An alternative approach to the hot deck is to generate imputations as draws from the distribution of the missing values based on a parametric model. It appears therefore from the simulations and assumptions that in terms of bias, coverage and empirical standard error that method 4 or 5 would be best in cases where method 6 is not feasible. tandfonline. Survey data typically contains categorical variables, which may be either nominal or ordered. The simplest hot deck procedureusing the entire sample of respondents as a single donor poolproduces consistent estimates only when data are missing completely at random (MCAR) (Rubin, 1976; Little Rubin, 2002).

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(2004) extend Srndals method to the hot deck, using the assumption that within adjustment cells (g = 1, , G) values of Y are independent and identically distributed with mean g and variance
g2. The hot deck is commonly used by other government statistics agencies and survey organizations to provide rectangular data sets for users. (2004), who extend the idea of neighbourhoods defined by predictive means to multivariate missingness. On inspection, in both cases, the learn the facts here now data is similar but not identical to the complete case data. 3, 33.

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If you are a registered author of this item, you may also want to check the “citations” tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. MI was conducted using the -ice- routine in Stata 10 [19, 24, 25]. 39,95 €Price includes VAT (Pakistan)Rent this article via DeepDyve. For subsequent country-level data sets the amount of missing data was in fact lower, 5–22 %, but M = 25 was maintained for consistency. We devised a simulation study to impartially assess the performance of the new method against some alternatives in a realistic setting—based on the case study. about his third National Health and Nutrition Examination Survey (NHANES III) is a large-scale survey that has previously been used to compare imputation methods, including parametric and non-parametric and single and multiple imputation methods (Ezzati-Rice et al.

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With the advent of MI routines in SPSS, R, SAS and Stata, MI is now readily accessible to analysts as a robust method for handling missing data, which can be applied in a number of contexts [4]. Our discover here highlights several issues with the hot deck that we feel deserve consideration. Methods 1 and 2 show an increase in bias compared to the base case, with method 4 showing significant downward bias and reduced coverage for TPB slope in both scenarios. mil. More incomplete observations with partial data: 18 % had all items missing for a scale; 25 % had one or two items missingSimulation results for the three scenarios.

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 4. The simulation was carried out using the software R with the MICE package for parametric imputation (Van Buuren Oudshoorn, 1999; R Development Core Team, 2007). 9 %). Seven procedures are compared in a simulation experiment when missing data are confined to one independent binary variable: complete case analysis, zero order regression, categorical zero order regression, pi imputation, single imputation, multiple imputation, modified first order regression. Empirical standard error also shows little variability between methods, except that it is lower for method 5 on slope for practitioner satisfaction. A complete MI prediction equation results in 134 predictors for each incomplete variable.

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One such procedure is the Bayesian Bootstrap (BB) (Rubin, 1981). Also, when the MCAR assumption does not hold, the data is no longer representative of the target population, compromising external generalisability [7]. A review of randomised controlled trials identified 77 articles from the latter half of 2013, of which 73 reported missing data.

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the various RePEc services. We now review the (somewhat limited) literature on theoretical and empirical properties of the hot deck. By assuming a model for the distribution of Y in the population, the variance of an estimator in the presence of missingness is decomposed into a sampling variance and an imputation variance.

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