ERIC Number: EJ1056802
Record Type: Journal
Publication Date: 2015
Abstractor: As Provided
A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test
Liang, Tie; Wells, Craig S.
Applied Measurement in Education, v28 n2 p115-129 2015
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three IRT models (three-and two-parameter logistic model, and generalized partial credit model) used in a mixed-format test. The statistical properties of the proposed fit statistic were examined and compared to S-X[superscript 2] and PARSCALE's G[superscript 2]. Overall, "RISE" (Root Integrated Square Error) outperformed the other two fit statistics under the studied conditions in that the Type I error rate was not inflated and the power was acceptable. A further advantage of the nonparametric approach is that it provides a convenient graphical inspection of the misfit.
Descriptors: Nonparametric Statistics, Goodness of Fit, Item Response Theory, Test Format, Comparative Analysis, Monte Carlo Methods
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A
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