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1 Whither Survey Response Rates Do They Still Matter? Presented to: MRIA Ottawa Chapter October 26, 2006 by: Patricia Simmie, PhD Chair, MRIA Response.

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Presentation on theme: "1 Whither Survey Response Rates Do They Still Matter? Presented to: MRIA Ottawa Chapter October 26, 2006 by: Patricia Simmie, PhD Chair, MRIA Response."— Presentation transcript:

1 1 Whither Survey Response Rates Do They Still Matter? Presented to: MRIA Ottawa Chapter October 26, 2006 by: Patricia Simmie, PhD Chair, MRIA Response Rate committee

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

3 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”

4 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

5 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

6 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. One-time Telephone Studies, Incidence 50% Plus February 1 – June 301995199920022005 Refusal Rate66%68%78%? Response Rate16%17%12%? (Refusal Rate = Refusals/Total Asked; Response Rate = Cooperative Contacts/Total Eligible Numbers) Average Annual Increase1995 – 19991999 - 2002 Increase in refusal rate per year0.5%3.3% 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.

7 What is Happening to Response Rates? … cont’d The longer the interview, the higher the refusal rate. 2002 data showed this impact very clearly. Aggregate Refusal Rate Interview Length (Minutes)<1010 – 1920+ 1995505968 1999456263 2002657480

8 Standardized Response Rate Calculation

9 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.

10 How Do We Measure Response Rates – MRIA Approved Definition Empirical Method of Response Rate Calculation Empirical Calculation for Data CollectionExample (Every HHLD qualifies) Total Numbers Attempted4000 Invalid NIS, fax/modem, business/non-res. 1000 Unresolved (U) Busy, no answer, answering machine 900 In-scope – non-responding (IS) Language problem Illness, incapable Selected respondent not available Household refusal Respondent refusal Qualified respondent break-off 1050 100 50 100 500 250 50 In-scope – Responding units ( R ) Language disqualify No one 18+ Other disqualify Completed interviews 1050 - 1050 Response Rate = R / (U + IS + R): 1050/900 + 1050 + 105035%

11 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

12 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

13 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)

14 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

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

16 MRIA’s Research Project

17 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.

18 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

19 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

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

21 Fieldwork Undertaken By: Ipsos-Reid Maritz Opinion Search Synovate TNS-Canadian Facts

22 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

23 Disposition of Last Attempt9% RR31% RR Valid numbers attempted14,8324,348 100% Busy/No Answer5,843780 Refused4,8261,647 Other Non-Responding2,820569 Cooperative Respondents1,3431,352 Response Rate = R / (U + IS + R)9.1%31.1% Disqualified10579 Completed Interviews1,2381,273 IS R U

24 Key Findings

25 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

26 Food Items Used in Past 6 Months Results Identical 9% RR31% RRSig. Diff. * Eggs9796N Cold Cereals86 N Cheese (Not processed)6971N Honey6766N Frozen Pizza55 N * At 90% level of confidence

27 Items Bought in the Last 12 Months Same result regardless of whether category incidence is high, medium or low 9% RR31% RRSig. Diff. Men’s or Women’s Clothing93 N Sunscreen / Suntan Lotion54 N Paint or Stain5251N Camping Equipment23 N Car Polish / Wax2120N Traveler’s Cheques88N

28 Appliances in Household Similar findings for both commonplace and more esoteric items 9% RR31% RRSig. Diff. Microwave oven95 N Automatic Dishwasher6361N Gas BBQ5957N Security System3437N Espresso/Cappuccino Maker1413N

29 Print Media Readership Similar estimates of generic print media consumption 9% RR31% RRSig. Diff. Read a Daily Newspaper - Yesterday60 N - Past Week84 N Last Time Read a Magazine - Yesterday3840N - Past Week72 N

30 Traveled Outside Canada in Past 12 Months Parallel results for both business and personal travel behaviour 9% RR31% RRSig. Diff. For Personal3233N For Business89N

31 Personal Access to The Internet Penetration levels virtually identical 9% RR31% RRSig. Diff. Any Access76 N At Home70 N At Work45`46N

32 Cell Phones No differences in either ownership incidence or carrier share 9% RR31% RRSig. Diff. Has a Cell Phone58 N Cellular Provider * Bell2928N Telus2527N Rogers25 N Fido66N Other11 N * Base Total Cell Phone Owners

33 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

34 9% RR31% RRSig. Diff. Has any Credit Cards7882+ 5 Specific Cards Owned * Visa6770N MasterCard5248-4-4 American Express1318+ 5 Diners11N Any Department Store4546N Any Gasoline Company1514N Average # of Cards Owned * 2.42.5N * Base: Total Credit Card Owners Credit Cards Owned

35 Credit Cards Used Most Often Base = Owners of Credit Cards 9% RR31% RRSig. Diff. Visa4952N MasterCard3029N American Express34N Any Department Store Card33N Any Gasoline Company Card11N Claimed usage level unaffected by higher response rate.

36 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

37 9% RR31% RRSig. Diff. I like to try new and different products6.0 N I am willing to pay extra to save time5.4 N I lead a fairly busy social life5.96.0N A person’s career should be their 1 st priority4.9 N TV is a primary source of entertainment5.7 N I have more self-confidence than most people my age6.9 N I keep up-to-date with changes in style5.45.3N I am careful of what I eat7.2 N I go out with friends a great deal of the time5.15.2N To me shopping is a chore rather than a pleasure6.15.9- 0.2 I prefer to postpone a purchase rather than buy on credit6.66.7N Attitudinal Statements

38 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

39 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.

40 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

41 Online Research

42 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

43 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

44 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

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