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Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors

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Listed:
  • Celeste Combrinck
  • Vanessa Scherman
  • David Maree
  • Sarah Howie

Abstract

Missing Not at Random (MNAR) data present challenges for the social sciences, especially when combined with Missing Completely at Random (MCAR) data for dichotomous test items. Missing data on a Grade 8 Science test for one school out of seven could not be excluded as the MNAR data were required for tracking learning progression onto the next grade. Multiple imputation (MI) was identified as a solution, and the missingness patterns were modeled with IBM Amos applying recursive structural equation modeling (SEM) for 358 cases. Rasch person measures were utilized as predictors. The final imputations were done in SPSS with logistic regression MI. Diagnostic checks of the imputations showed that the structure of the data had been maintained, and that differences between MNAR and non-MNAR missing data had been accounted for in the imputation process.

Suggested Citation

  • Celeste Combrinck & Vanessa Scherman & David Maree & Sarah Howie, 2018. "Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors," SAGE Open, , vol. 8(1), pages 21582440187, February.
    • Handle: RePEc:sae:sagope:v:8:y:2018:i:1:p:2158244018757584
    DOI: 10.1177/2158244018757584
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    References listed on IDEAS

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