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Microsimulation Collection Project. Kristen Couture Yves Bélanger Elisabeth Neusy Marcelle Tremblay. Outline. Overview Models created prior to Simulation Call Outcomes Call Duration Simulation Model SAS Simulation Studio program overview Aspects of Simulation Some Early Results

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Microsimulation collection project

Microsimulation Collection Project

Kristen Couture

Yves Bélanger

Elisabeth Neusy

Marcelle Tremblay


Outline
Outline

  • Overview

  • Models created prior to Simulation

    • Call Outcomes

    • Call Duration

  • Simulation Model

    • SAS Simulation Studio program overview

    • Aspects of Simulation

  • Some Early Results

  • Conclusions and Future Work


Overview
Overview

  • What are we trying to do?

    • 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


Overview1
Overview

  • Questions we are trying to answer:

    • What effect do time slices have on the collection process?

    • How does the distribution of interviewers affect collection?

    • How does the introduction of a cap on calls affect the overall response rate?


Steps to building simulation
Steps to Building Simulation

Simulation

Collection Parameters


Modelling call outcomes
Modelling Call Outcomes

  • 5 outcomes: Unresolved, Out of Scope, Refusal, Other Contact, Respondent

  • Modelled Using Multinomial Logistic Regression and CSGVP 2004 BTH

  • 7 parameters entered into the model

i = 1..n

j = 1..k

Parameters Data Set


Modelling call outcomes1
Modelling Call Outcomes

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


Modelling call duration
Modelling Call Duration

  • Use 2004 CSGVP BTH

  • Draw histograms for each outcome

  • Use Probability Plots to Determine Distribution and Parameters

Response Histogram

Normal Probability Plot

D

U

R

A

T

I

O

N

P

E

R

C

E

N

T

Normal Percentiles

Call Duration



Aspects of simulation
Aspects of Simulation

  • Consists of…

    • Input: user enters parameters for model

    • Clock: Creates parameters from simulation clock

    • Queue: calls wait to be interviewed

    • Call Center: calls are made, outcome and duration of call is simulated

    • Interviewer Agenda: change # of interviewers

    • Time Slices (in progress): maximum number of attempts implemented for each time slice

    • Output: BTH file


Input
Input

  • Allows user to enter parameters via SAS Data Sets

Parameters Data Set

Time Slice Data Sets


Clock
Clock

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


Queuing system
Queuing System

  • Cases are created and enter a queue waiting to be interviewed


Determining call outcome
Determining Call Outcome

  • Determines Call Outcome:

    • Unresolved

    • Out of Scope

    • Other Contact

    • Refusal

    • Respondent


Call center
Call Center

  • Call is sent to Call Center where it is interviewed


Call center1
Call Center

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


Finalizing cases
Finalizing Cases

  • Outcome of Out of Scope or Respondent

  • Reached Cap on Calls

    • Residential: 20

    • Unknown: 5

  • Number of Refusals=3

  • Output is created in terms of SAS data set




Simulation example
Simulation Example

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

  • Interviewers:

    • Shift 1 (9am-12pm) : 10

    • Shift 2 (12pm-5pm) : 10

    • Shift 3 (5pm-9pm) : 10

      *Note: No time slices in this example


Diagnostics
Diagnostics

Finalized Cases and Response Rate

Distribution of Outcome Codes


Diagnostics1
Diagnostics

Last Call Outcome

Last Call Outcome by Original Residential Status


Changing parameters
Changing Parameters

Effect on changing the number of interviewers and days of collection


Conclusions
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


Future work
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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