Think intelligently about method (error) variance and about how to select measured variables to adequately represent a latent construct that interests you. Explain what a model is and what it means for a model to the data. Interpret “fit” common fit indices. Describe sound principles for model modification, when the fit of the initial model is less than adequate. Discuss issues of sample size in SEM, especially in regards to (a) stability of parameter estimates and (b) interpretation of fit indices. Compare pairs of nested models in SEM to determine whether one or more paths need to be included in the model. Discuss the problem of alternative models and what this means for interpreting SEM findings.
Think intelligently about method (error) variance and about how to select measured variables to adequately represent a latent construct that interests you. Explain what a model is and what it means for a model to “fit” the data. Interpret common fit indices. Describe sound principles for model modification, when the fit of the initial model is less than adequate. Discuss issues of sample size in SEM, especially in regards to (a) stability of parameter estimates and (b) interpretation of fit indices. Compare pairs of nested models in SEM to determine whether one or more paths need to be included in the model. Discuss the problem of alternative models and what this means for interpreting SEM findings.