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Microsimulation Microsimulation Collection ProjectCollection Project

Kristen CoutureKristen Couture

Yves BélangerYves Bélanger

Elisabeth NeusyElisabeth Neusy

Marcelle TremblayMarcelle Tremblay

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OutlineOutline

OverviewOverview Models created prior to SimulationModels created prior to Simulation

Call OutcomesCall Outcomes Call DurationCall Duration

Simulation ModelSimulation Model SAS Simulation Studio program overviewSAS Simulation Studio program overview Aspects of SimulationAspects of Simulation

Some Early ResultsSome Early Results Conclusions and Future WorkConclusions and Future Work

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OverviewOverview

What are we trying to do? What are we trying to do? Construct a simulation model that will Construct a simulation model that will

represent the CATI collection process using represent the CATI collection process using SAS Simulation StudioSAS Simulation Studio

Why are we doing this?Why are we doing this? To attempt to find ways to optimise collection To attempt to find ways to optimise collection

activities that will make collection more activities that will make collection more efficient within a controlled environmentefficient within a controlled environment

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OverviewOverview

Questions we are trying to answer:Questions we are trying to answer: What effect do time slices have on the What effect do time slices have on the

collection process?collection process? How does the distribution of interviewers How does the distribution of interviewers

affect collection?affect collection? How does the introduction of a cap on calls How does the introduction of a cap on calls

affect the overall response rate?affect the overall response rate?

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Steps to Building SimulationSteps to Building Simulation

Pre-existing BTH from Survey (2004 CSGVP BTH)

Model Call Outcomes Model Call Duration

Simulation

Collection Parameters

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Modelling Call OutcomesModelling Call Outcomes• 5 outcomes: Unresolved, Out of Scope, Refusal, Other 5 outcomes: Unresolved, Out of Scope, Refusal, Other

Contact, RespondentContact, Respondent• Modelled Using Multinomial Logistic Regression and CSGVP Modelled Using Multinomial Logistic Regression and CSGVP

2004 BTH2004 BTH

• 7 parameters entered into the model7 parameters entered into the model

i = 1..nj = 1..k

Parameters Data Set

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Modelling Call OutcomesModelling Call Outcomes

Calculate probability for each possible call outcome Calculate probability for each possible call outcome using estimated betas and collection parametersusing estimated betas and collection parameters

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Modelling Call DurationModelling Call Duration Use 2004 CSGVP BTHUse 2004 CSGVP BTH

Draw histograms for each outcomeDraw histograms for each outcome

Use Probability Plots to Determine Distribution and ParametersUse Probability Plots to Determine Distribution and Parameters

NEWCODE=5

1. 25 5. 25 9. 25 13. 25 17. 25 21. 25 25. 25 29. 25 33. 25 37. 25 41. 25 45. 25 49. 25 53. 25 57. 25 61. 25

0

0. 5

1. 0

1. 5

2. 0

2. 5

Percent

DURATI ON

0. 001 0. 01 0. 1 1 5 10 25 50 75 90 95 99 99. 9 99. 9999. 999

0

10

20

30

40

50

60

70

DURATION

5

Nor mal Per cent i l esNormal PercentilesCall Duration

Response Histogram

P

E

R

C

E

N

T

D

U

R

A

T

I

O

N

Normal Probability Plot

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SAS Simulation StudioSAS Simulation Studio

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Aspects of SimulationAspects of Simulation

Consists of…Consists of… Input: user enters parameters for modelInput: user enters parameters for model Clock: Creates parameters from simulation clockClock: Creates parameters from simulation clock Queue: calls wait to be interviewedQueue: calls wait to be interviewed Call Center: calls are made, outcome and duration of Call Center: calls are made, outcome and duration of

call is simulatedcall is simulated Interviewer Agenda: change # of interviewersInterviewer Agenda: change # of interviewers Time Slices (in progress): maximum number of Time Slices (in progress): maximum number of

attempts implemented for each time sliceattempts implemented for each time slice Output: BTH fileOutput: BTH file

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InputInput

Time Slice Data SetsParameters Data Set

Allows user to enter Allows user to enter parameters via SAS parameters via SAS Data SetsData Sets

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ClockClock

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

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Queuing SystemQueuing System

Cases are created and enter a queue waiting to be Cases are created and enter a queue waiting to be interviewedinterviewed

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Determining Call OutcomeDetermining Call Outcome

Determines Call Outcome: Determines Call Outcome: UnresolvedUnresolved Out of ScopeOut of Scope Other ContactOther Contact RefusalRefusal RespondentRespondent

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Call CenterCall Center

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

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Call CenterCall Center

User can change the number of interviewers User can change the number of interviewers during a specified time periodduring a specified time period

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Finalizing CasesFinalizing Cases

Outcome of Out of Outcome of Out of Scope or RespondentScope or Respondent

Reached Cap on CallsReached Cap on Calls Residential: 20Residential: 20 Unknown: 5Unknown: 5

Number of Refusals=3Number of Refusals=3

Output is created in Output is created in terms of SAS data setterms of SAS data set

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SAS Simulation DemonstrationSAS Simulation Demonstration

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Demonstration OutputDemonstration Output

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Simulation ExampleSimulation Example

Create 10,000 cases and run the Create 10,000 cases and run the simulation for 30 days of collectionsimulation for 30 days of collection

Interviewers:Interviewers: Shift 1 (9am-12pm) : 10Shift 1 (9am-12pm) : 10 Shift 2 (12pm-5pm) : 10Shift 2 (12pm-5pm) : 10 Shift 3 (5pm-9pm) : 10Shift 3 (5pm-9pm) : 10

*Note: No time slices in this example*Note: No time slices in this example

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DiagnosticsDiagnosticsFinalized Cases and Response Rate

Distribution of Outcome Codes

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DiagnosticsDiagnosticsLast Call Outcome

Last Call Outcome by Original Residential Status

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Changing ParametersChanging Parameters

Effect on changing the number of interviewers and days of collection

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ConclusionsConclusions

Allows user to enter parameters into Allows user to enter parameters into modelmodel

Reproduce results similar to CSGVP 2004Reproduce results similar to CSGVP 2004 Create a BTH fileCreate a BTH file Change parameters and look at the effectChange parameters and look at the effect

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Future WorkFuture Work

Improve the model by adding more Improve the model by adding more parametersparameters

Produce results with time slices Produce results with time slices implemented to model to measure impactimplemented to model to measure impact

Add attributes to the interviewers such as Add attributes to the interviewers such as English/French/bilingual and Senior/JuniorEnglish/French/bilingual and Senior/Junior

Rearrange the cases in the queue so that Rearrange the cases in the queue so that they will be pre-empted at best time to callthey will be pre-empted at best time to call