A Review of Hot Deck Imputation for Survey Non‐response

By Rebecca Andridge, Roderick J. A. Little. International statistical review = Revue internationale de statistique, 2010

1,113 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ed3fc9d20c76ead32209962be4d27e67530ceed0

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Citations in the corpus

Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data (2011, methodology): “…(2) imputations will not be outside the range of possible values (as might happen with multiple imputations, see He, 2010); and (3) it is not necessary to deﬁne an explicit model for the distribution of the missing val-ues” (p. 84; see also Andridge & Little, 2010; Roth, Switzer, & Switzer, 1999).”
Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales : estimating latent trait scores (2016, neutral): “A recent review of different techniques of hot-deck imputation can be found in Andridge and Little (2010).”
A survey on missing data in machine learning (2021, methodology): “The hot-deck method is very popular in all single imputation methods as it results in a rectangular data [47], that can be used by secondary data analysts.”
A Martingale Representation for Matching Estimators (2012, methodology): “Andridge and Little (2010) provided a recent survey on hot-deck imputation methods.”
The Consequences of Identity Theft Victimization: An Examination of Emotional and Physical Health Outcomes (2017, methodology): “Compared to alternative imputation methods, SRPI has been validated as an appropriate method for handling missing data, and is widely used in social science (Andridge & Little, 2010; Gmel, 2001).”
Review of soft sensor methods for regression applications (2016, background): “Another approach is the hot-deck imputation, where a missing value is imputed from a randomly selected value of the input for similar target values [14].”
Media Exposure to Collective Trauma, Mental Health, and Functioning: Does It Matter What You See? (2019, methodology): “Over two thirds of the sample had provided this information before the BMB (N = 3,351; 71.7%); for those who had not, missing values were imputed using Sequential Hot-deck Imputation (Andridge & Little, 2010; Cox, 1980).”
CORRELATES OF VIOLENT POLITICAL EXTREMISM IN THE UNITED STATES (2018, result): “…missing values on mental illness to values of “0,” the effective rate of mental illness in the data set drops to 8.4 percent, which is comparable to rates of mental illness in the general population. and simple imputation using fixed values (i.e., cold-deck imputation; Andridge and Little, 2010).”
Sensitivity to the Ferguson Effect: The role of managerial organizational justice (2016, methodology): “We employed multiple imputation using chained equations (MICE; 10 imputations) to handle missing data, which is available in Stata 14 (Andridge & Little, 2010; Fuller & Kim, 2005).”
Nearest neighbor imputation algorithms: a critical evaluation (2016, methodology): “Several methods exists for imputing missing data [2–5], among the most popular there are the so-called hot deck imputation methods, that in their deterministic form include the “nearest neighbour” (NN) imputation procedure [6].”
Racial Disparity in Federal Criminal Sentences (2014, methodology): “These variables come from the USSC’s data and are available only for those sentenced for nonpetty offenses.17 It was therefore necessary to impute the criminal history and education for the 3,716 arrestees for whom this information was not available ðapproximately 10 percent of the sampleÞ.18 Our pr”
Accounting for missing data in statistical analyses: multiple imputation is not always the answer (2019, methodology): “Several different methods can be used to impute missing values, including joint modelling imputation,(1,50) fully conditional specification imputation and hotdeck imputation.(44,54) As with any statistical analysis, the approach used should be decided a priori, with the reasons for the selected appr”
New Confidence Intervals and Bias Comparisons Show That Maximum Likelihood Can Beat Multiple Imputation in Small Samples (2013, methodology): “MI can also impute values nonparametrically, using a “hot-deck” procedure that replaces the missing values by sampling from the observed values (Andridge & Little, 2010).”