Weighting issues in complex cross-sectional and longitudinal surveys 2 |
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Convenor | Ms Nicole Watson (University of Melbourne ) |
Coordinator 1 | Dr Olena Kaminska (University of Essex) |
To avoid errors due to undercoverage in CATI-Surveys, the dual-frame approach that employs independently drawn samples from both the landline and the mobile frame might be an appropriate solution for nationwide surveys. A specific source of error that can arise within this sampling approach might be determined by individual usage-patterns for specific means of communication. Therefore this paper will introduce a weighting approach that takes such frame-dependent causes of nonresponse into account by subdividing the survey population into different categories based on their usage-patterns and employing a composite design weighting approach.
Dual-frame telephone surveys have become increasingly important in Australia with the proportion of adults owning mobile phones and living in households without a landline telephone increasing exponentially from 5% in 2005 to 27% in 2014.This paper reviews the dual-frame estimators available for estimating population quantities using combined data from overlapping telephone sampling frames. Selected estimators are assessed empirically in the Australian context, where data on differential non-response by sampling frame is unknown. Additionally, bias in each of the estimators is evaluated under different patterns of non-response for simulated data with known telephone frame population totals.
The paper describes the construction of cross-sectional weights for the second wave of the Panel of Household Finances survey, the German part of the euro area Household Finance and Consumption Survey. The approach is the ‘base weight’ approach (eg Verma, Betti and Ghellini, 2006). It thus entails the construction of person weights, from which household weights are derived. The procedure includes adjustments of the initial weights of the panel members and their affiliated members, adjustments of the design weights of the refreshment sample, non-response adjustments and calibration of the merged sample.The procedure is compared with alternative approaches.
The concept of MCAR, MAR and NMAR is useful theoretically, but in practice it is rare to observe a situation with pure MCAR (where nonrespondents are exactly the same as respondents on a particular estimate) or MAR (where nonresponse can be perfectly explained). The most common situation in practice is what we call ‘partial MAR’, where some, but not the whole, nonresponse process can be explained. The presentation will introduce the concept of partial MAR, provide theoretical background for its concept and showcase its usefulness in developing nonresponse weights using a few empirical examples from the UK Household Longitudinal Survey.