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USE OF PROCESS DATA TO DETERMINE THE NUMBER OF CALL ATTEMPTS IN A TELEPHONE SURVEY

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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

Annica Isaksson

Linköping University, Sweden

Peter Lundquist

Statistics Sweden

Daniel Thorburn

Stockholm University, Sweden

Q2008

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
• (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
• 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

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

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 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

Assume: of the costs are allocated to RHG h

Q2008

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

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
• 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

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

= .002

= 55,267,619,616

= 110,979,155

.

Q2008

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

Q2008