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From single-race reporting to multiple-race reporting: using imputation methods to bridge the transition.
Nathaniel Schenker1, Jennifer D Parker
1Office of Research and Methodology, National Center for Health Statistics, Hyattsville, MD 20782, USA. nschenker@cdc.gov
Statistics in Medicine
|April 22, 2003
Summary
This study explores methods for assigning a single race category to individuals who report multiple races, crucial for analyzing trends over time. Advanced imputation methods using demographic data show promise for reducing bias and improving variance estimation in federal surveys.
Area of Science:
- Statistics
- Demography
- Public Health
Background:
- Revised 1997 standards allow multiple-race responses in federal data collection.
- Combining data across old and new standards requires single-race imputation.
- The National Health Interview Survey (NHIS) collects primary race alongside multiple-race responses.
Purpose of the Study:
- To explore methods for imputing single-race categories from multiple-race responses.
- To evaluate imputation methods for analyzing federal statistics with historical data.
- To assess the utility of demographic and contextual covariates in imputation models.
Main Methods:
- Exploratory analysis of NHIS data.
- Comparison of imputation methods, including those using covariate information versus simpler methods.
- Assessment of bias and variance estimation.
Main Results:
- Imputation methods using demographic and contextual covariates may offer lower bias and improved variance compared to simpler methods.
- These advanced methods can be beneficial for combining NHIS data collected under different race reporting standards.
- The relationships between primary race and covariates may change over time.
Conclusions:
- Demographic and contextual covariates can improve single-race imputation accuracy.
- Caution is advised when applying imputation models across different time periods due to potentially shifting covariate relationships.
- Further research is needed to refine imputation strategies for longitudinal race data analysis.