One thing that virtually everyone seems to believe about survey research is that a bigger sample is better: a sample of 500 respondents is better than 100, and 1000 is better still. And best of all is a census where, technically, the margin of error is reduced to zero.
However, the truth of the research adage about bigger sample sizes holds if, and only if, there is no bias. And the probability of zero bias is rare.
Researchers know this – but the media and the general public do not. The press and their audiences fixate on the number of respondents in research. To overcome this problem, we need to talk about bias.
When the Census 2016 online form was inaccessible for more than 48 hours from 7.30pm on census night (August 9), it created a media flurry. However, when the ABS reported that more than 96 per cent of households had completed the census by the closing date (September 23), many concluded that the problem was resolved.
However, the problem of bias remains, and this is not necessarily resolved with a high response rate. The real #censusfail is less a data collection glitch and more the threat it has posed to data quality.
This potential for biased results in the census has been barely discussed and appears to have been largely overlooked or ignored. One columnist at The Australian notes that it is a mystery why the government has not acknowledged the problem.
The data provided by the census are very important, which reinforces the need to identify potential problems including bias.
According to one news report, ‘the ABS is adamant the quality of data has not been compromised.’ But is this true – are the data unbiased?
Whether or not there is bias is vexingly uncertain. Bias is much more difficult to measure than sample size and sampling error, but it is much more important to try to do so.
There are perhaps two key measures that might be considered in assessing potential bias.
What proportion of invited respondents did not respond (which the ABS calls ‘undercount’)? And, more importantly, are those non-respondents different from those who did respond?
The media, the public and other research audiences need to be reminded of how wrong even a very large sample can be as illustrated by the infamous failure of The Literary Digest poll in predicting the winner of the Landon-Roosevelt presidential race in 1936.
While many remember this case study as an example of a sample selection bias (using readers of The Literary Digest and lists of automobile and telephone owners), empirical research to determine why the poll failed concluded that the incorrect prediction was due to the non-response bias.
The Literary Digest predicted a Landon win based on an enormous sample of 2.4 million respondents. However, a total of 10 million were invited to participate. If the 75 per cent non-response rate had been eliminated, the poll would have correctly predicted a Roosevelt win.
The danger of high non-response rates and non-response bias continues to undermine the accuracy and usefulness of much survey research today including more recent US Presidential polls.
While it appears that only four per cent have not responded to Census 2016, could the non-respondents differ from those who have responded?
Failing to get responses from a small, distinctly different segment can have a significant impact. This is why the ABS makes special efforts to capture potential non-responders such as those living rough (<1 per centof the population), on the road or out bush.
If we hypothesise that the four per cent of non-respondents to Census 2016 were those who tried to respond online, were frustrated in their efforts by the website outage, and subsequently refused to respond, we could guess that they would be more likely to be younger, live in major cities and have children under 15 years, based on ABS research about internet users.
What proportion of respondents mis-responded? Mis-responding (which the ABS describes as ‘respondent error’) can of course, be intentional, unintentional or both.
The unprecedented antipathy expressed towards Census 2016 in the media and in the Twittersphere seems to suggest a degree of uncooperativeness.
Even before the census collection began in earnest, privacy concerns led many to consider approaches to mis-responding. A number of politicians then publically indicated that they would not provide their name as required, thereby openly admitting their intention to mis-respond.
Incipient frustration was likely fanned by the outage of the website, suspicions about the reasons for the outage, and some fairly heavy-handed threats about substantial fines for not completing the census form.
Might respondents have intentionally falsified or fabricated responses? It seems unreasonable not to expect it!
Even if we could rely on the saintly nature of our respondents to remain unphased by the entire furore, could unintentional mis-responding have resulted through memory failure due to delayed responding?
One week after census night, only 50 per cent of households were reported as having completed the census. This means the remaining 46 per cent completed their census form in the following five weeks up to September 23. They therefore had to remember all the persons present on census night and to remember (if they ever knew) all the relevant details for each person: age, previous addresses, religion, race, occupation, income, education, etc.
Perhaps Australian householders retained all that was needed in memory and/or were very forgiving and remained helpful throughout the process of completing their form.
Or perhaps there is a strong likelihood of bias.
The Census 2016 reminds us that:
- All survey research is subject to bias; the census is no exception.
- Bigger samples may be better, but unbiased is best.
- Noisy estimates can be reduced with larger samples, but systematic bias is not so easily eliminated. We need to start talking about bias.
- We need to determine both the non-response rate and the mis-response rate – and establish whether those not responding or mis-responding are different from others.
(Published in Research News, November 2016, pp 6-7, Australian Market & Social Research Society, http://www.amsrs.com.au/searchbook?id=102#page=3).