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In this paper, we provide a description of the Italian Treasury Econometric Model (ITEM).
In the model, item 1 is tested for linear position effects and interaction effects.
In addition to the specific item fit statistics of the Rasch model, item discriminatory power from classical test theory was used to estimate the fit of the items to the total score of FK.
We choose the time decaying parameter β in order to maximize the agreement between the real and model item degree distribution (see Figure 2 for the result with (beta=0.025) and (lambda=1.5)).
To compute the magnetic field in a 3-D conductivity model (item 1 of the above list), we use a 3-D contracting integral equation solver, extensively described in Kuvshinov and Semenov (2012).
For applied researchers, it might be of particular interest to see the comparative performance between the complex and simple models for DIF detection using the mainstream software, which can model item and person clustering effects simultaneously.
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CDM characteristics share several features with traditional item-response theory models (e.g., latent trait model, probabilistic model, item-response-patterns), as well as with confirmatory factor analysis (CFA: e.g., complex loading structure, a priori defined skill to task assignments as depicted within the Q matrix).
With this model, items end up selling substantially below their market value.
Only some structure models can model items with more than two response options, commonly referred to as polytomous items.
In terms of the Rasch Model, items being never or always solved (i.e. s i =0 and s i =n) are infinitely difficult or easy, respectively.
The initial measurement model items (50 items) were distilled from existing empirical studies, and then evaluated by groups of experts which resulted in decreasing the number of items to 40 items.
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