Problems with sample data (MR, unit 9)

Potential problems:

• Bias - some households may have poor chance of being selected in sampling frame is out of date, individuals decline to respond, bias in questionnaire or interview
  
• Insufficient data - sample too small to be reflect whole population
  
• Unrepresentative data - data might be collected in abnormal conditions
  
• Omission of important factor - important questions omitted in design of questions
  
• Carelessness
  
• Confusion of cause & effect - wary of assuming associated variables mean one causes the other
• Interpretation - true of depth interviews with lengthy replies

Solving problems (ANN):

• Accuracy - insert control questions into questionnaire. Reply to one should be compatible with another. If not, the value of responses could be dubious or interviewee may be confused.
• Non-sampling error (results from way observations made) - poorly worded questions, unsuitable people surveyed, purpose of study may affect responses, personal bias of interviewer, non-response
• Non-response - units outside population (demolished houses), unsuitable for interview, movers, refusals, away from home

• common in random sample surveys
• rising crime and data fatigue reduces response rates (sugging & frugging - selling/fundraising under guise of research)
• can be avoided (except in mail surveys):
• DO NOT JUST APPROACH NEIGHBOUR, affects sampling framework
• People who've moved. Select individual from new household with rigorous procedure.
• Minimise refusals by keeping brief as possible using skilled interviewers and financial incentives
• Contact people away from home at later date. Plan call times sensibly as most out at work during day.