Microsimulation
Collection Project
Kristen Couture
Yves Bélanger
Elisabeth Neusy
Marcelle Tremblay
1
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
2
Overview
 What

Construct a simulation model that will
represent the CATI collection process using
SAS Simulation Studio
 Why

are we trying to do?
are we doing this?
To attempt to find ways to optimise collection
activities that will make collection more
efficient within a controlled environment
3
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?
4
Steps to Building Simulation
Pre-existing BTH from Survey (2004 CSGVP BTH)
Model Call Outcomes
Model Call Duration
Simulation
Collection Parameters
5
Modelling Call Outcomes
•
•
5 outcomes: Unresolved, Out of Scope, Refusal, Other
Contact, Respondent
Modelled Using Multinomial Logistic Regression and CSGVP
2004 BTH
i = 1..n
j = 1..k
•
7 parameters entered into the model
Parameters Data Set
6
Modelling Call Outcomes

Calculate probability for each possible call outcome
using estimated betas and collection parameters
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Modelling Call Duration

Use 2004 CSGVP BTH

Draw histograms for each outcome

Use Probability Plots to Determine Distribution and Parameters
Response Histogram
N
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=5
Normal Probability Plot
2. 5
2. 0
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RD 50
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5. 25
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90 95
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99. 9 99. 9999. 999
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
D
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m
al Per cent i l es
NormalNorPercentiles
Call Duration
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SAS Simulation Studio
9
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
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Input

Allows user to enter
parameters via SAS
Data Sets
Parameters Data Set
Time Slice Data Sets
11
Clock

Creates Time Parameters including Evening,
Weekend, PM, and Time Slices by reading the
current simulation time
12
Queuing System

Cases are created and enter a queue waiting to
be interviewed
13
Determining Call Outcome

Determines Call Outcome:

Unresolved

Out of Scope

Other Contact

Refusal

Respondent
14
Call Center

Call is sent to Call Center where it is interviewed
15
Call Center

User can change the number of interviewers
during a specified time period
16
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
17
SAS Simulation Demonstration
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Demonstration Output
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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
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Diagnostics
Finalized Cases and Response Rate
Distribution of Outcome Codes
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Diagnostics
Last Call Outcome
Last Call Outcome by Original Residential Status
22
Changing Parameters
Effect on changing the number of interviewers and days of collection
23
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
24
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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