

ML estimation versus WLS estimation 

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Hi, I am testing a longitudinal crosslagged model with both ordinal (with four categories) and continuous variables. My sample size is very big (N > 10,000) and my variables are not normally distributed. Is it ok to use maximum likelihood estimation for such a model? thanks Giovanni 


The answer to that seemingly simple question has many layers of complexity. First, what do you mean by ML when you have ordinal and continuous variables? Do you mean treating all variables as continuous? Or do you mean specifying the ordinal ones as categorical? The latter can also be done using ML. Second, in the latter ML analysis the mediating ordinal variables (say time 2 vbles) are treated as continuous when they are used as predictors. WLSMV and Bayes can instead use their continuous latent response variables which might be a better alternative. See also our FAQ: Estimator choices with categorical outcomes 


Third, if the ordinal variables don't show strong floor or ceiling effects, you might want to claim that they are continuous  and use ML. 

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