Can I use AIC to compare non nested models?
The AIC does not require nested models. One of the neat things about the AIC is that you can compare very different models. However, make sure the likelihoods are computed on the same data. Beware of AIC values computed using conditional likelihoods because the conditioning may be different for different models.
Can AIC and BIC be used to compare nested models?
In SEM context, you can use AIC and BIC to compare non-nested models. However, note that there is some disagreement in the literature over the use of AIC for comparing non-nested models, as the original theory by Akaike was worked out for nested models only.
Is Chi-square a model?
A chi-square (χ2) statistic is a test that measures how a model compares to actual observed data. The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample.
How to compare models on the basis of AIC?
One does not compare the absolute values of two AICs (which can be like ∼ 100 but also ∼ 1000000), but considers their difference : Δ i = A I C i − A I C m i n, where A I C i is the AIC of the i -th model, and A I C m i n is the lowest AIC one obtains among the set of models examined (i.e., the prefered model).
When do CFA models need to be nested?
Models are nested if you can move from one model to the second model by adding additional constraints. CFA models are only nested if two factors become one factor by constraining factor correlations to 1 (and their covariances with other factors to be equal). But then perhaps you shouldn’t treat them as nested.
When to use lavaan to compare CFA models?
When comparing those two, lavaan returns an error. Error in lav_test_diff_af_h1 (m1 = m1, m0 = m0) : lavaan ERROR: unconstrained parameter set is not the same in m0 and m1. (Please note: If I use normal Maximum Likelihood I can compare the models.)
Is there difference between DWLS and CFA models?
I’ve noticed that there is a difference in the outputs. With Maximum Likelihood estimation there are AIC and BIC values in the outputs, which are missing in the DWLS-outputs. These seem to be relevant for the comparison since the output of the comparison of the Maximum-Likelihood models contains those values.