1. Microsimulation of Survey Collection Yves Bélanger Kristen Couture 26 January 2010

2. 2 Outline  Motivation  Main aspects of microsimulation  Overview of the system  A short demo  A few results  Future work

3. 3 Motivation  Ultimate goal: make CATI collection more efficient proactive collection management  Recent initiatives in the field Experimentation with time slices, cap on calls, calling priorities, Z-groups,... Takes time, lack of control, costly(?), results not always easy to interpret  Need for a controlled environment, where the impact of each aspect can be isolated

4. 4 Main Aspects of Microsimulation  What is microsimulation? A modelling technique that operates at the level of individual units, such as persons, households, vehicles, etc. For us: a "virtual collection" system  What elements are we considering? The cases (sampled units) The servers (interviewers) The call attempts The waiting queue(s) The rules of the call scheduler (flows and priorities)

5. 5 Main Aspects of Microsimulation (cont'd)  What do we want to simulate? 1.A random component: the result of each call attempt  Use existing BTH data with appropriate statistical models 2.A deterministic component: how the cases flow through the system  Use a simulation software to replicate Blaise: SAS Simulation Studio

6. 6 Overview of the System Pre-existing BTH from Survey Model Call Outcomes Model Call Duration Simulation Collection Parameters

7. 7 Overview of the System (cont'd)  Call outcome Modeled using CSGVP 2004 BTH data Five outcomes derived from BTH outcome codes Unresolved (eg. Busy signal, wrong #) Out of Scope (eg. Cell phone, Business) Refusal Other Contact (eg. Ans. Machine, appointment) Respondent

8. 8 Overview of the System (cont'd) Used Multinomial Logistic Regression 7 parameters entered into model:  Afternoon – 1 if call made between 12 and 5  Evening – 1 if call made between 5 and 9  Weekend - 1 if call made on weekend  Resid – 1 if initial status was residential  Unresolved – 1 if call history is only unresolved  Refusal – 1 if history shows at least one refusal  Contact – 1 if history shows at least one contact i = 1..n j = 1..k

9. 9 Overview of the System (cont'd) Calculate probability for each of the five possible outcomes using estimated betas and collection parameters

10. 10 Overview of the System (cont'd)  Call duration Modeled using existing CSGVP 2004 BTH data Modeled distributions for each of the 5 outcomes

11. 11 Overview of the System (cont'd)  Components of model Input  Allows user to enter parameters via SAS data sets

12. 12 Overview of the System (cont'd) Clock  Creates Time Parameters including Afternoon, Evening, Weekend, and Time Slice by reading the current simulation time

13. 13 Overview of the System (cont'd) Queuing System  Cases are created and enter a queue waiting to be interviewed

14. 14 Overview of the System (cont'd) Determining Call Outcome  Uses probability formulas to determine call outcome: Unresolved, Out of Scope, Other Contact, Refusal, Respondent

15. 15 Overview of the System (cont'd) Call Center  Interview takes place  Call duration is simulated  Ability to control interviewer schedule

16. 16 Overview of the System (cont'd) Finalizing Cases  Case exits system when… Outcome code = OOS or Respondent Cap on Calls is reached  Cap of 20 for Residential Status  Cap of 5 for Unknown Status Number of Refusals=3  A BTH file is created as output in terms of a SAS dataset

17. 17 A Short Demo

18. 18 A Few Results  Simulation with 10,000 cases for 30 days of collection  Interviewer Agenda Shift 1 (9am-12pm): 10 interviewers Shift 2 (12pm-5pm): 10 interviewers Shift 3 (5pm-9pm): 10 interviewers * Note: No Time Slices in this example

19. 19 A few results (cont'd) Finalized Cases and Response Rate Distribution of Outcome Codes

20. 20 A few results (cont'd)  Impact of Changing Parameters Number of Interviewers Length of Collection Period

21. 21 A few results (cont'd) Changing the Time Per Unit Cap on Calls is in Effect

22. 22 Future Work  Continue improvements to system To outcome model  More explanatory variables  Distinguish between hhld and person contacts To simulation system  Implement time slices  Improve priorities  Presentation to JSM (incl. article)  Potential cooperation with Census  Other?... will depend on available budget