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'''Hot-Deck Imputation''' replaces missing data with comparable data from the same set. "Hot-deck imputation is a means of imputing data, using the data from other observations in the sample at hand." <ref>http://analytics.ncsu.edu/sesug/1999/075.pdf</ref> For example, suppose census officials were unable to count the number of people in a given house and decided to fill in the missing data using hot-deck imputation. They would use the data from a similar house in the same area, and substitute the number of people in that house for the missing data.
'''Hot-Deck Imputation''' replaces missing data with comparable data from the same set. "Hot-deck imputation is a means of imputing data, using the data from other observations in the sample at hand." <ref>http://analytics.ncsu.edu/sesug/1999/075.pdf</ref> For example, suppose census officials were unable to count the number of people in a given house and decided to fill in the missing data using hot-deck imputation. They would use the data from a similar house in the same area, and substitute the number of people in that house for the missing data. Hot-deck imputation is one of the most widely used imputation methods. <ref>http://nces.ed.gov/StatProg/2002/appendixb3.asp</ref>
This paper discusses various types of imputation as well as their benefits and problems: http://nces.ed.gov/StatProg/2002/appendixb3.asp
This paper discusses various types of imputation as well as their benefits and problems: http://nces.ed.gov/StatProg/2002/appendixb3.asp
All methods of imputation are less-than-ideal. "Kalton and Kasprzyk, 1982...cautioned that imputation methods do not necessarily lead to a reduction in bias, relative to the incomplete data set. And, they warned against the danger of analysts treating the "complete" cases as actual responses, thus overstating the precision of the survey estimates." <ref>http://nces.ed.gov/StatProg/2002/appendixb3.asp</ref>
All methods of imputation are less-than-ideal. "Kalton and Kasprzyk, 1982...cautioned that imputation methods do not necessarily lead to a reduction in bias, relative to the incomplete data set. And, they warned against the danger of analysts treating the "complete" cases as actual responses, thus overstating the precision of the survey estimates." <ref>http://nces.ed.gov/StatProg/2002/appendixb3.asp</ref>
"One problem occurs with [hot-deck imputation] when several records with missing values occur together on the file. This results in the current donor value being assigned to multiple records, thus leading to a lack of precision in the survey estimates (Kalton and Kasprzyk, 1986)."<ref>http://nces.ed.gov/StatProg/2002/appendixb3.asp</ref> The method by which the donor value is selected could easily skew results, particularly in areas where there are typically many blank values, such as densely populated cities. If the population of these areas (which tend to be democratic) were exaggerated, redistricting could assign more Representatives to their state. Ultimately, this would result in more liberal politicians in Congress.
Finally, the results of hot-deck imputations must be treated carefully. These data sets appear to be complete, but in reality, they are not. "Regardless of the specifics, all hot-deck procedures take imputed values from a respondent in the same data file, thus yielding imputations that are valid, although not necessarily internally consistent for the respondent values. In order to evaluate the hot-deck imputation used for any specific data collection, detailed information is required." <ref>http://nces.ed.gov/StatProg/2002/appendixb3.asp</ref>
==Imputation in the United States Census==
==Imputation in the United States Census==