Transcript Slide 1
Responsive Collection Design (RCD)
for CATI Surveys and
Total Survey Error (TSE)
François Laflamme
International Total Survey Error Workshop (ITSEW)
Quebec, June 2011
Statistics Canada • Statistique Canada
18/07/2015
Outline
Introduction
RCD surveys
RCD strategy
Active management for RCD
Monitoring and decision making tools
Highlights and lessons learned
RCD and TSE
Next Steps
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Introduction
Responsive Collection Design (RCD) is an
adaptive approach that uses the information
available prior and during data collection to adjust
collection strategy for the remaining in-progress
cases
Trade-off between quality, cost, productivity,
responding potential of in-progress cases, survey
mode and interaction between surveys
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RCD Surveys
Two experiment surveys with RD and control groups
Households and the Environment Survey (HES 2009)
● Dwelling survey with a cross-sectional design
● Canadian Community Health Survey (CCHS 2009) sampling
frame
Survey of Labour and Income Dynamics (SLID 2010)
● Longitudinal survey (Complex survey design)
One full RCD survey - SLID 2011
Embedded experiment for the first call
Objectives: proof of concept, improve efficiency
and quality
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RCD Strategy
1) Planning phase
Analysis of previous data collection cycle
Data collection phases and strategies
● RCD objectives, staffing plans and response
propensity model for each survey
Sample validation
Active management tools and reports
● New key indicators and communication plan
Control group to assess RCD impact
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RCD Strategy (cont’d)
2) Initial data collection phase
Use strategic improvement opportunities
previously identified
● New time slice strategy and intermediate cap
More likely to collect easy cases
Monitor key indicators to identify start of RCD
Phase 1
● Response rate, productivity, cost (proportion of budget
spent) and responding potential of in-progress cases
● By Regional Office (RO)
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RCD Strategy (cont’d)
3) RCD Phase 1 - Daily overnight job
Categorize and prioritize cases to improve overall
response rates
● Probability of completion (propensity) - logistic regression
model (sampling frame and sequence of calls information)
Monitor key indicators to identify start of RCD Phase 2
● Representativity indicator and previous key indicators
● By Regional Office (RO)
4) RCD Phase 2 - Daily overnight job
Prioritize cases to improve sample representativity
● Priority to domains of interest with lower response rates
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RCD Strategy for HES and SLID
Planning
Phase
Initial Collection
Phase
Control
group
Responsive Collection
Design Phase 1
Responsive Collection
Design Phase 2
Control
Priority
groups
nHES = 10,000 units
nSLID = 16,805 units
Sample
nHES = 20,000 units
nSLID = 33,306 units
No contact
RD group
Other
cases
High probability
nHES = 10,000 units
nSLID = 16,801 units
Miscellaneous
Intermediate Cap on calls
Special
group
Denotes a reassessment of the sample, after which cases will be assigned to a new group.
1) For SLID 2010, a group called “High probability-Tracing” was used during RD phase 1
2) For SLID 2011, another group called “High probability-Refusal” was added during RD phase 1
3) The intermediate and global caps on calls were (20, 25) for HES and (30, 40) for SLID
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Active Management for RCD
Set of plans and tools to manage data collection while
in progress
By Regional Office (RO)
Paradata and data sources used
Blaise Transaction file (BTH) (i.e. calls and contact information),
interviewer payroll hours, budget and target figures, previous
and current collection cycle information, response propensity
model results
Key indicators
Used to identify when to start RCD Phase 1 and Phase 2
No survey estimates monitoring so far
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When to initiate RCD Phases?
Decision based on survey progress in terms of response rate,
productivity, proportion of budget spent (cost) and responding
potential of in-progress sample
Indicators to Identify Start of Responsive Collection Design Phase 1,
Sturgeon Falls, HES 2009
Response rate
Proportion of budgeted system time
Proportion of regular in-progress cases
Daily productivity
100%
Average productivity over the last 5 days
Proportion of budgeted payroll hours
Average number of calls made on 'regular' cases / cap on calls
80%
60%
40%
20%
0%
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3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
Data Collection Days
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RCD Dashboard -
Example for RCD Phase 1
Dashboards are used to identify when to start both RCD phases
to facilitate interpretation and objective decision-making
RCD phase 1: 6 conditions, RCD phase 2: 7 conditions
Yellow and red lights signal when many conditions are met
Response Rate
RO
Productivity
(Average over last 5
Response Cond
Cond
Initial Current
Rate
1
2
Budget and Cost
% of
Budget
payroll
hours
Cond
3
% of
Budget
System
Time
Propensity of In-Progress Cases
Avg. # of calls
% of regular
Cond
Cond for regular in- Cond Sum of
In-progress
4
5
6
conditions
progress
cases
cases / cap
EDM
52.2%
1
64.3% 41.6%
1
56.6%
1
62.4%
1
28.3%
0
7.2
0
HFX
52.6%
1
63.6% 45.5%
1
62.8%
1
69.8%
1
27.0%
0
7.7
0
SHER 46.3%
0
66.9% 58.3%
1
49.8%
0
50.7%
0
34.5%
0
3.4
0
STURG 64.2%
1
74.4% 46.6%
1
67.2%
1
65.6%
1
13.9%
1
11.3
1
TOR
52.6%
1
65.7% 53.6%
1
58.0%
1
55.4%
1
27.9%
0
6.8
0
WIN
62.4%
1
69.9% 46.8%
1
69.8%
1
69.0%
1
12.5%
1
13.0
1
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When to initiate RCD Phase 2?
Similar dashboard than RCD Phase 1 is used (7 conditions)
Condition 7: average response rate increase over the last 5 days
Representativity indicator is also used
Representativity indicator is a measure of variability of response rates
between domains of interest
National versus regional objectives
Representativity Indicator, National and some Regional Offices, HES 2009
1.000
0.990
0.980
0.970
0.960
0.950
0.940
0.930
National
Sherbrooke
Sturgeon Falls
Edmonton
0.920
0.910
0.900
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3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53
Data Collection Day
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Other Active Management Tools for
RCD - Examples
Interviewing progress, results and effort
Response and resolved rates, tracing and refusal conversion results
and effort, refusal at the first contact, refusal and no contact rates
In-progress cases
Distribution of calls (for cap on calls monitoring), distribution of cases by
Blaise group, browser use, intensive tracing, cases with high
propensity but few calls
System time (effort)
By period of day, phase and group (RCD, CG)
Expected distribution by Blaise group between phases
Useful for staff planning
Several other ad hoc tools
Used to identify problems or emerging issues
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Active Management Challenges
Large amount of information and reports available
In the past not enough info, currently too much
Need to concentrate on major issues (not on good to know info)
● Can spend a lot of time on something not broken
● Some analysis can wait at the end of collection
Analysis and communication
Often require an extra analytical step before the information
is communicated
● Reports are not enough
● Real challenge is to analyse, summarize and communicate
● In the case of RCD, only the main reports were distributed
● Other reports were only used when required
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Highlights and Lessons Learned
Higher overall response rate when RCD is used
Compared to previous survey cycle
RD group achieved same response rate with less effort (~2%)
Sample representativity generally improved
High probability and priority groups had positive impact
SLID 2011
Better response rate and contact rate (over 2%) for cases for which the
first call was forced to be in the same time slice of previous interview
Pilot test demonstrated the technical feasibility of RCD
Active Management and communication
Essential for any RCD
Need timely and accessible paradata
RCD is not a “magic” solution
Need to be used in conjunction with other initiatives
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RCD and TSE
RCD objectives in terms of TSE
Reduce variance and non-response bias
Currently, more likely the variance
But might depend on the non-response adjustment strategy
Did RCD introduce a potential non-response bias?
Active Monitoring of the non-response bias requires survey
estimates during data collection - but can be done after
Did RCD change the responding propensity of cases?
Soft treatments - More meaningful grouping of cases
● No incentives, no sub-sampling of respondents
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Next Steps
Implement RCD for other CATI surveys
Developed with a research rather than a production perspective
Improve current RCD strategy
Propensity models
Gradually phase-in of RCD phase1
Integrate new conditions for decision making
● Representativity, survey estimates, non-response bias
Include cost-efficiency objective
RCD for CAPI surveys
Potential benefits of RCD to improve cost-efficiency
Feasible study : communication flow, potential actions, concurrent
surveys, operational and technical constraints.
RCD for multi-mode surveys
RCD theoretical framework
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For more information, please contact
Pour plus d’information, veuillez contacter
François Laflamme
[email protected]
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