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1 Microsimulation Collection Project Kristen Couture Yves Bélanger Elisabeth Neusy Marcelle Tremblay.

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Presentation on theme: "1 Microsimulation Collection Project Kristen Couture Yves Bélanger Elisabeth Neusy Marcelle Tremblay."— Presentation transcript:

1 1 Microsimulation Collection Project Kristen Couture Yves Bélanger Elisabeth Neusy Marcelle Tremblay

2 2 Outline  Overview  Models created prior to Simulation Call Outcomes Call Outcomes Call Duration Call Duration  Simulation Model SAS Simulation Studio program overview SAS Simulation Studio program overview Aspects of Simulation Aspects of Simulation  Some Early Results  Conclusions and Future Work

3 3 Overview  What are we trying to do? Construct a simulation model that will represent the CATI collection process using SAS Simulation Studio Construct a simulation model that will represent the CATI collection process using SAS Simulation Studio  Why are we doing this? To attempt to find ways to optimise collection activities that will make collection more efficient within a controlled environment To attempt to find ways to optimise collection activities that will make collection more efficient within a controlled environment

4 4 Overview  Questions we are trying to answer: What effect do time slices have on the collection process? What effect do time slices have on the collection process? How does the distribution of interviewers affect collection? How does the distribution of interviewers affect collection? How does the introduction of a cap on calls affect the overall response rate? How does the introduction of a cap on calls affect the overall response rate?

5 5 Steps to Building Simulation Pre-existing BTH from Survey (2004 CSGVP BTH) Model Call Outcomes Model Call Duration Simulation Collection Parameters

6 6 Modelling Call Outcomes 5 outcomes: Unresolved, Out of Scope, Refusal, Other Contact, Respondent 5 outcomes: Unresolved, Out of Scope, Refusal, Other Contact, Respondent Modelled Using Multinomial Logistic Regression and CSGVP 2004 BTH Modelled Using Multinomial Logistic Regression and CSGVP 2004 BTH 7 parameters entered into the model 7 parameters entered into the model i = 1..n j = 1..k Parameters Data Set

7 7 Modelling Call Outcomes  Calculate probability for each possible call outcome using estimated betas and collection parameters

8 8 Modelling Call Duration  Use 2004 CSGVP BTH  Draw histograms for each outcome  Use Probability Plots to Determine Distribution and Parameters Normal Percentiles Call Duration Response Histogram PERCENTPERCENT DURATIONDURATION Normal Probability Plot

9 9 SAS Simulation Studio

10 10 Aspects of Simulation  Consists of… Input: user enters parameters for model Input: user enters parameters for model Clock: Creates parameters from simulation clock Clock: Creates parameters from simulation clock Queue: calls wait to be interviewed Queue: calls wait to be interviewed Call Center: calls are made, outcome and duration of call is simulated Call Center: calls are made, outcome and duration of call is simulated Interviewer Agenda: change # of interviewers Interviewer Agenda: change # of interviewers Time Slices (in progress): maximum number of attempts implemented for each time slice Time Slices (in progress): maximum number of attempts implemented for each time slice Output: BTH file Output: BTH file

11 11 Input Time Slice Data Sets Parameters Data Set  Allows user to enter parameters via SAS Data Sets

12 12 Clock  Creates Time Parameters including Evening, Weekend, PM, and Time Slices by reading the current simulation time

13 13 Queuing System  Cases are created and enter a queue waiting to be interviewed

14 14 Determining Call Outcome  Determines Call Outcome: Unresolved Unresolved Out of Scope Out of Scope Other Contact Other Contact Refusal Refusal Respondent Respondent

15 15 Call Center  Call is sent to Call Center where it is interviewed

16 16 Call Center  User can change the number of interviewers during a specified time period

17 17 Finalizing Cases  Outcome of Out of Scope or Respondent  Reached Cap on Calls Residential: 20 Residential: 20 Unknown: 5 Unknown: 5  Number of Refusals=3  Output is created in terms of SAS data set

18 18 SAS Simulation Demonstration

19 19 Demonstration Output

20 20 Simulation Example  Create 10,000 cases and run the simulation for 30 days of collection  Interviewers: Shift 1 (9am-12pm) : 10 Shift 1 (9am-12pm) : 10 Shift 2 (12pm-5pm) : 10 Shift 2 (12pm-5pm) : 10 Shift 3 (5pm-9pm) : 10 Shift 3 (5pm-9pm) : 10 *Note: No time slices in this example

21 21 Diagnostics Finalized Cases and Response Rate Distribution of Outcome Codes

22 22 Diagnostics Last Call Outcome Last Call Outcome by Original Residential Status

23 23 Changing Parameters Effect on changing the number of interviewers and days of collection

24 24 Conclusions  Allows user to enter parameters into model  Reproduce results similar to CSGVP 2004  Create a BTH file  Change parameters and look at the effect

25 25 Future Work  Improve the model by adding more parameters  Produce results with time slices implemented to model to measure impact  Add attributes to the interviewers such as English/French/bilingual and Senior/Junior  Rearrange the cases in the queue so that they will be pre-empted at best time to call


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