Why Structural Equation Modeling (SEM)?

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?

A. Sulthan

Author and Assistant professor in finance

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