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.