In my previous post, I have discussed What is Structural Equation Modeling (SEM)?. In this post Let’s see why would a researcher want to use SEM? SEM has a number of attractive advantages:
- Assumptions underlying the statistical analyses are clear and testable, giving the investigator full control and potentially furthering understanding of the analyses.
- Graphical interface software boosts creativity and facilitates rapid model debugging (a feature limited to selected SEM software packages).
- SEM programs provide overall tests of model fit and individual parameter estimate tests simultaneously.
- Regression coefficients, means, and variances may be compared simultaneously, even across multiple between-subjects groups.
- Measurement and confirmatory factor analysis models can be used to purge errors, making estimated relationships among latent variables less contaminated by measurement error.
- Ability to fit non-standard models, including flexible handling of longitudinal data, databases with autocorrelated error structures (time series analysis), and databases with non-normally distributed variables and incomplete data.
- This last feature of SEM is its most attractive quality. SEM provides a unifying framework under which numerous linear models may be fit using flexible, powerful software.
Let us know in the comment section below, why you prefer SEM?
