Ive heard SEM is just for non-experimental data, right?
• How should one handle missing data in SEM? Listwise deletion means a case with missing values is ignored in all calculations. Pairwise means it is ignored only for calculations involving that variable. However, the pairwise method can result in correlations or covariances which are outside the range of the possible (Kline, p. 76). This in turn can lead to covariance matrices which are singular (aka, non-positive definite), preventing such math operations as inverting the matrix, because division by zero will occur. This problem does not occur with listwise deletion. Given that SEM uses covariance matrices as input, listwise deletion is recommended (or some form of estimation of missing values, such as substituting mean values). • Can I use Likert scale and other ordinal data in SEM? For reasonably large samples, when the number of Likert categories is 4 or higher and skew and kurtosis are within normal limits, use of maximum likelihood estimation (the default in SEM) is justified. In
