Structural Equation Modelling (SEM) does not require specific type of data with compared to traditional analysis.
· SEM allows for the assessment of the relationships specified in the hypotheses and the SEM is used to validate the theoretically driven model and it is ideal when testing theories that include latent variables.
· The SEM consists of the measurement model and the structural model. Specifically, the path coefficients are examined with attention to the strength, direction, and significance of the relationships.
· CFA should meet three assumptions: 1) latent and observable variables are measured as deviations from their means; 2) the figure of observable variables in the indicators was bigger than the number of unobservable variables; and 3) and the common and also unique factors were not correlated.
· When you have sample, the sample size justification could be based on several perspectives to calculate the ideal sample size for an SEM model.
· For instance Rule of Thumb which requires the multiplication of the number of unknown parameters by 5-20, or Less Optimal and Minimum Sample Size Approach assumes that the sample size for SEM should be in the range of 100-200, as 100 is the less optimal sample size, while some other propose 200 is satisfactory number for the minimum sample size for the SEM. This number might be increased according to complexity of the model.
Please check the following dissertation and manuscript that I co-authored to see an applied SEM technique for the data: