Structural Equation Modeling: 10 Worst Pitfalls and Mistakes

Not testing the measurement model.
Testing only one model
Thinking good fit is a strong model.
Violating linearity.
Treating ordinal data as interval in level.
Reporting results in spite of a warning that the covariance matrix is not positive definite or if convergence is not achieved.
Using SEM with small samples .
Using modification indexes atheoretically .
Using significance tests when you do not have a random sample. .
Not meeting the assumptions of multiple linear regression in ordinary SEM, or the assumptions of other types of regression in generalized SEM. .

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