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Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling.

Seungwon Chung1, Li Cai2

  • 1a Graduate School of Education and Information Studies , University of California , Los Angeles , CA , USA.

Multivariate Behavioral Research
|April 6, 2019
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Summary
This summary is machine-generated.

Researchers developed a new statistical method for handling missing data in categorical structural equation modeling. This approach improves upon traditional multiple imputation techniques, offering better inference for social science research using questionnaire data.

Keywords:
Categorical variablesgoodness-of-fit testmultiple imputationstructural equation modeling

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Area of Science:

  • Social Sciences
  • Statistics
  • Psychometrics

Background:

  • Item responses from questionnaires are widely used in social science research.
  • Handling missing data, specifically item non-response, is a common challenge.
  • Multiple imputation is a prevalent technique for addressing missing data.

Purpose of the Study:

  • To propose an alternative statistical inference method for multiple imputation in categorical structural equation modeling.
  • To address limitations of traditional multiple imputation approaches in this context.

Main Methods:

  • Developed a novel statistical inference method inspired by Lee and Cai's work.
  • Applied the method to categorical structural equation modeling.
  • Conducted a simulation study to evaluate performance.

Main Results:

  • The proposed method demonstrated effective statistical inference in categorical structural equation modeling with missing data.
  • Simulation results indicated the performance of the new approach.

Conclusions:

  • The new method offers a viable alternative for statistical inference with multiple imputation in categorical SEM.
  • This research contributes to improved handling of missing data in social science research.