Evaluation of Model Fit in Structural Equation Models with Ordinal Missing Data: An Examination of the D2 Method

In many applied situations, the questionnaire items in measurement instruments do not approximate continuous, normally distributed variables but instead are ordinal. Properties of these instruments are often most accurately evaluated using structural equation models for ordinal data. However, most evaluations of instrument functioning need to overcome the problem of missing data. Multiple imputation is one approach to handling missing data, but no published article addresses the mechanism of pooling m tests of model fit across m imputed datasets for models with ordinal variables. This study conducts simulations to examine the feasibility of extending the D-2 procedure to combining model fit information across multiply imputed datasets with ordinal variables. Our results suggest that the D-2 procedure may be a reasonable procedure to use in this new context, so long as the analysis model also includes variables with little or no missing data that correlate with the incomplete ordinal variables.