Given the advantages of SEM over OLS regression, when would one ever want to use OLS regression?
• Is SEM the same as MLE? Can SEM use other estimation methods than MLE? SEM is a family of methods for testing models. MLE (maximum likelihood estimation) is the default method of estimating structure (path) coefficients in SEM, but there are other methods, not all of which are offered by all model estimation packages: • OLS. Ordinary least squares (OLS). This is the common form of multiple regression, used in early, stand-alone path analysis programs. It makes estimates based on minimizing the sum of squared deviations of the linear estimates from the observed scores. However, even for path modeling of one-indicator variables, MLE is still preferred in SEM because MLE estimates are computed simultaneously for the model as a whole, whereas OLS estimates are computed separately in relation to each endogenous variable.OLS assumes similar underlying distributions but not multivariate normality, as does MLE, but ADF (see below) is even less restrictive and is a better choice when MLE’s mu
