<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Surveys estimate and monitor a variety of data collection parameters, including response propensity, number of contacts, and data collection costs. These parameters can be used as inputs to a responsive/adaptive design or to monitor the progression of a data collection period against predefined expectations. Recently, Bayesian methods have emerged as a method for combining historical information or external data with data from the in-progress data collection period to improve prediction. We develop a Bayesian method for predicting a measure of case-level progress or productivity, the estimated time lag, in days, between first contact attempt and first respondent contact. We compare the quality of predictions from the Bayesian method to predictions generated from more commonly-used predictive methods that leverage data from only historical data collection periods or the in-progress round of data collection. Using prediction error and misclassification as short- or long- day lags, we demonstrate that the Bayesian method results in improved predictions close to the day of the first contact attempt, when these predictions may be most informative for interventions or interviewer feedback. This application adds to evidence that combining historical and current information about data collection, in a Bayesian framework, can improve predictions of data collection parameters.</p>
</abstract>

Surveys estimate and monitor a variety of data collection parameters, including response propensity, number of contacts, and data collection costs. These parameters can be used as inputs to a responsive/adaptive design or to monitor the progression of a data collection period against predefined expectations. Recently, Bayesian methods have emerged as a method for combining historical information or external data with data from the in-progress data collection period to improve prediction. We develop a Bayesian method for predicting a measure of case-level progress or productivity, the estimated time lag, in days, between first contact attempt and first respondent contact. We compare the quality of predictions from the Bayesian method to predictions generated from more commonly-used predictive methods that leverage data from only historical data collection periods or the in-progress round of data collection. Using prediction error and misclassification as short- or long- day lags, we demonstrate that the Bayesian method results in improved predictions close to the day of the first contact attempt, when these predictions may be most informative for interventions or interviewer feedback. This application adds to evidence that combining historical and current information about data collection, in a Bayesian framework, can improve predictions of data collection parameters.

Predicting Days to Respondent Contact in Cross-Sectional Surveys Using a Bayesian Approach

Department of Biostatistics, School of Public Health, University of Michigan

Journal of Official Statistics

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

Surveys estimate and monitor a variety of data collection parameters, including response propensity, number of contacts, and data collection costs. These...

Predicting Days to Respondent Contact in Cross-Sectional Surveys Using a Bayesian Approach

Online veröffentlicht: 07. Sept. 2023

Seitenbereich: 325 - 349

Eingereicht: 01. Apr. 2022

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/jos-2023-0015

Schlüsselwörter
Adaptive design, hurdle models, national health interview survey

© 2023 Stephanie Coffey et al., published by Sciendo

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

Predicting Days to Respondent Contact in Cross-Sectional Surveys Using a Bayesian Approach

Online veröffentlicht: 07. Sept. 2023

Seitenbereich: 325 - 349

Eingereicht: 01. Apr. 2022

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/jos-2023-0015

SchlüsselwörterAdaptive design, hurdle models, national health interview survey

© 2023 Stephanie Coffey et al., published by Sciendo

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

Schlüsselwörter
Adaptive design, hurdle models, national health interview survey