Analyzing structural relationships is made feasible with the help of Structural Equation Modelling. It is the amalgamation of factor analysis and regression analysis. These techniques are used to study the relationship between structural variables and constructs in latent variables. This method is preferred by researchers because of its ability to perform multiple operations in a single analysis.
SEM is applied in building complex models using latent variables. A graphical or programming interface is necessary to observe and obtain variables, such as SPSS AMOS software.
Characteristics of efficient SEM
- SEM facilitates controlling and understanding of the analyzes.
- Assumptions derived from the different analysis techniques are transparent in nature and could be tested.
- Creative graphical user interface is employed and it allows rapid model debugging.
- Model fit and individual parameter estimate tests are run alongside the process.
- Models involving estimation of the relationship between latent variables are kept free from errors that could arise in measurement.
- Models are customized to fit using different database of error structures that are autocorrelated.
- Multiple linear models can be fit under a single unifying framework.
About SPSS AMOS
Being an additional SPSS module, SPSS AMOS is specially used for performing structural equation modelling, path analysis, confirmatory factor analysis (CFA). AMOS is programmed to represent SPSS derived results in visual models, during structural equation modelling.
Attributes of SPSS AMOS
- Simple drawing tools are used for drawing models.
- Computations regarding SEM are quickly resolved.
- Results obtained by both graphical and non-graphical applications.
- Various methods are used to calculate SEM coefficients.
- Manual drawing of model is allowed.
How SEM is executed by using SPSS AMOS?
- Attach data – By choosing suitable file name, data to be used for SEM analysis is attached in the SPSS AMOS software.
- Represent cause-effect relationship – Using a single headed arrow to draw the cause-effect relationship between the unobserved and observed variables.
- Name the variable – Using object properties in a graphical window, the variables in the AMOS is named.
A model that is consistent in its theoretical understandings and parameter estimations using respective statistics is significant for research purposes. It falls on the researchers to rule out occurring model alternatives by efficiently testing and identifying fitting models. Hence, SEM using SPSS AMOS alleviates that and contributes to better research knowledge.
Purpose of using SPSS AMOS to obtain SEM results
SEM is conducted using AMOS specifically for the following advantages:
- The number of variables used and the variables used for the purpose of SEM analysis can be studied using the text output variable summary.
- The observed variables and unobserved variables are differentiated from the model.
- To ensure the normal distribution of the SEM model, different tests are used.
- These tests derived outputs with normality of data.
- Proper estimates of different effects that have on data is provided in output.
- The different tests performed on regression weight, direct, indirect, total effect affecting standardized loading of factor, residua, correlation and covariance to study effects and produce appropriate results.
- The statistics of the model are ensured to fit the desired results.
- Complications arising while drawing the model are prompted by error messages or failure to deliver the output.
Merits of SEM
SEM involves using confirmatory path analysis which comes to the rescue by avoiding possible mistakes. Here, factors are specified exactly in numbers and loading patterns.
Since SEM uses different analysis techniques, it facilitates examination of more than one at a time. It replaces EFA and Regression by considering potential measurement errors too.