I have run an IRT model using binary indicator variables using the WLSMV estimator and theta parameterization. The output gives me the difficulty and discrimination parameters. I'm now running a graded response IRT model using 3 level categorical indicator variables using the MLR estimator and I wanted to now if similarly the difficulty and discrimination parameters can be obtained? Many thanks
Thanks for the reply. I've had a look at the paper and I'm not sure I follow how I can use Model constraint to get the difficulty and discrimination parameters? If it helps here is some of the syntax for my graded response IRT model:
Analysis: estimator = MLR;
Model: Dep by q18_a*1 q18_c q18_d q18_e q18_f q18_g q18_i q18_j q18_l q18_m q18_n q18_o q18_p ;
I actually don't think you need to translate results but stay with the Mplus parameterization for the graded response model. It seems to be the parameterization used in the IRT literature. That is, with ordinal response as opposed to binary response, they seem to switch to the Mplus factor analytic parameterization. See for example section 22.214.171.124 in the book
Reckase (2011). Multidim. IRT. Springer.
as well as eqn (6) of the Psych Method article
Cai et al (2011). Generalized full-info....
Reckase gives a discussion of interpretations. In my view, the fact that IRT makes a parameterization switch when going from binary to ordinal speaks to using the factor analytic parameterization all the time.
This portion of the model (f1 BY U1@1 U2-U20*;) is the 2PL IRT model.
I am wondering what the appropriate conversion formula would be to get from the MPlus parameters to the IRT parameters. Is it simply the factor loading*sqrt(f1 var) or is it more complicated because of the second factor?
You use the regular formula you point to when an item loads on only one factor. It doesn't matter that you have other items loading on other factors.
For multiple factors, see a new FAQ to be posted tomorrow.
Alvin posted on Thursday, August 14, 2014 - 10:20 pm
Hi Dr Muthen,
I ran a two-parameter IRT model - with four factors. Given the number of integration points, it seems to take a long time for estimation ... My question is, in addition to using monte carlo integration (is this recommended?), are there alternative estimator methods (e.g. Bayesian) that may reduce computational time? Or does it make sense to run separate models for each component of the measure? but I am interested in the item response properties as well as the structure of this psychiatric construct. Also, the other question, in IRT models, is factor variance always fixed to 1, as demonstrated in mplus users guide?