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In the second part of the study, a Monte Carlo simulation study was conducted to evaluate the accuracy of the SEM approach in detecting item position effects.
The results from a simulation study are also presented to evaluate the accuracy of the SEM approach in detecting item position effects.
Methods of detecting item level bias have been developed in the area of education testing designed to avoid biases, such as, for different racial groups.
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Hit rates in detecting items with the linear position effect and Type I error rates in flagging items with no position effects were examined via simulated data sets.
Hit rates (i.e., correctly detecting items with a linear position effect) and Type I error rates (i.e., falsely flagging items with no position effects) were evaluated.
First, we describe the development and underlying assumptions of two Bayesian methods for detecting item-level mode effects.
A preliminary Monte Carlo simulation study was performed to assess the accuracy of the proposed method for detecting item-level mode effects.
The originally simulated P&P response data and simulated CAT item-response data were employed in the DIF analysis procedures described earlier (see "A Bayesian Procedure for Detecting Item-Level Mode Effects").
Two methods for detecting item-level mode effects are proposed using Bayesian estimation of posterior distributions of item parameters: (1) a modified robust Z (RZ) test, and (2) 95% credible intervals (CrI) for the CAT-P&P difference in item difficulty.
RMT provides a powerful framework to guide scale construction by detecting items deviating from model expectations with the intention of improving scale attributes.
Thus statistical methods (for example estimates of internal consistency, factor structure, information/discrimination of items) have been useful in detecting items subsequently found in the 'think aloud' study to be problematic for respondents.
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