I have a relatively small dataset (n < 130) based on a questionnaire set up with 5-point likert type scale. Each respondent has filled out the questionnaire at 4 time points, i.e. a longitudinal (or panel) data study.

In papers and other theory that address Maximum Likelihood estimation in case of non-normal data, the authors usually recommend to use the robust ML estimator (MLM) (e.g. Brown, T.A. (2006) Confirmatory Factor Analysis for Applied Research, New York, NY: The Guilford Press).

In case of non-continuous data, e.g. ordinal data other estimators are recommended such as Diagonally Weighted Least Squares (DWLS).

Two questions:

1) Isn't it always a good idea to fit your models with both ML and MLM, even if you think the data is normally distributed? I was thinking of doing this myself and if the results differ a lot, ML is probably not a good approach.

2) In all these cases of whether or not the data is non-normal, non-continuous, etc. Do the authors refer to the outcome data type (i.e. the latent variable that you try to measure), the residuals or the manifest variables / indicators?

P.s. If your answer can be backed by a paper I would appreciate a reference to read the details.

EDIT I forgot to link a somewhat related forum topic: Exploratory Factor Analysis (EFA) followed by Confirmatory Factor Analysis (CFA)


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.