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Modeling and Simulation of Survey Collection Using Paradata

Modeling and Simulation of Survey Collection Using Paradata. Presented by: Kristen Couture Co-authored by: Yves Bélanger Elisabeth Neusy. Outline. Motivation for Simulating Survey Collection Details of Simulation Modeling using Paradata Preliminary Results Conclusions Future Work.

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Modeling and Simulation of Survey Collection Using Paradata

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  1. Modeling and Simulation of Survey Collection Using Paradata Presented by: Kristen Couture Co-authored by: Yves Bélanger Elisabeth Neusy

  2. Outline • Motivation for Simulating Survey Collection • Details of Simulation • Modeling using Paradata • Preliminary Results • Conclusions • Future Work

  3. Motivation • Ultimate goal: make CATI survey collection more efficient • Recent initiatives in the field • Experimentation with call attempts and calling priorities • Takes time, lack of control, costly, results not always easy to interpret • Need for a controlled environment, where impact of each experiment can be tested prior to collection

  4. Microsimulation • What is microsimulation? • A modeling technique that operates at the level of individual units, such as persons, households, vehicles, etc. • For us: microsimulation = a "virtual collection" system • Recreates CATI collection environment with Simulation Software (SAS Simulation Studio) • Allows manipulation of parameters in simulated environment

  5. Microsimulation • What are the elements of our microsimulation? • Cases • Queues • Interviewers • Rules of the Call Scheduler (flows and priorities) • Output Call Transaction File

  6. SAS Simulation Studio Paradata Model Call Outcomes Model Call Duration Model Parameters Simulation Model Collection Parameters Overview of Microsimulation

  7. Modeling using Paradata • Use existing survey data (Blaise Transaction History) • Call Outcome • Multinomial logistic regression • Call Duration • Create histograms and fit distributions for each of the outcomes • Output Model parameters • Estimated parameters from logistic regression model • Fitted distribution and parameters • Input into simulation model

  8. Modeling using Paradata: Call Outcome • Multinomial Logistic Regression Model • Model probability of outcomes (sum of probabilities = 1) • k+1 outcomes • xi = explanatory variables from paradata • pj = probability of outcome j • = parameters from logistic regression model

  9. Modeling Call Outcome: An Example • Paradata: Existing RDD survey • 5 outcomes: • Unresolved, Out of Scope, Refusal, Other Contact, Respondent • 7 explanatory variables entered into the model • Time of Call: Afternoon, Evening, Weekend • Residential Status: Residential phone number • Call history: Unresolved, Refusal, Contact • Estimated parameters from model are entered into simulation

  10. Time of Call Call History Modeling Call Outcome: An Example • Calculate probability of each outcome pj values

  11. Paradata Model Call Outcomes Model Call Duration Model Parameters Simulation Model Collection Parameters Microsimulation

  12. Preliminary Results: Two examples • Investigate how collection parameters impact response rates • Two Examples: • Example 1 : Different distributions of interviewers throughout the day • Example 2: Different distributions of interviewers throughout the day combined with different time slices • Purpose: • Demonstrate how users can manipulate collection parameters to test specific collection scenarios • Verify that simulation results reflect collection

  13. Example 1 One Possible Scenario • Change allocation of interviewers throughout the time periods • 3 Time Periods each 4 hours in length • 30 interviewers per day for 30 days • What happens to response rates?

  14. Example 1

  15. Example 2 One Possible Scenario • Same setup as Example 1 • Add time slices: control maximum number of attempts made at different time periods throughout the day • What happens to response rates?

  16. Example 2 • Response Rates

  17. Conclusions • Create simple simulation model using paradata that produces results that reflect collection • Able to test different collection parameters to see impact on response rates without spending a lot of money or time • Approach adaptable to all types of CATI surveys

  18. Future Work • Improve logistic model by adding more parameters • Add more complicated collection procedures to the model such as interviewer characteristics • Simulate collection with multiple surveys at a time to see impact • Run simulation for a survey to predict outcome and compare with actual results from field

  19. For more information,  Pour plus d’information, please contact: veuillez contacter : Kristen Couture kristen.couture@statcan.gc.ca Yves Bélanger yves.belanger@statcan.gc.ca Elisabeth Neusy elisabeth.neusy@statcan.gc.ca

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