Issues in data collection: missing data and the 2001 New Zealand census

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

Postgraduate study: the hidden cost of writing a thesis

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

SLIP: 10 years ago and 10 years from now

Founded in 1999, the ACM SLIP Workshop is now in its 12th year. The 2010 SLIP Panel session will highlight perspectives from three individuals who have had great influence on the course of SLIP, and provide an opportunity for lively discussion by workshop attendees of prospects for the next 10 years of SLIP. This panel summary records preliminary thoughts of the panelists on two starting questions

Data mining in the survey setting: why do children go off the rails?

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