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| Jeehoon Kim posted on Saturday, February 26, 2011 - 5:14 pm
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Dear Professors:
I've once asked you if IRT might be used to measure 9 binary indicators as an endogenous mediating variable in my path model and got confirmed.I've tested a measurement only model and final structural model, and got those fit indices.
For IRT measurement only model, overall fit indices are not good except RMSEA (CFI:.865, TLI: .819, RMSEA: .049, WRMR: 1.602). For my final structural model, fit indices are acceptable although WRMR is still high (CFI:.972, TLI: .963, RMSEA: .035, WRMR: 1.302).
The reason for poor fit indices in measurement only model is those variables are services utilized by caregivers, so some services are quite related.
Because a measurement only model was not good fitted to the data, I'd like to know if I need to find alternative or if it is fine to use.
A summed score was range of 0 to 8, and skewness is 1.08 and kurtosis is 1.34. If I can't use IRT measure in my structural model, can I use this summed score as a continuous variable? Or can I use a transformed score by normal score transformation with Prelis? For both models, treating it as continuous variable and with a normal score transformed variable, model fit indices are almost identical like CFI: .987, TLI: .971, RMSEA: .047, and WRMR: .961.
Any advice would be greatly appreciated.
Regards,
Jeehoon |
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| Jeehoon Kim posted on Sunday, February 27, 2011 - 7:16 am
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| Or what if I can treat this summed score variable as a categorical variable? Thank you for your any advice or suggestion. |
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| I would not inlcude an ill-fitting IRT model as part of a larger model. I would instead include the needed residual covariances using the weighted least squares estimator. Summing the categorical variables ignores the fact that the model does not fit. |
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| Jeehoon Kim posted on Tuesday, March 01, 2011 - 1:18 pm
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Thank you for your suggestion. I, however, wonder if including residual covariates would violate local independence assumption with IRT measure.
I'm a beginner to use IRT, so I might be wrong, but want to make sure of it.
Also, if I will be fine to include residual covariates, and I would like to do multiple group analysis further, how can I handle with residual covariates then? Any advice will be greatly appreciated. Thanks again!
Jeehoon |
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Having correlated residuals violates the local independence assumption of IRT. But you have to make a distinction here - this violation is only a problem if your model does not include the correlated residuals. Including the correlated residuals, you are no longer assuming the standard IRT, so you are ok.
Multiple-group analysis can also handle correlated residuals. |
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| Jeehoon Kim posted on Tuesday, March 01, 2011 - 3:37 pm
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Thank you so much for clarifying this issue. I now can move it forward.
Many thanks,
Jeehoon |
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| Jeehoon Kim posted on Tuesday, March 01, 2011 - 4:43 pm
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Dear Bengt,
Could you let me know any paper have allowed residual covariates with IRT measure? Thanks.
Jeehoon |
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| None comes to mind off hand - you may want to ask on SEMNET. But bi-factor modeling can be seen as a version of this where a group of items correlate beyond what a single factor can explain. |
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| Jeehoon Kim posted on Tuesday, March 01, 2011 - 6:54 pm
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Thank you for the information. I will ask on SEMNET. I have one more question though.
For multiple group analysis for a path model with two latent variables, should I test measurement invariance with only measurement model separately or with my whole model? If it is latter, after checking measurement invariance, can I compare path coefficients with all free model and equality constraints model?
Again, thanks for your valuable advice.
Jeehoon |
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Those are questions with too long answers which are covered in our courses. Briefly:
You have to make that decision and it depends on the circumstances.
You can compare path coefficients when the measurement models have a sufficient degree of invariance. |
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