1. Microsimulation: An Update
Yves Bélanger
Kristen Couture
Elisabeth Neusy
Presented to the Household Surveys Collection Research Steering Committee
15 December 2010

2. To Recap... Microsimulation of collection for CATI surveys
Simulation at the case level
Every call is simulated
Uses two types of models:
A microsimulation model to replicate actions of Call Scheduler (Blaise), built with SAS Simulation Studio
A statistical model to predict outcome of each call, using actual paradata
Duration of calls also randomly generated based on observed distributions
Prototype built for a RDD-type survey, using paradata from CSGVP 2004

3. Characteristics of Current Prototype
What it has
Limit of 3 refusals
Cap on calls (may depend on case characteristics)
Definition of time slices
Distribution of interviewers (pre-determined but may vary with time)
Time slice strategies (pre-determined but may vary with time)
What it doesn’t have
Appointments (soft or hard)
Rules to limit use of time slices
# of calls in the outcome model

4. Various Scenarios Common parameters
3 time periods for interviewer distribution:
9:00-13:00
13:00-17:00
17:00-21:00
10,000 cases (about 2/3 residential)
40 days of interviewing
Fixed total of 4,800 interviewer-hours
A limit of 3 refusals
Interviewer distribution kept fixed throughout collection
Time slice strategy kept fixed throughout collection

5. Results

6. Results

7. Results

8. Results

9. Results

10. Results

11. Results

12. Results

13. Conclusions So Far
For general populations, try to have more interviewers in the evening
More importantly: time slice strategy must respect interviewer distribution (as much as possible)
Investigate alternative ways to use time slices
More active: try to alternate calls between time slices
For example: give priority to cases with lower # of calls within time slices

14. Future Work Add variables to outcome model
Does it pay to alternate time slices during collection?
Add a few outcomes to model to better represent how Blaise uses times slices
Extend capabilities of simulation model to deal with case priorities
Closer cooperation with SAS to help solve technical difficulties
Any other suggestions?