I am performing Confirmatory factor analysis in python using the factor_analyzer module. I have searched hi and low for a way to generate the model diagnostics such as the Root Mean Square Error of Approximation, the chi square, the CFI and Tucker-Lewis index. I'm not particularly mathematically inclined and relatively new to python but I have been able to muddle through for the most part. I understand that the factor_analyzer module produces a lot of different objects that allow would, in-theory, allow me to carry out additional calculations and I have found this document which provides me with most of the formula I need. However, I do not know what to take (or calculate) to get the model diagnostics I need. The CFA code is model_dict = {"F1": factor_1, "F2": factor_2}# I have made these lists previously model_spec = ModelSpecificationParser.parse_model_specification_from_dict(df[influence_scale], model_dict) cfa = ConfirmatoryFactorAnalyzer(model_spec, disp=False) cfa.fit(df[influence_scale].values) cfa_loadings = pd.DataFrame(cfa.loadings_) I have gotten no errors and the code works fine giving me clean loadings as I would have expected on each factor, however I'm really stuck on getting the additional stats I need. If anyone can help me out I'd really really appreciate it. Continue reading...
