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Data set representativeness during data collection in three UK social surveys: generalizability and the effects of auxiliary covariate choice
Author(s) -
Moore Jamie C.,
Durrant Gabriele B.,
Smith Peter W. F.
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12256
Subject(s) - representativeness heuristic , generalizability theory , covariate , data collection , data set , computer science , survey data collection , sample (material) , set (abstract data type) , statistics , non response bias , econometrics , data quality , data mining , mathematics , business , marketing , programming language , chemistry , chromatography , metric (unit)
Summary We consider the use of representativeness indicators to monitor risks of non‐response bias during survey data collection. The analysis benefits from use of a unique data set linking call record paradata from three UK social surveys to census auxiliary attribute information on sample households. We investigate the utility of census information for this purpose and the performance of representativeness indicators (the R ‐indicator and the coefficient of variation of response propensities) in monitoring representativeness over call records. We also investigate the extent and effects of misspecification of auxiliary covariate sets used in indicator computation and design phase capacity points in call records beyond which survey data set improvements are minimal, and whether such points are generalizable across surveys. Given our findings, we then offer guidance to survey practitioners on the use of such methods and implications for optimizing data collection and efficiency savings.