Statistics Networking Day

Question design

Jacki Schirmer

Senior Research FellowHealth Research Institute & Institute for Applied Ecology

jacki.schirmer@canberra.edu.au

Designing survey questionsWhether 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

Valid Reliable

UnbiasedAccurate

Ideal survey question design process1. Conceptualise

a. Clearly define end uses of datab. Design topics that will achieve objectives

2. ‘Straw man’ drafta. Draft initial measures – but expect them to change

3. Qualitative testinga. Focus group with survey usersb. 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 reviewa. Get a statistician or survey expert to review questions

Advise on response options, direction of question

5. Pilot testa. Sample of participants complete survey under exact conditions in which it will be deliveredb. Analyse results from pilot sample to identify problem areasc. Revise

6. Deliver your survey

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

Statistics Networking Day

Reweighting

Jacki Schirmer

Senior Research FellowHealth Research Institute & Institute for Applied Ecology

jacki.schirmer@canberra.edu.au

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

Responses by region – 2014 Regional Wellbeing Survey

State Total no. survey participants

NSW 3,403

VIC 5,289

QLD 728

SA 1224

WA 791

TAS 564

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?

How do you tell if your sample is representative?

• Explore for bias

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, 20111

Regional Wellbeing Survey, 2014

State NSW & ACTVICQLDSAWA & NTTAS

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 FemaleMale

50.7%49.3%

57.9%42.1%

Age 18-3940-5455-6465+

33.2%28.0%17.3%21.5%

13.7%29.8%28.6%27.9%

Working as a farmer FarmerNon-farmer

4.5%95.5%

30.6%69.4%

1Data 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

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

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

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$/s427944/Windows%20Profile/Desktop/Creating%20Weights%20to%20Improve%20Estimates.pdf

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