Invest igat ing Internet Opt-in Panels for Behavioral Surveillance
Carol A. Gotway Crawford, PhD Chief, Population Health Surveillance Branch
Federal Committee on Statistical Methodology Research Conference November 4, 2013
Washington, DC
National Center for Chronic Disease Prevention and Health Promotion Division of Population Health
Catherine Okoro, PhD Epidemiologist
Satvinder Dhingra, MPH Epidemiologist
Northrop Grumman
What Are Internet Opt-in Panels?
Potent ial panelists are recruited via the Internet Banner ads, email lists, promotions, and offers Double opt-in process to become a panel member
Panelists become the pool for sample select ion
Panel may or may not be representat ive of the
populat ion Coverage is limited to Internet users (~ 80% of the population) Respondent selection and motivation
Why Use Internet Opt-in Panels?
Lower cost than probability-based sampling
Shorter collect ion and prep t ime for data release to the public than current methods (RDD, face-to-face)
Expands the surveillance and study tool-kit
Permits longitudinal and in-depth follow-up studies
Increases administrat ive and design flexibility and efficiency
Pilot Study
4 States Cooperative agreements in GA, IL, NY, and TX
3 Vendors Different sampling methodologies Cooperating and collaborating
De-duplication of respondents Nearly identical questionnaire format
3 Levels of Geography National State Metropolitan Statistical Area
Pilot Object ives
Compare sampling methodologies Sample matching, source blending, and quota
Assess feasibility and accuracy for public health
Compare est imates with those from other surveys
Evaluate across a range of parameters: Cost, geographic granularity, and timeliness
Sampling Methodologies
Sample Matching Different modes of recruitment are used to ensure representativeness for
hard-to-reach populations Potential respondents are selected by matching to a random sample from
the American Community Survey Final responses are weighted to known characteristics in the U.S. using
propensity score weighting
Sample Blending Uses population segments designed to reflect behavioral differences but
based on Census data Apply the segmentation structure locally to balance, weight, and blend
sample
Quota Sampling A non-probability sample in which respondents take the survey on a first-
come, first-served basis according to a fixed quota
Quest ionnaire Development
Survey consists of ~80 questions (20 minutes) Questions drawn from:
• CDC: BRFSS, NHANES, & NHIS • NIH: PROMIS • SAMHSA: NSDUH • ONC: Consumer Survey of Attitudes Toward the Privacy and
Security Aspects of EHR and HIE • NPWF (National Partnership for Women and Families) • NSF supported Cooperative Congressional Election Study
Benchmarking
National: Demographics (Unweighted)
NHIS 2012 HH CAPI
BRFSS 2012 DF-RDD CATI
YouGov 2013 IPS Matched
CPS 2012
0.0 20.0 40.0 60.0 80.0
White nH
Black nH
Hispanic
Other
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Male
Female
0.0 10.0 20.0 30.0 40.0
18 -29
30 - 44
45 - 64
65+
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
<HS
=HS
>HS
National: Demographics (Weighted)
NHIS 2012 HH CAPI
BRFSS 2012 DF-RDD CATI
YouGov 2013 IPS Matched
0.0 10.0 20.0 30.0 40.0
18 -29
30 - 44
45 - 64
65+
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Male
Female
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
<HS
=HS
>HS
0.0 20.0 40.0 60.0
White nH
Black nH
Hispanic
Other
State: Age
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
GA
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
IL
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
NY
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
TX
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
BRFSS 2011 DF-RDD CATI
MSA: Age
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
Houston
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
New York
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
Chicago
0
10
20
30
40
50
60
18-25 26-34 35-64 >=65
Atlanta
State: Race/Ethnicity
0
20
40
60
80
White nH Black nH Hispanic Other
TX
0
20
40
60
80
White nH Black nH Hispanic Other
NY
0
20
40
60
80
White nH Black nH Hispanic Other
IL
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
0
20
40
60
80
White nH Black nH Hispanic Other
GA
MSA: Race/Ethnicity
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
010203040506070
White nH Black nH Hispanic Other
Atlanta
010203040506070
White nH Black nH Hispanic Other
Chicago
010203040506070
White nH Black nH Hispanic Other
New York
010203040506070
White nH Black nH Hispanic Other
Houston
State: Education
010203040506070
<HS =HS >HS
TX
010203040506070
<HS =HS >HS
NY
010203040506070
<HS =HS >HS
IL
010203040506070
<HS =HS >HS
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
MSA: Education
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
010203040506070
<HS =HS >HS
Houston
010203040506070
<HS =HS >HS
New York
010203040506070
<HS =HS >HS
Chicago
010203040506070
<HS =HS >HS
Atlanta
National: Outcomes
NHIS 2012 HH CAPI
BRFSS 2012 DF-RDD CATI
YouGov 2013 IPS Matched
0.0 10.0 20.0 30.0 40.0
Obesity (BMI ≥30)
NHANES (MEC) NHANES (SR) NHIS 2012
BRFSS 2012 YouGov 2013
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Diabetes
NHANES (MEC) NHANES (SR) NHIS 2012
BRFSS 2012 YouGov 2013
0.0 20.0 40.0 60.0 80.0 100.0
Ex/VG/GD
Fair/Poor
NHIS 2012 BRFSS 2012 YouGov 2013
State: Obesity (BMI ≥30)
0
10
20
30
40
50
Yes
TX
0
10
20
30
40
50
Yes
NY
0
10
20
30
40
50
Yes
IL
0
10
20
30
40
50
Yes
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
MSA: Obesity (BMI ≥30)
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
0
10
20
30
40
50
Atlanta
0
10
20
30
40
50
Chicago
0
10
20
30
40
50
New York
0
10
20
30
40
50
Houston
State: Diabetes
0
5
10
15
Yes
TX
0
5
10
15
Yes
NY
0
5
10
15
Yes
IL
0
5
10
15
Yes
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
MSA: Diabetes
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
-2
3
8
13
18
Houston
-2
3
8
13
18
New York
-2
3
8
13
18
Chicago
-2
3
8
13
18
Atlanta
State: Disability
0
10
20
30
40
Activity or Eqpt
TX
0
10
20
30
40
Activity or Eqpt
NY
0
10
20
30
40
Activity or Eqpt
IL
0
10
20
30
40
Activity or Eqpt
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2013 Preliminary DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
MSA: Disability
05
101520253035
Activity or Eqpt
Houston
05
101520253035
Activity or Eqpt
New York
05
101520253035
Activity or Eqpt
Chicago
05
101520253035
Activity or Eqpt
Atlanta
Mktg Inc. 2013 Blended
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
SMART BRFSS 2012 DF-RDD CATI
National: Health Care Access, Utilization, Behaviors & Outcomes
NHIS 2012 HH CAPI
BRFSS 2012 DF-RDD CATI
YouGov 2013 IPS Matched
0.0 20.0 40.0 60.0 80.0
No Pap
No Mam
No Colorectal
No HIV Test
No Flu
0.0 10.0 20.0 30.0 40.0 50.0
No Insurance
No Usual Source
Cost Barrier
No Past year Checkup
0.0 5.0 10.0 15.0 20.0 25.0 30.0
Current Smoker
Former Smoker
Heavy Drinker
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
Diabetes
Hypertension
Any Cancer
Asthma
Arthritis
CHD
State: Health Insurance
05
101520253035
No
TX
05
101520253035
No
NY
05
101520253035
No
IL
05
101520253035
No
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
State: Primary Care Provider
05
101520253035
No
TX
05
101520253035
No
NY
05
101520253035
No
IL
05
101520253035
No
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
State: Cost Barrier
0
10
20
30
40
Yes
IL
0
10
20
30
40
Yes
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
0
10
20
30
40
Yes
TX
0
10
20
30
40
Yes
NY
State: Current Smoker
0
10
20
30
40
100 + Ev/Some
TX
0
10
20
30
40
100 + Ev/Some
NY
0
10
20
30
40
100 + Ev/Some
IL
0
10
20
30
40
100 + Ev/Some
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
State: Heavy Drinker
0
5
10
15
20
Yes
TX
0
5
10
15
20
Yes
NY
0
5
10
15
20
Yes
IL
0
5
10
15
20
Yes
GA
Mktg Inc. 2013 Blended
NHIS 2011 HH CAPI
BRFSS 2011 DF-RDD CATI
YouGov 2013 IPS Matched
uSamp 2013 IPS Quota
Quantifying Uncertainty
The use of Frequentist confidence intervals with data from a non-probability sample is theoretically inappropriate
Bayesian credible intervals are a more appropriate way to quantify uncertainty when analyzing data from a non-probability sample
In our pilot studies, however, both methods yielded highly similar, if not ident ical, results
Uncertainty Comparison
Variable Confidence
Interval Credible Interval
Obesity 29.22 32.61 29.12 32.56
Diabetes 9.88 11.95 9.85 11.94
High BP 26.64 29.62 26.56 29.58
Major Benefits
• Time (samples constructed to be representative): < 15 days for a national survey ~ 4,000 interviews ~ 30 days for most states ~3,000 interviews ~ 30 days for large (5+ million) MSAs ~2,000 interviews
• Cost: – Internet opt-in panels: $5-$15 per completed interview
• Costs include editing and weighting
– Dual-frame RDD State direct costs average ~$70/CI • Considerable additional costs for editing and weighting
Preliminary Results • Great deal of similarity
– Results of sample matching comparable with BRFSS and NHIS – Variation among surveys consistent across states – Internet opt-in panels fairly accurate at lower levels of geography – Quota sampling not as accurate
• Differences can be attributed to: – Coverage effects (sample selection*outcome interaction) – Use of different control totals and weighting methods – Mode effects (face-to-face, telephone, Internet) – Question differences and order effects – Temporal changes (2013 vs. 2011) – Sample size differences – Cross-sectional differences
Acknowledgements
Stephen Ansolabehere, Harvard University & CCES Steven Gittelman, Mktg. Inc. Douglas Rivers, Stanford University & YouGov Meena Khare, NCHS Georgia, Illinois, New York, and Texas Departments of Health, BRFSS
Rana Bayakly, Madhavi Vajani, Francis Annor, GA Bruce Steiner, IL Bethany Hawke, NY Rebecca Wood, TX
Haci Akcin, CDC Derek Ford, CDC Guixiang Zhao, CDC Soyoun Park, CDC