Structural Equation Modeling and AMOS Paper Example

Introduction

Structural Equation Modeling is a casual modelling statistical technique that is constituted of a varied set of mathematical equations, statistical methods, and computer algorithms that are designed to correctly fit networks of constructive data. This type of modelling involves path analysis, modelling of the latent growth, confirmation of factor analysis as well as partial least of squares path modelling. AMOS (Analysis of Moment Structures) is statistical software which is an added SPSS module that is also used for path analysis, confirmatory factor analysis and Structural Equation Modeling This technique is also known as casual modelling software or the analysis of covariance. AMOS is a visual program of the greater SEM which enables individuals to draw models graphically by use of simple drawing tools.

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Advantages of Structural Equation Modeling

The Structural Equation Modeling has numerous advantages due to its wide range of applications and the use of complex and appropriate techniques. These modelling equations are normally applied in the assessment of many unobservable and latent constructs. Structural Equation Modeling is also significantly useful in invoking the measurement model which usually defines latent variables through the process of using single or multiple observed variables and also a structural technique which depicts the existing relationship between various variables.

Structural Equation Modeling offers a significant link between various constructs of a more complex structural equation model which can readily be estimated by the utilization of numerous independent regression equations or even via other appropriate techniques such as those methods which are employed in LISREL. The other advantage is that the utilization of Structural Equation Modeling brings about the desired suitable results in social sciences since it has the ability to attribute the relationship between observable variables from the unobservable variables. For instance, the concept of human intelligence is not able to be measured directly since one can measure weight as well as height. However, the psychologists come up with a hypothesis of intelligence through the writing of the measurement instruments with items in terms of questions and hypothesis to measure intelligence by use of this technique. Structural Equation Modeling is, therefore, testing this hypothesis by use of the available data which would not be used by Multivariate techniques such as multivariate analysis of variance, the actor analysis and the multiple regression methods.

In The Structural Equation Modeling, the assumptions made underlying the statistical analyses are said to be accurate, clear and also testable. This model is able to give the researcher unlimited control and potentially hence enabling him or her to further understand the analysis. The Structural Equation Modeling also enables graphical interface software to elevate creativity, innovativeness while facilitating brisk model debugging.

Structural Equation Modeling programs offer overall tests of the model that is able to perfectly fit entities and an individual parameter estimating and evaluating tests concurrently. By use of SEM, one is able to evaluate and compare regression coefficients, standard deviations, means, and variances simultaneously even when dealing with multiple measurements and entities between the subjects groups. SEMs also enables measurement and also asserting and confirmatory factor analysis models so as to purge errors hence being able to make estimated relationships that are seen among latent variables less contaminated by measurement error. Structural Equation Modeling has the capacity to fit non-standard models such as the flexible handling of longitudinal data hence enabling autocorrelation of error structures through time series analysis as well as databases with a variable that are non-normally distributed and incomplete data. This last characteristic of Structural Equation Modeling is its most striking quality. Structural Equation Modeling also offers a unifying layout and framework through which numerous linear techniques can fit by use of flexible and powerful software unlike in Multivariate techniques.

Advantages of AMOS (Analysis of Moment Structures)

AMOS has a wide range of application hence it is a significant component of SEM. In the calculation of SEM, AMOS is important as it uses various unique methods to bring out the desired results. AMOS uses the methods of maximum likelihood, scale-free least squares, generalized least squares as well as unweighted least squires hence making this model quick as it swiftly performs complex computations for Structural Equation Modeling and displays the required results.

The other advantage is that AMOS has the "AMOS graphic" window that offers various useful features to easily visualize and interpret data such as the tool for attaching data, observed variables, unobserved variables, covariance, cause-effect relationship, naming variable and error Term. These features make AMOS suitable for quantitative research as compared to the other methods.

AMOS is able to help one determine the effects of loads on various physical structures as well as their components. Therefore, this advantage is the reason why this model is applied in mechanics, mathematics and materials science. AMOS has the ability to be applied also in the determination of deformations, stresses, support systems, accelerations, stability, and support reactions.

AMOS is also important because it is able to evaluate if or not a given structure design will be able to ultimately withstand the many external and internal forces and stresses that are to be expected for that kind of design. This analysis is specifically significant and beneficial in the determination of the causes and possibilities of structural failure.

AMOS is one of the most significant and reliable software packages that ultimately examines and evaluates the Structural Equation Model. With this software, one is able to collect data of all the variables, set up a sequence that normally is based on many hypotheses of AMOS hence being able to explain the existing relationship among all the observed and unobserved variables. Therefore, one is able to assume possible measurement errors in all the variables hence helping him come up with hypothesized variables as belonging in one pool conceptually.

Reasons to Choose AMOS in Quantitative Research

There are many reasons as to why AMOS was chosen in this quantitative research. From the above discussion, it is evident that AMOS has many advantages over the other models that can be used in this quantitative research. AMOS program is of high accuracy in examining the hypothesized model. It estimates the path model via the establishment of the variance-covariance matrix and also reads the path diagram as input. Through the use of this program, the overall fit of the quantitative research model will be assessed by use of the many components and features of AMOS. The program will also serve as a confirmatory factor analysis for all the measurements of the research hence confirming the adequacy of the models used.

Conclusion

From the above discussion, it is evident that both Structural Equation Modeling and AMOS have various advantages that make them suitable than other models like Multivariate techniques such as multivariate analysis of variance, the actor analysis and the multiple regression methods. These techniques are a general hence wide range of applications, chiefly linear, accurate, fast, and offers explanatory elements.