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Modified Item-Fit Indices for Dichotomous IRT Models with Missing Data.

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Summary

New item fit indices (M-X and M-G) effectively handle missing data in scale development. These modified statistics ensure accurate item analysis even with incomplete response datasets, improving scale reliability.

Keywords:
Chi-square-based item fit indicesitem fitmissing datamultiple imputationsingle imputation

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Scale development

Background:

  • Item-level fit analysis is crucial for scale development, guiding item revision and deletion.
  • Existing chi-square-based item fit indices (S-X, S-G) cannot directly handle incomplete response data.
  • Missing data is a common issue in data collection, potentially compromising scale analysis.

Purpose of the Study:

  • To propose modified item fit indices (M-X, M-G) capable of handling incomplete response data.
  • To evaluate the performance of these new indices using imputation methods.
  • To compare the proposed indices against existing methods under various conditions.

Main Methods:

  • Developed modified versions of S-X and S-G indices, denoted M-X and M-G, utilizing imputed total scores.
  • Employed single and multiple imputation methods (two-way, corrected item-mean substitution, response function imputation).
  • Conducted simulation studies manipulating test length, misfit sources, misfit proportion, and missing proportion.

Main Results:

  • The proposed M-X and M-G indices perform comparably to S-X and S-G with complete data.
  • Simulation results demonstrated the effectiveness of the new indices in handling incomplete data.
  • Performance varied based on manipulated factors, with specific indices recommended for different conditions.

Conclusions:

  • Modified item fit indices (M-X, M-G) provide a viable solution for analyzing scales with missing response data.
  • The choice of imputation method and specific index depends on the characteristics of the data and the research context.
  • These advancements enhance the robustness of item-level fit analysis in psychometric scale development.