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Activity Number:
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500
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #306864 |
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Title:
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Creating Imputation Classes Using Nonparametric Classification Trees
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Author(s):
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Darryl Creel*+ and Stephen Black and Karol Krotki and Jeremy Porter
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Companies:
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RTI International and RTI International and RTI International and RTI International
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Address:
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312 Trotter Farm Drive, Rockville, MD, 20850,
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Keywords:
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nonresponse ; imputation ; CHAID ; CART
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Abstract:
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Virtually all surveys encounter some level of item nonresponse. To address this potential source of bias, practitioners often use imputation to replace missing values with valid values through some form of stochastic modeling. In order to improve the reliabilities of such models, imputation classes are formed to produce homogenous groups of respondents, where homogeneity is measured with respect to the item that is being imputed. A common method used to form imputation classes is CHAID where the splitting rule is based on Chi-squared tests. This paper examines an alternative methodology used to form imputation classes, nonparametric classification trees where the splitting rules are based on the Gini index of impurity. In addition to a brief description of the nonparametric classification tree methodology, comparative examples are provided.
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