NCEA Level 3Surveys and biasAS 91584

Surveys and bias: sampling frames, non-response and wording

What this lesson covers

  • A sample can only speak for the population its sampling frame reaches.
  • Good design reduces bias; it never eliminates it.
Key idea

Errors a margin of error misses

  • Who can be reached, who answers, how it is asked.
  • A bigger sample fixes none of them.
  • Design reduces them; it never eliminates.
Worked example

Target population and sampling frame

  • Target: all adult residents.
  • Frame: the list actually sampled.
  • A landline list misses many people.
Rates survey
Targetall adult residents
Framelandline directory
Missedno landline
Oftenyounger, renting
Key idea

Why a mismatch matters

  • A sample speaks only for its frame.
  • Missed people may think differently.
  • Name the mismatch and explain the limit.
Worked example

Survey methods: the trade-offs

  • Phone, online, face to face, mail.
  • Each has an upside and a cost.
MethodPlusMinus
Phonea real person asksmany do not answer
Onlinecheap and quickmisses offline people
Face to facehigh responseslow and costly
Mailtime to thinkfew send it back
Key idea

In context, not a learned list

  • Tie each point to the report.
  • Online could miss older rural residents.
  • They may feel most strongly about rates.
Key idea

Non-response

  • Selected people who do not take part.
  • Those who reply may differ.
  • Follow-ups reduce this bias.
Often
Who repliesstrong views, keen
Who does notbusy, less interested
Reduce itreminders, callbacks
Key idea

Incentives: what and why

  • More replies: less non-response.
  • But it can change who replies, and how.
  • Give the effect and the reason.
Effect
Incentive$50 voucher draw
Upsidemore people reply
Riskprize seekers reply
Riskanswers to please
Worked example

Question wording

  • Leading and loaded words push answers.
  • One question, one idea.
  • Neutral: do you support or oppose?
ProblemExample
Leading"Surely you agree..."
Loaded"wasteful spending"
Double-barrelledrates and roads?
Neutralsupport or oppose?
Key idea

Ways to choose a sample

  • Random methods reduce selection bias.
  • Volunteers are the most likely to be biased.
MethodHow
Simple randomall equally likely
Stratifiedrandom within groups
Clusterrandom whole groups
Systematicevery 10th on a list
Self-selectedvolunteers: biased
Worked example

Stratified by area

  • Split the region into areas.
  • Sample each in proportion.
  • At random within each area.
AreaShareSample
City50%500
Towns30%300
Rural20%200
Total100%1000
Worked example

The 2025 error: "eliminates bias"

  • Bias is reduced or minimised.
  • A bigger sample does nothing for bias.
  • Never write eliminates.
AvoidWrite
Random sampleeliminates biasreduces bias
Stratifyingmakes it perfectmore representative
Bigger sampleremoves biassmaller MOE only
Key idea

Where the marks are

  • Achieved: name a source of bias.
  • Merit: its effect in this survey.
  • Excellence: what and why, precisely.
Summary

What to take away

  • Check who could be reached, who replied, and how they were asked.
Framewho can be reached
Mismatchlimits generalising
Non-responseresponders may differ
Wordingneutral, one idea
Stratifyrandom within groups
Biasreduced, not eliminated
Your turn

Your turn: four questions

  • Pause here and answer each one.
  • Say what the effect is, and why.
  • Tie it to the survey.
SurveyFind
1online poll of a townwho's missed
2cycle lane questionfix wording
3café prize drawits effect
460% city, 40% ruralsplit 500
Show answer

1. People rarely online are outside the frame; if their views differ, the estimate is biased. 2. Leading and loaded; ask "Do you support or oppose the cycle lanes?" 3. More customers reply, reducing non-response, but it may attract regulars or prize seekers, or answers meant to please. 4. Stratify: 300 city and 200 rural, chosen at random within each area.

Worked example

The answers

  • People rarely online: outside the frame.
  • Do you support or oppose the cycle lanes?
  • More replies, but who replies changes.
  • 300 city, 200 rural, random within each.

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