<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is adaptive in the sense that the effort assigned to a sample unit may vary from one unit to another and may also vary during data collection. The goal of an adaptive procedure is usually to increase quality for a given cost or, alternatively, to reduce cost for a given quality. The quality criterion often considered in the literature is the nonresponse bias of an estimator that is not adjusted for nonresponse. Although the reduction of the nonresponse bias is a desirable goal, we argue that it is not a useful criterion to use at the data collection stage of a survey because the bias that can be removed at this stage through an adaptive collection procedure can also be removed at the estimation stage through appropriate nonresponse weight adjustments. Instead, we develop a procedure of call prioritization that, given the selected sample, attempts to minimize the conditional variance of a nonresponse-adjusted estimator subject to an overall budget constraint. We evaluate the performance of our procedure in a simulation study.</p></abstract>

We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is adaptive in the sense that the effort assigned to a sample unit may vary from one unit to another and may also vary during data collection. The goal of an adaptive procedure is usually to increase quality for a given cost or, alternatively, to reduce cost for a given quality. The quality criterion often considered in the literature is the nonresponse bias of an estimator that is not adjusted for nonresponse. Although the reduction of the nonresponse bias is a desirable goal, we argue that it is not a useful criterion to use at the data collection stage of a survey because the bias that can be removed at this stage through an adaptive collection procedure can also be removed at the estimation stage through appropriate nonresponse weight adjustments. Instead, we develop a procedure of call prioritization that, given the selected sample, attempts to minimize the conditional variance of a nonresponse-adjusted estimator subject to an overall budget constraint. We evaluate the performance of our procedure in a simulation study.

An Adaptive Data Collection Procedure for Call Prioritization

Statistics Canada, 100 Tunney’s Pasture Driveway, R.H. Coats Bldg., 16-B Ottawa K1A 0T6, Canada.

Statistics Canada, 100 Tunney’s Pasture Driveway, R.H. Coats Bldg., 18-E Ottawa K1A 0T6, Canada.

Université de Montréal, Département de mathématiques et de statistique, Pavillon André Aisenstadt, Case postale 6128, Montre´al H3C 3J7, Canada.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"An Adaptive Data Collection Procedure for Call Prioritization"}

We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is...

An Adaptive Data Collection Procedure for Call Prioritization

Publicado en línea: 11 dic 2014

Páginas: 607 - 621

Recibido: 01 ene 2013

Aceptado: 01 jul 2014

DOI: https://doi.org/10.2478/jos-2014-0040

Palabras clave
Adaptive collection design, nonresponse bias, nonresponse variance, nonresponse weight adjustment, paradata, responsive collection design

© by Jean-Francois Beaumont

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Adaptive Data Collection Procedure for Call Prioritization

Publicado en línea: 11 dic 2014

Páginas: 607 - 621

Recibido: 01 ene 2013

Aceptado: 01 jul 2014

DOI: https://doi.org/10.2478/jos-2014-0040

Palabras claveAdaptive collection design, nonresponse bias, nonresponse variance, nonresponse weight adjustment, paradata, responsive collection design

© by Jean-Francois Beaumont

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Adaptive collection design, nonresponse bias, nonresponse variance, nonresponse weight adjustment, paradata, responsive collection design