Methodological issues of using administrative data to improve the quality of survey data 2
|Convenor||Dr Emanuela Sala (Dipartimento di Sociologia e Ricerca Sociale, Università di MIlano Bicocca )|
|Coordinator 1||Dr Jonathan Burton (ISER, University of Essex)|
|Coordinator 2||Dr Gundi Knies (ISER, University of Essex)|
We take advantage of the fact that for EU-SILC 2011 in Austria comparable income data from register and from questionnaires are available. Our analysis shows that 1) the collection of income is more complete with register data and 2) the distribution for the majority of income types becomes more unequal as compared to questionnaire data. After a description of households whose poverty status changes if register data is used, explanatory factors will be analysed. A focus on explanations for the increase is provided. Moreover, the consequences of using register data for the calculation of income and weights are examined.
Because of the imperfections in autobiographical recall, there is often skepticism about the usefulness of retrospective data. One way of analyzing retrospective recall errors is using linked survey and administrative data. However, the reliability of administrative data also has been distrusted. By using the linked data-set ALWA-ADIAB (which combines interview data and administrative data from the same individuals) we analyze recall errors of retrospective life course data considering the “notification error” – the error which can be ascribed to the administrative notification process. We show that the extent of the “notification error” is large and cannot remain unconsidered.
This presentation highlights the uses and challenges of integrating administrative data in the Survey of Income and Program Participation (SIPP). We discuss four areas where administrative records are being used with household survey processes and products. These include imputation, data synthesis, data validation, and evaluation of bias. This work represents some of the important areas of innovation and are key areas of the SIPP program’s future. We discuss several of the challenges associated with integrating administrative and survey data systems, producing linked data resources, providing public access opportunities, and protecting respondent confidentiality.
Increasingly studies are incorporating biological data to help elucidate mechanisms linking social factors and health. However, differences in obtaining consent to collect these data can affect representativeness. Administrative data sources can be used to identify characteristics associated with nonresponse that are otherwise unobserved in a panel survey. We use administrative health records linked to 20,000+ Health and Retirement Study respondents to investigate potential nonresponse bias in measured biological data. We examine whether the current nonresponse propensity adjustment method used to create subsample weights sufficiently adjusts for potential bias once characteristics beyond those collected in the panel study are considered.
The utility of research utilising data linkage depends on linked dataset representativeness: inferences may be biased if population subgroup relative frequencies differ from those in the parent population. Non-representativeness can occur, for example, because respondent propensities to consent to linkage vary. We study such questions in four business surveys. To quantify representativeness and estimate impacts of consent propensity variation associated with different business attribute variables, we utilise for the first time methods developed to investigate survey non-response bias (R indicators, Coefficients of Variation, Dissimilarity indices). We describe our findings considering data collection strategies, and also evaluate differences observed