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Microsimulation of Survey Collection

Microsimulation of Survey Collection. Yves Bélanger Kristen Couture 26 January 2010. Outline. Motivation Main aspects of microsimulation Overview of the system A short demo A few results Future work. Motivation. Ultimate goal: make CATI collection more efficient

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Microsimulation of Survey Collection

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

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

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

  6. Overview of the System Simulation Collection Parameters

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

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

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

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

  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. A Short Demo

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

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

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

  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

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