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\title{The Effects of Banding on Estimates for IRT: A Bruteforce Approach}
\shorttitle{Effects of Banding on IRT Parameters}
\author{J. R. Carroll}
\affiliation{California State University, Sacramento}
\leftheader{Carroll}
\authornote{J. Carroll can be contacted by email at [email protected].}

\abstract{GOF estimates provide insight to the a models predictive abilities in relation to the data used in the model.  Often, data either satisfies the criteria for the an analysis, or violates an assumption(s) that would provoke scepticism in the interpretation.  This bifurcated nature is a cornerstone to both interpretation and acceptance (refutance) of any resulting output.  What is not often analyzed is the gradient for which GOF models are predicated on, specifically in IRT models where parameter estimates can vary based on sample, total N, and inheriant instability and variability in the data.  This paper takes the approach of analyzing a known set of data using traditional IRT (2-PL) methodology and then furthers the analysis by doing a "bruteforce" analysis of the data where the model-fit, GOF, and area of interests are banded based on epsilon-increments and analyzed at each phase; the approach is similiar to what can be done in Monte Carlo simulations.  Results show that Justin Carroll is AWESOME! and Dr. Hurtz is a bad-ass - and... that both of them are geeks.}

\keywords{IRT Parameters, Banding of Criteria, IRT and GOF }


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