SEM Using AMOS
Stat www.statswor swor k.com Statistical k Consulting Company
What is SEM Structural Equation Modelling (SEM) is a widely used technique in statistics to primarily study relationships based on structures. It encompasses various models involving mathematics, statistical procedures etc. This technique is known to be extremely effective when it comes to measuring latent constructs.
“
SEM Using AMOS
Benefits Of SEM Here are some of the significant benefits of using Structural Equation Modelling as a technique: If you are a researcher looking to expand your scope, using SEM would be the ideal choice for the assumptions which brings a lot of clarity and they are testable too. It enables survey sampling analyses. Coefficients, means and variances from different subjects can be compared at once. You can use models that are not standard including databases containing data which is not enough and incorrectly distributed.
Functioning of SEM As a researcher, you ought to begin by choosing a model. And, you have to collect data only after figuring out how to evaluate constructs. Finally, you supply the SEM software with sufficient amount of data. The software then fits the data to the chosen model and generates the outcome. The outcome would usually include estimates and overall model fit figures.
SEM And AMOS
SEM Structural Equation Mode lling (SEM) is a widely used technique in statistics to primarily study relationships based on structures. It encompasses various models involving mathematics, statistical procedures etc.
AMOS SEM as a technique is largely dependent on this statistical software called AMOS ( Analysis of Moment Struc tures ). It produces tabular results similar to the ones, one can see in SPSS, considering it is an added module of the
SEM Using AMOS
Use Big Images To
Methods used by AMOS • UnWeighted Least Squares: It eliminates residual errors in order to access the conditional mean. • Generalised Least Squares: It estimates the coefficients in a linear regression model if some correlation exists amongst the residuals. • Generalised Least Squares: It estimates the coefficients in a linear regression model if some correlation exists amongst the residuals.
Model Construction • • • • •
Data Input: You will need to enter your data for the purpose of SEM Analysis. Choose a name for your file and record your data in AMOS. Icons: Go with Rectangle and Circle icons for observed and unobserved variables respectively. Establishing Relationships: Draw an arrow to denote the relationship between observed and unobserved variables. Covariance: Choose a double-headed arrow to denote the covariance amongst variables. Error Term: The icon denoting the same is situated next to the unobserved variable icon. The Error Term icon is present to chart the latent variable.
Text Results in AMOS While graphic window will only show you some part of the data including standardized and unstandardized regressions, text output will reveal the results in its entirety. • Number of Variables: The number of observed and unobserved variables used in the process of SEM analysis will be revealed. • Data normality: It is important that the data used in SEM analysis is normally distributed. The text output of AMOS will help us gauge the normality of data. • Impact of Path Analysis: Modification Index results tell us how impactful the path drawn by you can be, if the index is high, it is a sign for you to draw more paths.
Thanks! Any questions? You can find me at: info@statswork.com www.statswork.com