ABSTRACT. This article considers the assessment of the risk of identification of respondents in survey microdata, in the context of applications at the United Kingdom (UK) Office for National Statistics (ONS). The threat comes from the matching of categorical 'key' variables between microdata records and external data sources and from the use of log-linear models to facilitate matching. While the potential use of such statistical models is well-established in the literature, little consideration has been given to model specification nor to the sensitivity of risk assessment to this specification. In numerical work not reported here, we have found that standard techniques for selecting log-linear models, such as chi-squared goodness of fit tests, provide little guidance regarding the accuracy of risk estimation for the very sparse tables generated by 1 the accuracy of risk estimates. We find that, within a class of 'reasonable' models, risk estimates tend to decrease as the complexity of the model increases. We develop criteria which detect 'underfitting' (associated with overestimation of the risk). The criteria may also reveal 'overfitting' (associated with underestimation) although not so clearly, so we suggest employing a forward model selection approach. Our criteria turn out to be related to established methods of testing for overdispersion in Poisson log-linear models. We show how our approach may be used for both file-level and record-level measures of risk. We evaluate the proposed procedures using samples drawn from the 2001 UK Census where the true risks can be determined and show that a forward selection approach leads to good risk estimates. There are several 'good' models between which our approach provides little discrimination. The risk estimates are found to be stable across these models, implying a form of robustness. We also apply our approach to a large survey dataset. There is no indication that increasing the sample size necessarily leads to the selection of a more complex model. The risk estimates for this application display more variation but suggest a suitable upper bound.

Summary Non‐response is a common source of error in many surveys. Because surveys often are costly instruments, quality‐cost trade‐offs play a continuing role in the design and analysis of surveys. The advances of telephone, computers, and Internet all had and still have considerable impact on the design of surveys. Recently, a strong focus on methods for survey data collection monitoring and tailoring has emerged as a new paradigm to efficiently reduce non‐response error. Paradata and adaptive survey designs are key words in these new developments. Prerequisites to evaluating, comparing, monitoring, and improving quality of survey response are a conceptual framework for representative survey response, indicators to measure deviations thereof, and indicators to identify subpopulations that need increased effort. In this paper, we present an overview of representativeness indicators or R‐indicators that are fit for these purposes. We give several examples and provide guidelines for their use in practice.

More work is needed to encourage people to talk about their preferences at the end of life: this should not be restricted to those known to be dying. Increasing knowledge and achievement of preferences for place of death may also improve end-of-life care.

BackgroundAlthough in health services survey research we strive for a high response rate, this must be balanced against the need to recruit participants ethically and considerately, particularly in surveys with a sensitive nature. In survey research there are no established recommendations to guide recruitment approach and an ‘opt-in’ system that requires potential participants to request a copy of the questionnaire by returning a reply slip is frequently adopted. However, in observational research the risk to participants is lower than in clinical research and so some surveys have used an ‘opt-out’ system. The effect of this approach on response and distress is unknown. We sought to investigate this in a survey of end of life care completed by bereaved relatives.MethodsOut of a sample of 1422 bereaved relatives we assigned potential participants to one of two study groups: an ‘opt in’ group (n=711) where a letter of invitation was issued with a reply slip to request a copy of the questionnaire; or an ‘opt out’ group (n=711) where the survey questionnaire was provided alongside the invitation letter. We assessed response and distress between groups.ResultsFrom a sample of 1422, 473 participants returned questionnaires. Response was higher in the ‘opt out’ group than in the ‘opt in’ group (40% compared to 26.4%: χ2 =29.79, p-value<.01), there were no differences in distress or complaints about the survey between groups, and assignment to the ‘opt out’ group was an independent predictor of response (OR=1.84, 95% CI: 1.45-2.34). Moreover, the ‘opt in’ group were more likely to decline to participate (χ2=28.60, p-value<.01) and there was a difference in the pattern of questionnaire responses between study groups.ConclusionGiven that the ‘opt out’ method of recruitment is associated with a higher response than the ‘opt in’ method, seems to have no impact on complaints or distress about the survey, and there are differences in the patterns of responses between groups, the ‘opt out’ method could be recommended as the most efficient way to recruit into surveys, even in those with a sensitive nature.

Abstract. For decades, national statistical agencies and other data custodians have been publishing frequency tables based on census, survey and administrative data. In order to protect the confidentiality of individuals represented in the data, tables based on original data are modified before release. Recently, in response to user demand for more flexible and responsive table publication services, frequency table publication schemes have been augmented with on-line table generating servers such as the US Census Bureau FactFinder and the Australian Bureau of Statistics (ABS) TableBuilder. These systems allow users to build their own custom tables, and make use of automated perturbation routines to protect confidentiality. Motivated by the growing popularity of table generating servers, in this paper we study confidentiality protection for perturbed frequency tables, including the trade-off with analytical utility, focusing on a version of the ABS TableBuilder as a concrete example of a data release mechanism, and examining its properties. Confidentiality protection is assessed in terms of the differential privacy standard, and this paper can be used as a practical introduction to differential privacy, to calculations related to its application, to the relationship between confidentiality protection and utility and to confidentiality in general.

Age-associated disparity exists in care provided in the last two days and the realization of preferences.

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.

Background The End of Life Care Strategy highlighted a need to evaluate care experiences by accessing the views of those who use end of life care services. The Strategy identifi ed the Views of Informal Carers -Evaluation of Services (VOICES) questionnaire, which is completed by bereaved relatives, as a potential method of evaluating these experiences. The DH commissioned this study to explore the feasibility of a national VOICES survey. Aims To develop the most appropriate methods for a national end of life care survey by considering recruitment, sampling, online methods, ethics, increasing participation and support for participants. Methods VOICES was re-designed following user/professional discussion groups and analysis of existing VOICES datasets. 1422 deaths registered in two PCTs were identifi ed by the Offi ce of National Statistics using stratifi ed sampling methods. Coroner-registered deaths were excluded. Deaths were assigned to one of two trial groups to determine the most appropriate recruitment approach (opt-in vs opt-out). Online completion was offered to all informants. Local organisations representing minority ethnic groups collaborated in publicising the survey, interpreting services were provided and advertising posters were translated into fi ve languages. A series of support structures was initiated. Results Response rate was 33% and response was signifi cantly higher in the 'opt-out' trial group (40%, p<0.001). There were no complaints in either group: only two informants called the