1. Statistics Networking Day Question design Jacki Schirmer Senior Research Fellow Health Research Institute & Institute for Applied Ecology jacki.schirmer@canberra.edu.au

2. Designing survey questions Whether you are using existing measures, adapting existing measures, or designing new measures, you need to ensure your questions produce valid, reliable, accurate data Questions do not ‘lead’ people to a specific answer Result is close to real life, generates statistically useful data Produces similar results under consistent conditions Measures what it is intended to measure ValidReliable UnbiasedAccurate

3. Ideal survey question design process 1.Conceptualise a.Clearly define end uses of data b.Design topics that will achieve objectives 2.‘Straw man’ draft a.Draft initial measures – but expect them to change 3.Qualitative testing a.Focus group with survey users b.They attempt the survey and then discuss  What was confusing, hard, offensive, unclear  Where does wording need to change c.You revise the questions 4.Expert review a.Get a statistician or survey expert to review questions  Advise on response options, direction of question 5.Pilot test a.Sample of participants complete survey under exact conditions in which it will be delivered b.Analyse results from pilot sample to identify problem areas c.Revise 6.Deliver your survey

4. Key considerations (common pitfalls) What language/terminology does your target survey population use? What response option formats are best? Literacy and numeracy level of target survey population Eyesight, deafness prevalence

5. Statistics Networking Day Reweighting Jacki Schirmer Senior Research Fellow Health Research Institute & Institute for Applied Ecology jacki.schirmer@canberra.edu.au

6. Why reweight? Your sampling technique was not based on a specific sample frame – Online surveys promoted via social media – Self-select surveys Your sample is not representative of the population you sampled – Due to deliberate over- sampling of some groups – Due to bias in who responded to your survey

7. Responses by region – 2014 Regional Wellbeing Survey StateTotal no. survey participants NSW3,403 VIC5,289 QLD728 SA1224 WA791 TAS564

8. How should you analyse representativeness? Common approach: analyse demographics – Gender – Age – Education – Occupation Assume that if you have similar distribution to population, survey is OK But bias is often related to salience, not demographics – Are you biased to people with a greater interest in your topic (who may have different views to those with less interest) – Is there a general dataset you can compare to which asks questions that let you assess this?

9. How do you tell if your sample is representative? Explore for bias

10. Comparison of survey response to population benchmarks All surveys have biased samples Ours is no exception In 2014, we deliberately oversampled farmers, Victorians We also have a bias to older, female respondents (common across many surveys) Our large sample enables us to weight data so we can make statements about the population as a whole Characteristic Rural and regional Australia, 2011 1 Regional Wellbeing Survey, 2014 State NSW & ACT VIC QLD SA WA & NT TAS 35.7% 17.4% 23.5% 8.1% 11.3% 4.1% 28.4% 43.7% 6.0% 10.1% 7.0% 4.7% Gender Female Male 50.7% 49.3% 57.9% 42.1% Age 18-39 40-54 55-64 65+ 33.2% 28.0% 17.3% 21.5% 13.7% 29.8% 28.6% 27.9% Working as a farmer Farmer Non-farmer 4.5% 95.5% 30.6% 69.4% 1 Data source: Australian Bureau of Statistics Census of Population and Housing 2011. Data accessed via TableBuilderPro. Data were calculated for rural and regional Australia and exclude people living in cities with >100,000 population. ABS GregWT procedure used to weight data, using Census data as benchmarks. This ensures we use national best practice in analysis to correct for biases in our sample

11. If it’s not representative, how do you weight? Simple multiplicative approach – I have 15% young people in my survey compared to 30% in the population. Every young person will be assigned a weight of ‘2’ to ensure they are representative – OK if weighting by simple demographics and not many of them – Problematic when you weight by multiple attributes, as requires all cells to be populated, and simple approach does not represent interactions well Regression and other statistical modelling approaches – Use regression modelling to assign weighting to each respondent – E.g. GREGWT used by ABS – See Tanton et al. (2014) discussion http://microsimulation.org/IJM/V7_1/4_IJM_7_1_Tanton_Williamson _Harding.pdf http://microsimulation.org/IJM/V7_1/4_IJM_7_1_Tanton_Williamson _Harding.pdf

12. Challenges Cell sizes – when they become small, there is greater potential to ‘weight the bias’ and get an extreme result Number of benchmark variables used

13. Scale (population) weights vs proportional weights Scale weights scale to population size Proportional weights preserve your n Following examples are from file://stafffiles.win.canberra.edu.au/Homes$/s 427944/Windows%20Profile/Desktop/Creatin g%20Weights%20to%20Improve%20Estimates.pdf file://stafffiles.win.canberra.edu.au/Homes$/s 427944/Windows%20Profile/Desktop/Creatin g%20Weights%20to%20Improve%20Estimates.pdf

14. Problems with weighting Problematic to calculate standard error, confidence intervals for weighted data Other statistical tests may be non-robust Some programs assign population n, rather than sample n, to weighted data – with resulting problems with statistical analysis (SPSS in particular) Weighting does not correct all types of survey error Weighting is only as good as your benchmarks – pick the wrong benchmarks and it has limited utility Non-response effects – weighting does not correct for these