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Microsimulation: An Update

This update presents microsimulation of CATI survey collection, replicating Call Scheduler actions and predicting call outcomes. Results suggest optimal time slice strategies and the need to explore alternative collection methods. Future work includes enhancing outcome models and collaboration with SAS for technical improvements.

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Microsimulation: An Update

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  1. Microsimulation: An Update Yves Bélanger Kristen Couture Elisabeth Neusy Presented to the HouseholdSurveys Collection ResearchSteering 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 2/3residential) • 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?

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