Question

What are statistical assumptions that must be met to use SEM?

Answer 1

statistical assumptions of SEM

1. Multivariate normal distribution: The maximum likelihood method is used and assumed for multivariate normal distribution. Small changes in multivariate normality can lead to a large difference in the chi-square test.
2. Linearity: A linear relationship is assumed between endogenous and exogenous variables.
3. Outlier: Data should be free of outliers. Outliers affect the model significance.
4. Sequence: There should be a cause and effect relationship between endogenous and exogenous variables, and a cause has to occur before the event.
5. Non-spurious relationship: Observed covariance must be true.
6. Model identification: Equations must be greater than the estimated parameters or models should be over identified or exact identified. Under identified models are not considered.
7. Sample size: Most of the researchers prefer a 200 to 400 sample size with 10 to 15 indicators. As a rule of thumb, that is 10 to 20 times as many cases as variables.
8. Uncorrelated error terms: Error terms are assumed uncorrelated with other variable error terms.
9. Data: Interval data is used.