#Spss code non response how to

Another attractive feature of the hot deck is that only plausible values can be imputed, since values come from observed responses in the donor pool. Having said this, it is important to keep in mind that the hot deck makes implicit assumptions through the choice of metric to match donors to recipients, and the variables included in this metric, so it is far from assumption free. The hot deck method does not rely on model fitting for the variable to be imputed, and thus is potentially less sensitive to model misspecification than an imputation method based on a parametric model, such as regression imputation. It avoids the issue of cross-user inconsistency that can occur when analysts use their own missing-data adjustments. As with all imputation methods, the result is a rectangular data set that can be used by secondary data analysts employing simple complete-data methods. There are several reasons for the popularity of the hot deck method among survey practitioners. We note that our use of вЂњdeterministicвЂќ describes the way in which a donor is selected in the hot deck, and differs from the use of вЂњdeterministicвЂќ to describe imputation methods that impute the mean or other non-random value. Other methods impute summaries of values for a set of donors, such as the mean, rather than individual values we do not consider these as hot deck methods, although they share some common features. In other versions a single donor is identified and values are imputed from that case, usually the вЂњnearest neighbourвЂќ based on some metric we call these methods deterministic hot deck methods, since there is no randomness involved in the selection of the donor. In some versions, the donor is selected randomly from a set of potential donors, which we call the donor pool we call these methods random hot deck methods. Hot deck imputation involves replacing missing values of one or more variables for a non-respondent (called the recipient) with observed values from a respondent (the donor) that is similar to the non-respondent with respect to characteristics observed by both cases. This paper describes various forms of the hot deck, reviews existing research on statistical properties, and highlights some areas for future work.

#Spss code non response how to

This method is used extensively in practice, but the theory behind the hot deck is not as well developed as that of other imputation methods, leaving researchers and analysts with limited guidance on how to apply the method. We consider here hot deck imputation, which involves replacing missing values with values from a вЂњsimilarвЂќ responding unit. Thus, the objective of imputation is not to get the best possible predictions of the missing values, but to replace them by plausible values in order to exploit the information in the recorded variables in the incomplete cases for inference about population parameters ( Little & Rubin, 2002). It is important to note at the outset that usually sample surveys are conducted with the goal of making inferences about population quantities such as means, correlations and regression coefficients, and the values of individual cases in the data set are not the main interest. A common technique for handling item non-response is imputation, whereby the missing values are filled in to create a complete data set that can then be analyzed with traditional analysis methods. Missing data are often a problem in large-scale surveys, arising when a sampled unit does not respond to the entire survey (unit non-response) or to a particular question (item non-response).

Spss code non-response