Microsimulation Collection Project

1
Microsimulation Collection Project

• Kristen Couture
• Yves Bélanger
• Elisabeth Neusy
• Marcelle Tremblay

2
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

3
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

4
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?

5
Steps to Building Simulation
Simulation
Collection Parameters
6
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
7
Modelling Call Outcomes

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

8
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
9
SAS Simulation Studio
10
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

11
Input

• Allows user to enter parameters via SAS Data Sets

Parameters Data Set
Time Slice Data Sets
12
Clock

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

13
Queuing System

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

14
Determining Call Outcome

• Determines Call Outcome
• Unresolved
• Out of Scope
• Other Contact
• Refusal
• Respondent

15
Call Center

• Call is sent to Call Center where it is
  interviewed

16
Call Center

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

17
Finalizing Cases

• Outcome of Out of Scope or Respondent
• Reached Cap on Calls
• Residential 20
• Unknown 5
• Number of Refusals3
• Output is created in terms of SAS data set

18
SAS Simulation Demonstration
19
Demonstration Output
20
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

21
Diagnostics
Finalized Cases and Response Rate
Distribution of Outcome Codes
22
Diagnostics
Last Call Outcome
Last Call Outcome by Original Residential Status
23
Changing Parameters
Effect on changing the number of interviewers and
days of collection
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
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