Agenda

1. Agenda • How Important Are Response Rates? • What Is Happening With Response Rates? • Measuring Response Rates • Does Any Of This Really Matter?

2. What is the Issue Regarding Response Rates? • Telephone survey response rates have been declining over the past few decades from a high of 60% in the early years. • Range of factors seen to contribute to declining rates: • Answering machines, voice mail, call blocking, caller ID, etc. • Refusals: time constraints, general cynicism, inconvenience, privacy and confidentiality concerns, etc. • Cell only households now becoming an issue – up to almost 10% in some areas of U.S. • Result of declining response rates? • High non-response = risk of lower quality data • Increased cost and time to reach target response rates • For some, “response rate” is seen as only measure of survey “quality”

3. Response Rate Not the Only Factor in Determining Survey Quality • Apart from Response Rates, There Are Many Other Factors Affecting Survey Quality • Sampling errors • Universe definition • Sample design • Sample source • Non-sampling errors • Data collection methods • Interviewers, coders, data processing • Respondent boredom • Analysis

4. How Important is “Response Rate”? • Higher response rates always desirable • But, response rates should be only one consideration when research design and budgetary issues are considered • Avoid effects of other sources of error • Looking at research objectives, allocate resources where maximum benefit achieved • In many commercial surveys, response rate not even an issue (primarily quota samples) • Low response rates need not always be cause for concern • Key issue: how survey respondents differ from non-respondents • Bias from non-response will only be an issue when responders differ from non-responders

5. What is Happening to Response Rates? • The PMRS Response Rate Committee measured refusal rates in 1995, 1999, 2002 and again in 2005. Up until 2002, refusal rates have increased and response rates have fallen. • When analyzed on an increment basis year by year, the 2002 survey suggested that for one-time studies, the rate of refusals was accelerating. (Refusal Rate = Refusals/Total Asked; Response Rate = Cooperative Contacts/Total Eligible Numbers) • Data for 2005 are not yet available so it is not clear whether this process has continued, although results I will present in a few minutes suggest average response rates may be in the 10% - 12% range in 2005/2006.

6. What is Happening to Response Rates? … cont’d • The longer the interview, the higher the refusal rate. 2002 data showed this impact very clearly.

7. Standardized Response Rate Calculation

8. Why a Standard Method of Measuring Response Rates? • MRIA has recently adopted a “Standard Method of Measuring Response Rates” as a result of a request from the Federal Government. • Literature reviews among a range of sources unearthed a myriad of “acceptable” definitions of Response Rate. The American Association for Public Opinion Research (AAPOR) alone publishes at least six different calculation methods that it deems to be acceptable under varying circumstances. • The goal for the Response Rate Committee became one of developing a response rate definition that would let research buyers compare levels of fieldwork effort and productivity across research suppliers. With this goal clearly in mind, the Committee endorsed a response rate calculation method that it considered to be the most appropriate for reporting call outcomes at the data collection stage of a telephone survey. • En route, the committee consulted with Statistics Canada and with members of AIRMS Quebec. Both groups endorsed the concept.

9. How Do We Measure Response Rates – MRIA Approved Definition Empirical Method of Response Rate Calculation

10. High or Low Response Rates - Does it really matter? Presented to MRIA Annual Conference June 2006 by Gary Halpenny and Don Ambrose on behalf of MRIA Response Rate Committee

11. High or Low Response Rate – Does It Really Matter? • Telephone surveys have been under attack recently on the grounds that “Results are no longer accurate nor representative” • Low response rates are cited as the reason • However, a growing body of research begs to differ • A number of investigative projects in the U.S. have shown: • For most commercial and public opinion applications a 30% response rate produces essentially the same results as a 50% response rate

12. High or Low Response Rate – Does It Really Matter? … cont’d • Some of the research literature: • In 1997, two identical surveys, one at 61% response rate and the other at 36%, produced no meaningful differences • This project was replicated in 2003 with 51% and 27% response rates and with similar results • Researchers concluded “carefully conducted polls with relatively low response rates still yield representative samples and accurate data” (Keeter el al, Pew Research)

13. High or Low Response Rate – Does It Really Matter? … cont’d • The reality today is that few commercial telephone surveys even approach the 30% level • The demand for faster turnaround means most telephone response rates are now in the 10% to 20% range • Quick 1 or 2-day polls can yield even lower rates

14. The Critical Issue! • Can response rates at these levels still produce accurate and meaningful data? • Clearly more research was needed

15. MRIA’s Research Project

16. The Plan • In 2005, the MRIA Response Rate Committee sponsored research to investigate whether response rates as low as 10% can still produce reliable and useful data. • Five Canadian research companies who regularly conduct national omnibus surveys volunteered to combine efforts.

17. Stage 1 • Using an identical 5-minute question set, each company completed approximately 250 interviews on a single wave of its Omnibus in January, 2006. • 1,238 completed interviews in total • 4 days in-field • 9% aggregate response rate

18. Stage 2 • Using the same 5-minute question set, each company completed a second sample of approximately 250 interviews over January/February 2006. • 1,273 completed interviews in total • 4-to-5 weeks in-field • First refusals recontacted • 31% aggregate response rate

19. Both Samples Were: • National RDD, age 18+ • Weighted to Census for: • Age • Gender • Province • Community size

20. Fieldwork Undertaken By: • Ipsos-Reid • Maritz • Opinion Search • Synovate • TNS-Canadian Facts

21. Record of Call Comparison • Table on next slide indicates that additional call attempts yield three main benefits: • Higher contact ratio (lower proportion of busy/no answer) • Completion/refusal ratio increases from .26 to .77 • Means that fewer good telephone numbers required to yield same number of interviews

22. U IS R

23. Key Findings

24. Both Studies Yield Identical Results for: • Incidence of food items used in past 6 months • List of items bought in last 12 months • Appliances in household • Print media readership – not title specific • Incidence of travel outside Canada • Personal access to the internet • Cell phone ownership and carrier used

25. Food Items Used in Past 6 Months • Results Identical * At 90% level of confidence

26. Items Bought in the Last 12 Months • Same result regardless of whether category incidence is high, medium or low

27. Appliances in Household • Similar findings for both commonplace and more esoteric items

28. Print Media Readership • Similar estimates of generic print media consumption

29. Traveled Outside Canada in Past 12 Months • Parallel results for both business and personal travel behaviour

30. Personal Access to The Internet • Penetration levels virtually identical

31. Cell Phones • No differences in either ownership incidence or carrier share

32. Credit Card Ownership and Usage • Difference are found here. • Higher response rate yields higher incidence of credit card ownership • Among card owners, high RR yields a higher incidence of owning American Express and a lower incidence of MasterCard • Posit that the higher RR captures a more upscale, harder-to-find group of people but not proven in the demos • Equally as likely to be a statistical anomaly • No differences in card used most often

33. Credit Cards Owned

34. Credit Cards Used Most Often • Claimed usage level unaffected by higher response rate.

35. 12 Attitudinal Statements Measured • Mean scores the same on 11 attributes out of 12 • Difference on the statement related to shopping was statistically significant but would not have changed the interpretation

36. Attitudinal Statements

37. Conclusions • Previous findings are corroborated – “carefully conducted polls with relatively low response rates still yield representative samples and accurate data” • Important that all other aspects of good survey design also must be present: • The set of telephone numbers is a randomly drawn, representative sample of the universe • Respondent selection at HH level is as random as possible • The data are weighted appropriately

38. Conclusions… cont’d • High response rates are still achievable for studies where this is an important design criterion • Fast field turnaround and high response rates are incompatible • Available time to complete the fieldwork is the main factor • More focus on the sample management process is required, e.g. call scheduling, elapsed time between attempts, etc.

39. Where Next? • Will repeat this test in January 2007. • Can the overall findings be replicated? • Are the few data differences found real or merely random data anomalies • Modify the question set somewhat • Replace the attitudinal questions with questions related to public policy

40. Online Research

41. Online Surveys • Fastest growing methodology in North America • Primarily opt-in panels, but also client lists and pop-ups • Is “Response Rate” a valid term within this environment? • None of the standard criteria for true random sampling hold (unless we are doing a random sample of internet panel members) • What then do we use as measures of field effort and data quality

42. Online Surveys … cont’d • Lots of activity around online standards and Response Rates • ISO standards in process of development • MRIA standards developed • Response Rate Committee working with internet providers looking at data quality and measures of “success rate” for online surveys: • A. Total invitations (broadcast or pop-ups) • B. Undeliverables (nil in pop-ups) • C. Net usable invitations (c = a – b) • D. Total completes • E. Qualified break-offs • F. Disqualified • G. Not responded • H. Quota filled • Contact Rate = (d + e + f + h)/c • Success Rate = (d + f + h)/C

43. Conclusions • Response Rates continue to be of concern, and efforts to at least maintain current levels of respondent cooperation are needed • However, a well-designed and managed survey with a lower response rate is unlikely to result in a different management decision than would have been made if the response rate had been higher • Cost, time and overall research objectives must all be part of the decision process