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.
Many of us might be familiar with concepts like Multiple Regression Analysis and Factor Analysis, this in simple term, is a combination of these techniques. It is, in fact, a mere extension of General Linear Model. You can test a bunch of regression techniques at the same time.
Structural Equation Modelling includes a model that makes room for a lot of other statistical techniques such as path analysis, confirmatory factor analysis and latent growth modelling etc. This is impressive as SEM as a type of model covers many models that are both traditional and complex. It is also effective in the assessment of variance and Multiple Regression along with enabling modelling with latent variables.
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 eliminate or minimise measurement errors in relationships involving latent variables.
- You can use models that are not standard including databases containing data which is not enough and incorrectly distributed.
- SEM houses multiple features of its own such as Graphical Interface Software. It aids in enhancing creativity and enables model debugging.
- Additionally, SEM contains a framework that enables linear models when its software allows the same.
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.
You need to be using path diagrams to show the relationship between manifest and latent variables. Usually, SEM tests are done by assuming that appropriate and accurate data have been modelled. SEM as a technique is largely dependent on this statistical software called AMOS ( Analysis of Moment Structures). It produces tabular results similar to the ones, one can see in SPSS, considering it is an added module of the same. It is easier to come across relationships between two different concepts in areas such as marketing, social science etc., when you are into statistical research. As far as AMOS is concerned, concepts are considered to be latent variables, and these are evaluated by a couple of pointers using SEM. AMOS is also called as casual modelling software, it aids in drawing graphic models with the help of user-friendly tools.
Methods used by AMOS
Let’s take a look at some of the methods adopted by AMOS with regard to accessing coefficients of Structural Equation Modelling.
- 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.
- Asymptotic Distribution-Free: It is recommended when you have large samples containing non-normal data and to analyse covariance structures.
Model Construction
You have to begin by clicking the ‘Start’ button and choosing the ‘AMOS Graphic’ button in order to get the software running. Soon after, you will see a window appearing; it would read ‘AMOS Graphic’. Use that window to chart your SEM model yourself.
- 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.
- Names: It is important that you identify the variables correctly in order to work with them. Clicking right on any variable and choosing the option, ‘Object Properties’ will enable you to name the variable.
These are just some of the many icons in AMOS that you can use to draw a SEM model.
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. Here are some of the results that you get through AMOS:
- 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.
Most importantly, AMOS will not give out any result but it will show error message in case you omit details or enter data incorrectly, moreover, it can identify blank cells in the window too. AMOS aids in enabling the functioning of SEM analysis and thereby makes it easy to arrive at statistics, where direct measurement of something is not possible.