Abstract Details
Activity Number:
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444
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract - #309621 |
Title:
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Imputing Ordinal Data with One Predominate Category
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Author(s):
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Darryl Creel*+
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Companies:
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Keywords:
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imputation ;
ordinal data
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Abstract:
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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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Authors who are presenting talks have a * after their name.
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