Browsing by Author "Scheffer, Judi"

Now showing 1 - 4 of 4
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    Data mining in the survey setting: why do children go off the rails?
    (Massey University, 2002) Scheffer, Judi
    Data Mining is relatively new in the field of statistics, although widely used elsewhere. Is it a good idea to discard the model-based methods in favour of Data Driven methods? Data driven methods produce a high degree of accuracy, but very little interpretability. Model based methods are interpretable, but lack accuracy. Data mining techniques are commonly used where the data collection has been automated. I will show these methods are also useful in the large survey setting.
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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.
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    Postgraduate study: the hidden cost of writing a thesis
    (Massey University, 2002) Scheffer, Judi
    Not too many years ago the attitude that: "Women are no good at mathematics, as it is a logical subject which requires a rational mind. As women are irrational, therefore they will not succeed at mathematics or understand it, and therefore should not be encouraged to study it." (McKenzie, 2001) prevailed. Many of these attitudes are fading, but still unseen hurdles exist.