A Study of Interviewer Compliance in 2013 and 2014 Census Test Adaptive Designs

Walejko, Gina; Wagner, James

doi:10.2478/jos-2018-0031

Cited by 4 publications

(3 citation statements)

References 21 publications

(25 reference statements)

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“…For example, a response propensity model based on housing unit characteristics may determine a case is likely to respond, but the interviewer may ignore that recommendation because they know the householder is away on vacation. Recommendation-type adaptive procedures typically involve case prioritization where cases are prioritized to decrease the risk of survey error (Wagner et al 2012), increase the representativeness of harder-to-reach populations (Dahlhamer 2017;Tolliver et al 2017Tolliver et al , 2019, or increase overall response by targeting the cases most likely to respond (Walejko and Wagner 2018).…”

Section: Current Environmentmentioning

confidence: 99%

Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods

Coffey,

Damineni,

Eltinge

et al. 2024

Journal of Official Statistics

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Adaptive survey design is a framework for making data-driven decisions about survey data collection operations. This article discusses open questions related to the extension of adaptive principles and capabilities when capturing data from multiple data sources. Here, the concept of “design” encompasses the focused allocation of resources required for the production of high-quality statistical information in a sustainable and cost-effective way. This conceptual framework leads to a discussion of six groups of issues including: (1) the goals for improvement through adaptation; (2) the design features that are available for adaptation; (3) the auxiliary data that may be available for informing adaptation; (4) the decision rules that could guide adaptation; (5) the necessary systems to operationalize adaptation; and (6) the quality, cost, and risk profiles of the proposed adaptations (and how to evaluate them). A multiple data source environment creates significant opportunities, but also introduces complexities that are a challenge in the production of high-quality statistical information.

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Section: Current Environmentmentioning

confidence: 99%

Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods

Coffey,

Damineni,

Eltinge

et al. 2024

Journal of Official Statistics

View full text Add to dashboard Cite

show abstract

“…These parameters may be defined at various levels of the data collection process, including the interviewer level (Mneimneh et al 2018); the question-level (Mittereder and West 2021); the case-level (Wagner et al 2012), and the survey level (LaFlamme and Karaganis 2010). Indicators of cost, progress, and quality have also served as inputs into responsive and adaptive designs (Groves and Heeringa 2006;Schouten et al 2011) that allow for the balancing of cost and quality goals through centralized interventions including mode changes (Chesnut 2013;Coffey et al 2019), case prioritization (Walejko and Wagner 2018;Tolliver et al 2019;Peytchev et al 2020) and stopping rules (Wagner and Ragunathan 2010;Peytchev 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Coffey,

Elliott

2023

Journal of Official Statistics

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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.

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“…Responsive and adaptive survey designs may mitigate the risk for nonresponse bias and control data collection costs [8,9]. However, practical implementation of these designs is not necessarily easy [10,11]. One reason is that interviewer resources are often limited.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Response Propensities Using the Previous Survey

Honkala¹

2019

IJSDA

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Many surveys are carried out yearly, and the implementation of the surveys remains the same from year to year.Experience from a previous survey is useful when planning a new survey, because the response behavior usually remains quite the same in subsequent years. This paper studies how response propensities, estimated using the dataset of the previous survey, predict actual response rates. In this study, two consecutive datasets of the European Social Survey were available. The both datasets contained same register variables. Response propensities were estimated to the older dataset using a logistic regression model. Then the propensities were imputed to the newer dataset using a donor-recipient method. The imputation was based on the explanatory variables of the logistic regression model so that the donor and the recipient had the same values in the variables. Then it was examined if there was a connection between the imputed response propensities and actual response rates. The result was that the imputed response propensities predicted the response behavior quite well. People with low response propensities were often nonrespondents, and people with high response propensities were often respondents. Using the previous survey, it is possible to calculate response propensities for a new sample before the data collection of the survey has been started. Then challenging respondents are known before the data collection, and this information is useful for data collection.

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A Study of Interviewer Compliance in 2013 and 2014 Census Test Adaptive Designs

Cited by 4 publications

References 21 publications

Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods

Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods

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

Estimation of Response Propensities Using the Previous Survey

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