Institute of Natural and Mathematical Sciences

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    Dealing with missing data
    (Massey University, 2002) Scheffer, Judi
    What is done with missing data? Does the missingness mechanism matter? Is it a good idea to just use the default options in the major statistical packages? Even some highly trained statisticians do this, so can the non-statistician analysing their own data cope with some of the better techniques for handling missing data? This paper shows how the mean and standard deviation are affected by different methods of imputation, given different missingness mechanisms. Better options than the standard default options are available in the major statistical software, offering the chance to 'do the right thing' to the statistical and non-statistical community alike.
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    Issues in data collection: missing data and the 2001 New Zealand census
    (Massey University, 2001) Scheffer, Judi
    Missing data plagues all surveys, and to a degree the New Zealand Census suffers from the same malaise. While it is not a high level of missingness, it is present. If not correctly dealt with; just deleting cases with missing data will lead to biased conclusions, particularly if the missingness mechanism is NMAR. Some missing data may be inevitable; sometimes a respondent may be incapable of answering a question. This is usually MAR. If however the respondent refuses to answer a question because of say having a high income, then the results of the income question will be biased. Over time there have been a growing number of people employing avoidance tactics so as not to be classified as a refusal, but to make enumeration just too difficult. Anecdotal evidence among enumerators shows that this accounts for about 5% of respondents.