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pdfUnderstanding Trust in Official
Statistics in the United States
Jennifer Hunter Childs
Center for Survey Measurement
Research and Methodology Directorate
U.S. Census Bureau
Acknowledgements
• Ryan King and Aleia Clark Fobia, U.S.
Census Bureau
• Paul Scanlon, National Center for Health
Statistics
• Michelle Smirnova, University of Missouri,
Kansas City
Overview
• Developing a Framework to Measure Trust
in Official Statistics
• Qualitative Methodology and Findings
• Quantitative Methodology and Findings
• Implications for Building Trust
• Time Series Analysis
• Conclusions
Organization for Economic Co-operation and
Development (OECD) Working Group
• Measure trust in official statistics
• Produce model survey questionnaire to gather
comparable statistics internationally
• Cognitively tested in 6 member nations (Brackfield
2011)
• Questions are inadequately understood by U.S.
respondents (Willson 2010)
4
Trust in Statistical Products
•
•
•
•
•
•
•
Accuracy
Reliability
Credibility
Objectivity
Relevance
Timeliness
Coherence
5
Trust in Statistical Institution
•
•
•
•
•
Confidentiality Protected
Integrity
Openness/Transparency
Impartiality
Effective Stakeholder Management
6
OECD Model of Trust in Official Statistics
7
Qualitative Findings in US
• Overall lack of knowledge about federal statistics
led to:
– Shifting Interpretations
– Not thinking about statistics
– Interpretations limited to examples given
8
Recommendations from
Qualitative Testing
• Define concepts up front
• Be specific
• Keep it simple
9
Awareness
• Knowledge of Statistics
• Data User
– Found to be best predictor of question
comprehension in past cognitive testing by
NCHS
10
Trust in Statistical Products
•
•
•
•
•
•
•
Accuracy
Reliability
Credibility
Objectivity
Relevance
Timeliness
Coherence
11
Trust in Statistical Institution
•
•
•
•
•
Confidentiality Protected
Integrity
Objectivity/Transparency
Impartiality
Effective Stakeholder Management
12
Eurobarometer measure
Personally, how much trust do you have in the
federal statistics in the United States? Would
you say that you tend to trust federal statistics
or tend not to trust them?
Data Collection
• Gallup Nightly Survey
– 200 responses daily
– Subsample of National RDD Sample
– Landline and Cellphone
• Initial Gallup contract was 25 questions collected Feb.
2012
– September 2013
– 19 question core, 6 rotating questions
• Contract extension was 10 questions collected
October 2013 – March 2014
– 5 question core, 5 rotating questions
14
Quantitative Findings
• Relationships with trust
• Confidence in institutions
%
who
Trust
Federal
Sta1s1cs
Relationships between Overall
Trust and Other Measures
100
90
80
70
60
50
40
30
20
10
0
71.9
69.1
59.5
17
Disagree
24.7
21.1
Agree
Disagree
Relevance
of
Fed
Stats
Agree
Invade
Respect
Agencies
Keep
Data
Agencies
and
Privacy
ConfidenEal
A4tudes
on
Other
Measures
%
Who
Report
Trus1ng
Fed
Stats
Effects on Trust by Data User
100
90
80
70
60
50
40
30
20
10
0
Data
User
Non-‐Data
User
Disagree
Agree
Disagree
Agree
Invade
Respect
Relevance
of
Fed
Stats
Agencies
Keep
Data
Agencies
and
Privacy
ConfidenEal
A4tudes
on
Other
Measures
Relationship between Confidence in Institutions by
Knowledge of Federal Statistics
0.7
Gamma
Correla1on
0.6
0.5
FSS
-‐
Newspapers
FSS
-‐
Congress
FSS
-‐
UniversiEes
FSS
-‐
Newspapers
FSS
-‐
UniversiEes
0.4
0.3
FSS
-‐
Congress
FSS
-‐
Military
0.2
0.1
0
FSS
-‐
Military
Low
Knowedge
High
Knowledge
Implications for Building Trust
• In a time when confidence in Congress is
particularly poor, increasing knowledge about
the statistical system and increasing data users
could help people differentiate between sectors
of the government, thus increasing confidence
in the FSS.
• Overall, knowledge of the statistical system, as
well as using data, believing the data are
relevant, kept confidential and that agencies
respect privacy all correspond with increased
trust in the statistical products.
Trust
Trust Post Shutdown
Confidential
Confidential Post-Shutdown
Time Series Analysis
24
Do levels of trust change during
times of negative publicity?
October 5, 2012
June 6, 2013
May 10, 2013
October 1, 2013
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#SFBLJO%BUB$PMMFDUJPO
2013−09−15
2013−06−10
2013−05−10 *344UPSZ
0.35
2012−10−06 5XFFU
0.40
/4"4UPSZ
0.45
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2014−03−15
●
4IVUEPXO
●●
2013−10−01
0.65
2012−02−15
Proportion of Population Reporting Trust in Federal Statistics
3FQPSUFE5SVTUJO'FEFSBM4UBUJTUJDT0WFS5JNF
26
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*344UPSZ
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%JTBHSFF
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2013−10−01
●
2013−09−15
●
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2013−06−10
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2012−10−06
0.2
●
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2013−05−10
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2014−03−15
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2012−02−15
Proportion Reporting Agree/Disagree Need for Federal Statistics
3FQPSUFE#FMJFGUIBU1PMJDZ.BLFST/FFE'FEFSBM4UBUJTUJDT0WFS5JNF
28
So What?
• Does this impact response rates?
• Correlation DOES NOT imply causation,
but it’s something we can look at
29
Trust
and
Belief
in
ConfidenEality
along
with
Survey
Response
Rates*
1
0.9
0.8
0.7
Trust
Fed
Stats
0.6
FSAs
Keep
Data
ConfidenEal
0.5
FSAs
Respect
Privacy
0.4
Survey
1
RR
Survey
2
RR
0.3
Survey
3
RR
0.2
Survey
4
RR
2014-‐02
2014-‐01
2013-‐12
2013-‐11
2013-‐10
2013-‐09
2013-‐08
2013-‐07
2013-‐06
2013-‐05
2013-‐04
2013-‐03
2013-‐02
2013-‐01
2012-‐12
2012-‐11
2012-‐10
2012-‐09
2012-‐08
2012-‐07
2012-‐06
2012-‐05
2012-‐04
2012-‐03
0
2012-‐02
0.1
*Preliminary
unpublished
response
rates
Correlations between “Trust” and Response Rates
and “Confidential” and Response Rates
Survey 1 RR
• Trust r = 0.62 , p = 0.001
• Confidential r = 0.70, p < 0.001
Survey 2 RR
• Trust r = 0.74 , p < 0.001
• Confidential r = 0.58, p = 0.002
Survey 3 RR
• Trust r = 0.75, p < 0.001
• Confidential r = 0.64, p < 0.001
Survey 4 RR
• Trust r = 0.70, p<.001
• Confidential r = 0.55, p<.01
Conclusions
• “Breaking news” can impact how the
public views the statistical system.
• This can impact how they view our use of
administrative records for statistical
purposes.
• These news stories, or another
unmeasured yet related factor, seem to be
correlated with survey response rates.
32
Thank You!
Contact Information
Jennifer Hunter Childs
jennifer.hunter.childs@census.gov
File Type | application/pdf |
File Modified | 2014-12-22 |
File Created | 2014-12-22 |