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USE OF PROCESS DATA TO DETERMINE THE NUMBER OF CALL ATTEMPTS IN A TELEPHONE SURVEY. Annica Isaksson Linköping University, Sweden Peter Lundquist Statistics Sweden Daniel Thorburn Stockholm University, Sweden . The Problem.

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use of process data to determine the number of call attempts in a telephone survey
USE OF PROCESS DATA TO DETERMINE THE NUMBER OF CALL ATTEMPTS IN A TELEPHONE SURVEY

Annica Isaksson

Linköping University, Sweden

Peter Lundquist

Statistics Sweden

Daniel Thorburn

Stockholm University, Sweden

Q2008

the problem
The Problem

Consider a telephone survey of individuals, in which a maximum number A of call attempts is to be made to sampled individuals.

HOW SHALL A BE CHOSEN?

Part of a larger problem of designing efficient call scheduling algorithms.

Q2008

prerequisites
Prerequisites
  • (Single-occasion survey)
  • Direct sampling from a frame with good population coverage
  • Estimation of a population total by the direct weighting estimator

Observed value for individual k (proxy for the true value µk)

Response set after A call attempts

Estimated response probability for individual k

after A call attempts

Inclusion probability for individual k

Q2008

the survey as a three stage process
The Survey as a Three-Stage Process
  • Stage 1: Sample selection
  • Stage 2: Contact and response Maximally A call attempts are made. Individuals respond in accordance with an unknown response distribution.
  • Stage 3: Measurement Observed values are related to the true values according to a measurement error model.

Q2008

response model
Response Model

The sample can be divided into Hs response homogeneity groups (RHG) such that, for all A, given the sample,

  • all individuals within the same group have the same probability of responding
  • individuals respond independently of each other
  • individuals respond independently of each other after different numbers of call attempts

Q2008

measurement error model
Measurement Error Model

For an individual k in RHG h, given the sample and that the individual responds at call attempt a,

Indicates if individual k responds at attempt a=ak

Random interviewer effect with expectation 0 and variance

True value for individual k

Random response error with expectation 0 and variance

Q2008

bias and variance
Bias and Variance

Bias if the RHG model does not hold:

Sample covariance between response probabilities and design weighted true values

Average response probability within RHG

The variance of is derived in the paper

Q2008

optimum a for rhg h
Optimum A for RHG h

Assume: of the costs are allocated to RHG h

Q2008

optimum a for rhg h result
Optimum A for RHG h: Result

The optimum number of call attempts for RHG h is the number Ah that gives the lowest value on the function

Q2008

our data
Our Data

LFS data from March-Dec. 2007, supplemented with:

  • Annual salary 2006 according to the Swedish Tax Register (our y)
  • Process data from WinDati (WD)

.

Note: not all WD events are call attempts

Q2008

data processing and estimation
Data Processing and Estimation
  • Each monthly sample viewed as a SRS
  • Parameter: = total annual salary 2006
  • Bias within RHG h and month l estimated by

.

Q2008

measurement error model parameters
Measurement Error Model Parameters

Intraclass correlation, ICC (Biemer and Trewin, 1997):

= .002

= 55,267,619,616

= 110,979,155

.

Q2008

tentative results
Tentative Results
  • Efficient planning requires high-quality data on processes and costs
  • Perhaps the choice of A should be based on variance rather than MSE

Q2008

discussion and future work
Discussion and Future Work
  • Do the results hold for other study variables, other survey settings?
  • Improved models for measurement errors, response and costs?
  • Develop a planning tool?

Q2008

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