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Who Is More Likely To Attend? A Study Of “No-Shows” In Qualitative Research

Who Is More Likely To Attend? A Study Of “No-Shows” In Qualitative Research. Benoit Allard Questionnaire Design Resource Centre Statistics Canada QUEST – April 2007. Overview. Background Why a “no-show” study? Methodology Results The future. Background.

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Who Is More Likely To Attend? A Study Of “No-Shows” In Qualitative Research

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  1. Who Is More Likely To Attend? A Study Of “No-Shows” In Qualitative Research Benoit Allard Questionnaire Design Resource Centre Statistics Canada QUEST – April 2007

  2. Overview • Background • Why a “no-show” study? • Methodology • Results • The future

  3. Background • Main questionnaire testing methods at QDRC: • Cognitive Interviews • Focus Groups • Recruitment mostly by private firms (for household survey testing) • Roughly 15% of recruits are “no-shows” • 1-2 per focus group on average, but • Attendance is variable

  4. Why a “no-show” study? • Questions: • Which recruiters do the best job? • Does payment influence turnout? • What factors influence attendance? • Improve turnout by: • Avoiding high-risk situations (combinations of factors having a high “no-show” rate) • Recruiting specifications • Choice of location/schedule • Over-recruiting

  5. Methodology • Quantitative study • Build a database of recruits • Data capture recruit lists (w/o identifiers) • Final list of individuals who accepted invitation • Since early 2005 • Frequency table analysis of “no-shows” • Ongoing

  6. Methodology (cont.) • Two levels of information • 1) About the testing activity • Topic of discussion • Type of testing (FG, 1-on-1) • Location (facility, respondent’s home) • Province • Date and time of testing • Payment amount • Recruiter

  7. Methodology (cont.) • Two levels of information (cont.) • 2) About the recruited person • Age • Sex • With/without spouse • Presence of children in the household • Work status • Income • Education • Attendance ← variable of interest

  8. Methodology (cont.) • Limitations • Blind to the recruiting process • Only household survey testing activities are covered • Little control over experimental design • Data generated by “production” work • But demographic variables are standardized

  9. The results • Which recruiters to hire? • Some recruiters do better than others • Results depend on region • Better turnout rates when using recruiters from within region • Hire multiple recruiters (each to work within their own region) rather than a single recruiter for the entire project • Renewal of standing offers for recruitment • Use results as part of evaluation

  10. The results (cont.) • Location of one-on-one interviews: • About 50% fewer no-shows if the interview is at the respondent’s home • Payment is typically lower • Interview at home rather than in a facility if conditions allow it • Beware of “intimidation” factor • Few observers allowed • Somewhat longer interview schedule • Travel cost are offset by facility savings

  11. The results (cont.) • Demographic factors • More likely to “no-show: • Young adults 18-25 years (24%) • Over-recruit • Cap their number in recruiting specs • Less likely to “no-show” • Seniors and retirees (<10%) • Minimal over-recruiting required • Family situation • Spouse, children not a factor (likely accounted for in the recruiting process)

  12. The results (cont.) • Time variables • Derived from captured date and time • Time of day, day of week, season • Few differences • Mondays (9%) • Morning (7%) vs. late afternoon (20%) • Not significant… yet

  13. The future • Investigate other variables / interactions • Case studies • Look at particular events with low turnout • 5-6 shows out of 10 recruits: bad luck or combination of unfavourable factors? • Keep gathering data • n is increasing, but… • Verify that effects are constant over time

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