Agenda How Important Are Response Rates? What Is Happening With Response Rates? Measuring Response Rates Does Any Of This Really Matter?
Dec 16, 2015
Agenda
How Important Are Response Rates?
What Is Happening With Response Rates?
Measuring Response Rates
Does Any Of This Really Matter?
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”
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
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
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 30 1995 1999 2002 2005
Refusal Rate 66% 68% 78% ?
Response Rate 16% 17% 12% ?
(Refusal Rate = Refusals/Total Asked; Response Rate = Cooperative Contacts/Total Eligible Numbers)
Average Annual Increase 1995 – 1999 1999 - 2002
Increase in refusal rate per year 0.5% 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.
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) <10 10 – 19 20+
1995 50 59 68
1999 45 62 63
2002 65 74 80
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.
How Do We Measure Response Rates – MRIA Approved Definition
Empirical Method of Response Rate Calculation
Empirical Calculation for Data Collection Example(Every HHLD qualifies)
Total Numbers Attempted 4000InvalidNIS, fax/modem, business/non-res.
10001000
Unresolved (U)
Busy, no answer, answering machine
900
900In-scope – non-responding (IS)
Language problemIllness, incapableSelected respondent not available
Household refusalRespondent refusalQualified respondent break-off
1050
10050
100
50025050
In-scope – Responding units ( R )
Language disqualifyNo one 18+Other disqualifyCompleted interviews
1050
---
1050Response Rate = R / (U + IS + R): 1050/900 + 1050 + 1050 35%
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
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
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)
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
The Critical Issue!
Can response rates at these levels still produce accurate and meaningful data?
Clearly more research was needed
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.
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
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
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
Disposition of Last Attempt 9% RR 31% RR
Valid numbers attempted 14,832 4,348
100% 100%
Busy/No Answer 5,843 780
Refused 4,826 1,647
Other Non-Responding 2,820 569
Cooperative Respondents 1,343 1,352
Response Rate = R / (U + IS + R) 9.1% 31.1%
Disqualified 105 79
Completed Interviews 1,238 1,273
IS
R
U
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
Food Items Used in Past 6 Months
Results Identical
9% RR 31% RR Sig. Diff. *
Eggs 97 96 N
Cold Cereals 86 86 N
Cheese (Not processed) 69 71 N
Honey 67 66 N
Frozen Pizza 55 55 N
* At 90% level of confidence
Items Bought in the Last 12 Months
Same result regardless of whether category incidence is high, medium or low
9% RR 31% RR Sig. Diff.
Men’s or Women’s Clothing 93 93 N
Sunscreen / Suntan Lotion 54 54 N
Paint or Stain 52 51 N
Camping Equipment 23 23 N
Car Polish / Wax 21 20 N
Traveler’s Cheques 8 8 N
Appliances in Household
Similar findings for both commonplace and more esoteric items
9% RR 31% RR Sig. Diff.
Microwave oven 95 95 N
Automatic Dishwasher 63 61 N
Gas BBQ 59 57 N
Security System 34 37 N
Espresso/Cappuccino Maker 14 13 N
Print Media Readership
Similar estimates of generic print media consumption
9% RR 31% RR Sig. Diff.
Read a Daily Newspaper
- Yesterday 60 60 N
- Past Week 84 84 N
Last Time Read a Magazine
- Yesterday 38 40 N
- Past Week 72 72 N
Traveled Outside Canada in Past 12 Months
Parallel results for both business and personal travel behaviour
9% RR 31% RR Sig. Diff.
For Personal 32 33 N
For Business 8 9 N
Personal Access to The Internet
Penetration levels virtually identical
9% RR 31% RR Sig. Diff.
Any Access 76 76 N
At Home 70 70 N
At Work 45` 46 N
Cell Phones
No differences in either ownership incidence or carrier share
9% RR 31% RR Sig. Diff.
Has a Cell Phone 58 58 N
Cellular Provider *
Bell 29 28 N
Telus 25 27 N
Rogers 25 25 N
Fido 6 6 N
Other 11 11 N
* Base Total Cell Phone Owners
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
9% RR 31% RR Sig. Diff.
Has any Credit Cards 78 82 + 5
Specific Cards Owned *
Visa 67 70 N
MasterCard 52 48 -4
American Express 13 18 + 5
Diners 1 1 N
Any Department Store 45 46 N
Any Gasoline Company 15 14 N
Average # of Cards Owned * 2.4 2.5 N
* Base: Total Credit Card Owners
Credit Cards Owned
Credit Cards Used Most Often
Base = Owners of Credit Cards 9% RR 31% RR Sig. Diff.
Visa 49 52 N
MasterCard 30 29 N
American Express 3 4 N
Any Department Store Card 3 3 N
Any Gasoline Company Card 1 1 N
Claimed usage level unaffected by higher response rate.
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
9% RR 31% RR Sig. Diff.
I like to try new and different products 6.0 6.0 N
I am willing to pay extra to save time 5.4 5.4 N
I lead a fairly busy social life 5.9 6.0 N
A person’s career should be their 1st priority 4.9 4.9 N
TV is a primary source of entertainment 5.7 5.7 N
I have more self-confidence than most people my age 6.9 6.9 N
I keep up-to-date with changes in style 5.4 5.3 N
I am careful of what I eat 7.2 7.2 N
I go out with friends a great deal of the time 5.1 5.2 N
To me shopping is a chore rather than a pleasure 6.1 5.9 - 0.2
I prefer to postpone a purchase rather than buy on
credit
6.6 6.7 N
Attitudinal Statements
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
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.
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
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
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
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