables**, each characterized with two observable **variables**. Economy was found to play a more important role than population in driving LCC. Liu et al. (2016) measured the functional traits of trees as a **latent** variable based on tree height, crown diameter, wood diameter, and hydraulic conductivity. In addition to

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Background: Although dietary quality in middle-age and the prime age of a person ’ s work career might be determined by positive emotional well-being based on socioeconomic status (SES), causation among determinants of dietary quality still remains unclear. Our purpose was to elucidate the **structural** relationships among five-year prior dietary quality, equivalent income, emotional well-being, and a five-year subjective health by sex and age group separately. Methods: In 2003, 10,000 middle-aged urban dwellers aged 40-64 years, who lived in ward A in the Tokyo metropolitan area, were randomly selected and a questionnaire survey was conducted by mail. In 2008, we made a follow-up survey for dwellers, and were able to gather their survival status. A total of 2507, middle-aged men (n = 1112) and women (n = 1395), were examined at baseline. We created three **latent** **variables** for a **structural** **equation** **modeling** (**SEM**), five-year subjective health reported in 2003 and in 2008, dietary quality of principle food groups diversity and eating behavior in 2003, and emotional well-being constructed by enjoyment & ikigai (meaning of life) and by close people in 2003. Equivalent income in 2003 was calculated as SES indicator.

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Table 2 provides details for all the **latent** **variables** used in this study. Confirmatory factor analysis (CFA) was conducted to validate the measurement models. CFA was run for each of the four measurement models, namely CFA-1 (COMM); CFA-2 (SOC); CFA-3 (ENTER); and CFA-4 (ADDIC). Several items were dropped due to the violation of estimation. The revised four-CFA measurement models were consistent with the empirical data [ CFA- 1: (X 2 (df=4)= 9.826, p=.043, CFI= .989, TLI= .973, RMSEA= .069); CFA-2 (X 2 (df=5)= 12.862, p=.025, CFI=.985, TLI= .970, RMSEA= .071); CFA-3 (X 2 (df=2)= 2.539, p=.281, CFI= .999, TLI= .997, RMSEA= .030); CFA-4 (X 2 (df=4)= 4.525, p= .340, CFI= .999, TLI= .998, RMSEA= .021) ]. The overall fit index for the measurement model was then assessed (Figure 2). The goodness- of-fit for the model was satisfied; with normed Chi square: 2.25, p=.000, CFI=.920, TLI= .905, RMSEA= .070. The correlation among all measurement **variables** were statistically significant (p<.05) except the relationship between COMM and ADDIC.

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This paper proposes a new model of measuring a **latent** variable, stock market manipulation. The model bears close resemblance with the literature on economic well-being. It interprets the manipulation of a stock as a **latent** variable, Q*, in the form of a multiple indicators and multiple causes (MIMIC) model. This approach exploits systematic relations between various indicators of manipulation and between manipulation and multiple causes, which allows it to identify the determinants of manipulation and an index of manipulation simultaneously. The main reason of stock market ma- nipulation comes from the fact that information availability is not universally equal. The manipulation is thus critically linked to the creation, arrival and dissemination of information or rumors/mis-information. Thus, the immediate impact of manipulation is on the time profile of returns, or excess returns, from an asset and the excess volatility of returns in excess of the volatility explained by the fundamentals. In this basic set up, the model used these two **variables** as the indicators of stock market manipulation. The main intuition of the MIMIC approach is that some **variables**, or statistics, related to peace are indicators of manipulation, while others signify effects or outputs of causal factors, or inputs, of manipulation. In other words, distinction can be made between causes of manipulation and indicators of manipulation. The causal factors used in this model are classified into five different domains namely pure economic factors as determi- nants of manipulation, labour market conditions, international factors, quality of governance factors and systematic risk factors.

The relationship between a causal agent X and a subsequent Y variable has an answer in a statistical method referred to as mediation analysis. The fundamental mediation model, (i.e. the simple mediation model) is in the conceptual diagram in Figure 1 below. The latter depicts two resultant **variables** (M) and (Y) and two predecessor **variables** (X) and (M), with Y and M causally influenced by X, and M on Y. Therefore, where one causal antecedent X variable influences an outcome Y via a single intervening variable M, a simple mediation model exists with two separate pathways, by which a specific X variable influences a Y variable. Firstly, the pathway that runs through from X to Y without connecting M is the direct effect of X on Y. Secondly, the pathway that is from X to Y is the indirect effect of X on Y through M. The movement is then from precedent X to subsequent M and then from precedent M to consequent Y. Thus, the pictorial representation shows the indirect effect of X on Y through a connecting structure, in which X influences M, and the latter in turn influences Y.

The study population for this research consisted of 450 public secondary school teachers in Nigeria. The teachers were chosen because they were possessing the quality which to improve the students’ academic performance. The data collected from them was momentous for this study and the researcher identified which qualities lead to students’ academic performance. Stratified sampling technique was used in this study because of suitability for the study was crucial (Daramola, 2006). There were sixteen stratums or local governments exist in Kwara state namely Asa (32), Baruten (21), Edu (24), Ekiti (11), Ifelodun (42), Ilorin East (27), Ilorin South (47), Ilorin West (45), Irepodun (37), Isin (12), Kaiama (8), Moro (15), Offa (25), Oke-ero (9), Oyun (12) and Patigi (12). About 651 schools exist in the Kwara state which cut across the 16-stratum mentioned above. In this case, not all 651 schools that exist in the state was be used for the survey. The researcher had to select those schools that will participate in the research exercise through the process of proportionate stratified sampling. To achieve this method, the target sample size (n) was divided by the total target population (N). Thus, the proportionate of each quota was 1.28% (379/29420x100). This will give each school in the local government the equal opportunity to participate in the survey. Therefore, total sample for the study was 450 teachers which adequate for using **structural** **equation** modelling (Hair, Black, Babin, & Anderson, 2010; Kline, 2005; Byrne, 2013).

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Although a growing body of research has implicated disgust in the etiology of a variety of anxiety disor- ders, there remains a paucity of research examining this phenomenon across different cultures. The pre- sent study examined the factor structure and psychometric properties of a newly adapted Persian Disgust Scale-Revised (PDS-R). A large sample (n = 374) of Iranian students completed the PDS-R and other symptom measures of psychopathology including obsessive-compulsive disorder (OCD). Results showed good internal consistency and test-retest reliability of the PDS-R. Confirmatory factor analysis found support for two- and three-factor models of the PDS-R. However, examination of internal consistency es- timates suggests that a two-factor model of contagion disgust and animal-reminder disgust may be more parsimonious. The PDS-R total and subscale scores displayed theoretically consistent patterns of correla- tions with symptom measures of psychopathology. **Structural** **equation** **modeling** also revealed that **latent** disgust sensitivity, defined by the contagion disgust and animal-reminder disgust subscales of the PDS-R, was significantly associated with **latent** symptoms of contamination and non-contamination-based OCD when controlling for **latent** negative affect. The implications of these findings for the cross-cultural as- sessment of disgust in the context of anxiety related pathology are discussed.

Relationships between State K-12 Science Standards, State Religiosity, and State Educational Attainment To examine the degree of correlation between state sci- ence standard grades and state religiosity and educational attainment, we took data from the Pew Forum on Religion and Public Life Project’s Religious Landscape Survey (Lugo et al. 2008) (http://religions.pewforum.org/) and the U.S. Census Bureau for 2009 (www.census.gov). We extracted data from all sources over the same general time frame for consistency across data sets (2008-2009). We compared each state’s % evangelicals, % religion is “very” important to you, % word of God is literally true word for word, and % with at least once a week attend- ance at religious services with the state’s K-12 science standards’ numerical grade as given by Mead and Mates (2009). We also examined the number of bachelor de- grees and number of advanced degrees per state and compared this to each state’s K-12 science standards. We used those two measures of educational attainment since they were the most strongly correlated with ac- ceptance of evolution in Heddy and Nadelson’s (2013) state-by-state assessment of **variables** related to public acceptance of evolution. Numerical grades were squared to ensure normality, and then correlations were run.

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Data analysis techniques make use of in this research were **Structural** **Equation** **Modeling** (**SEM**) using the Partial Least Square (PLS) method. The research instrument was used to compile the questionnaire. The questionnaire that was used in this research was Likert scale. The sampling technique obtained 150 people. The samples chosen were the sub-districts that has the most female population in Karanganyar Regency. The research sample consisted of the people of Matesih District, Jumantono District, and Karanganyar District.

Population is a generalization region consisting of objects or subjects that have certain qualities and characteristics [12]. Population in this research is job director/technical team representing the job owner, service provider/contractor, consultant planner and supervisory consultant who have been involved in maintenance project of Harun Nafsi - H. M. Rifadin Street of Samarinda namely, PT. Wijaya Karya/ Wijaya Karya Inc., PT. Nindya Karya/ Nindya Karya Inc., PT. Citra Kalimantan Pratama/ Citra Kalimantan Pratama Inc., PT. Trialfa Indonesia/ Trialfa Indonesia Inc. and PT. Rexford Pandega/ Rexford Pandega Inc. Sample is part of the population number owned by the population [13]. It is in accordance with the analysis tool used: **Structural** **Equation** **Modeling** (**SEM**).

Abstract: The manufacturing industry is very much crucial for driving the economy of the nation. It has a direct impact on the GDP of any nation and also, it is very beneficial for a country to be strong in the manufacturing industry. This is also the case for a developing nation such as India. Hence, it is imperative for the organization to effectively manage their human resources to optimally utilize them as they are the non-perishable resources of the company. This could be done with the help of Cloud- Computing which is one of the advanced fields of elastic IT (Information Technology). This technology is continuously gaining momentum as it is relatively easier to use in E-HRM (Electronic-Human Resource Management) and also contains less initial investment. The objective of the study is to identify the factors that have an impact on the effectiveness of Human Resource Management by the help of Cloud computing. Various statistical tools like Exploratory Factor Analysis and **Structural** **Equation** **Modeling** have been executed in this study by SPSS 21 and AMOS 21 software.

**Structural** **Equation** Modelling and Partial Least Square: **Structural** **equation** modelling (**SEM**) is a multivariate analysis technique to test theoretical model that hypothesized by the researcher by using a combination of statistical data and qualitative causal assumption 37 . This approach is more confirmatory than exploration, and more suitable for theory testing than theory development. The results of **SEM** include two components, namely the measurement model and the **structural** model. The measurement model provides the relationship between **latent** **variables** and observed **variables**, which aims to provide reliability and validity based on these **variables**. The **structural** model provides the path strength and the direction of the relationship between **latent** **variables**. The combination of both is essential to ensure the quality of a research 38 .

If the coefficients between **variables** are statistically significant (t-value ≥ 1.96), it can be argued that the evaluation produces good results. From the figure above, it can be seen FC does not significantly influence on PEU (t-value 1.57<1.96). This implies that sufficient infrastructure and internet network as supporting infrastructure do not have significant effect on perceived ease of use of F-Learn. Similarly, SE does not significantly affect PEU (t–value 1.04<1.96). This result can be interpreted that self reliance in using the system does not have significance on perceived ease of use of F-Learn. Further, PE does not significantly affect PEU (t-value 1.04<1.96), implying that clear and not confusing application and understandable do not affect perceived ease of use of F-Learn.

**Structural** **equation** model according to whether the **variables** can be observed directly. The **variables** are divided into two categories: **latent** variable and measurable **variables**. **Latent** variable is not directly observable **variables**. Such as satisfaction, work pressure, attitude. Measurable **variables** can be directly observed **variables**. Such as student achievement, income, price. According to whether the other **variables**. **Variables** are divided into endogenous and exogenous **variables**. The endogenous variable is affected by other factors in the model. The exogenous **variables** are not affected by other factors.

incentives to control cost measurement model is insignificant and hence, the model fits the data (Kline, 2010). According to Hair et al., (2014) and Kaplan (2009), Z-values (critical ratios) and coefficient of determination (R 2 ) figures are important in explaining the significance and effects of the different parameters within a model. Correlation values and standard errors showed that all the coefficient values were less than 1.00. Z statistics had a positive value higher than 1.96 and therefore deemed to be very suitable. The Z test statistics showed the significance or otherwise of the path coefficients of the model. As indicated by Kline (2010), utilising a two-tailed Z-test with a significance level of 0.05, path coefficient is significant if Z statistics exceeds 1.96. All Z-values resulted from the data analysis exceeded 1.96 and therefore implies that the indicator **variables** loadings are very significant. R 2 , which is the coefficient of determination, measures the predictive accuracy of the model. The effect of measurement of R 2 spans between 0 and 1. The value 1 signifies perfect accuracy of prediction (Hair et al., 2014). A value of 0.75 or greater is seen as substantial, 0.50 is moderate while 0.25 or lesser signifies reflects weak accuracy of prediction (Henseler et al., 2010). From the results of CFA analysis, the robust fit indices met the prescribed cut-off criteria and hence the model sufficiently fits the data. All parameters estimates were found to be both statistically significant and viable.

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working memory with the intercept of comprehension monitoring, we find convergence and also a notable difference with a recent study by Kim (2015) of Grade 1 children in South Korea. In Kim’s study vocabulary was found to be a strong predictor of concurrent comprehension monitoring skill in Grade 1. Similarly, our study found that children’s vocabulary in Grade 1 accounted for significant variability in the comprehension monitoring skills of first graders. Together, these findings confirm the influence of vocabulary’s status as a lower-level or foundational language skill that supports more complex types of language processing. Other recent work also has demonstrated the influence of vocabulary on another higher- level language skill, namely inference making (Authors, 2015a, 2015b; Lepola, Lynch, Laakkonen, Silven, & Niemi, 2012). The size of the correlation between the two **variables**, together with other work demonstrating a separation between lower-level language skills (including vocabulary) and higher- level language skills (including comprehension monitoring) in grades 1 through 3 (Authors, 2015c), supports the viewpoint that these language skills are related but cannot be assumed to serve as proxies for each other. Critically, our study adds to Kim’s (2015) finding with a different population, demonstrating that the relation between the lower-level skill of vocabulary and the higher-level skill of comprehension monitoring generalizes across language and school systems.

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The results of construct reliability testing with the confirmatory analysis 2nd order in Table 6 above show that constructs have good reliability and give meaning that the elements that measure the construct / **latent** **variables** of self-identity meet the unidimensional criteria 0.830 and Cronbach’s Alpha 0.729. The validity and reliability test of the construct produced valid and reliable items that were able to reflect the elements of self- identity, namely the items in numbers 3, 4, 8, 11, 12, 24, 27, 28, and 40, while the items that were unable to reflect on self- identity, that is, items in numbers 1, 2, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 15, 26, 29 , 30, 31, 32, 33, 34, 35, 36, 37, 38 and 39.Based on the management and analysis of research data based on the processing and analysis of research data on the elements of the variable / construct identity formed by using Confirmatory Factor Analysis 2nd Order, the results show that the model can be accepted, because all elements are able to reflect the **variables** / constructs formed.