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Activity Number: 444
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract - #309621
Title: Imputing Ordinal Data with One Predominate Category
Author(s): Darryl Creel*+
Companies:
Keywords: imputation ; ordinal data
Abstract:

When evaluating a linear modeling approach and a hot deck approach for imputing an ordinal variable with one predominate category, we noticed rather large differences in the distribution of the imputed values. For the linear model approach, we used IVEware. IVEware does not have a cumulative logistic regression model for modeling an ordinal outcome variable. Therefore, we had two choices: treat the ordinal outcome variable as a nominal categorical variable or as a continuous variable. We chose to treat the ordinal outcome variable as a continuous variable and rounded the results to an integer. We will conduct a Monte Carlo simulation to determine if there is a more appropriate imputation approach under these conditions. In addition to the two imputation approaches above, we will treat the ordinal variable as a nominal variable with IVEware and using a proportional odds model for an ordered categorical variable with MICE. We will use three evaluation criteria: bias, coverage, and confidence interval length.


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