Suchergebnisse

Abstract
Survey researchers frequently use supplementary data sources, such as paradata, administrative data, and contextual data to augment surveys and enhance substantive and methodological research capabilities. While these data sources can be beneficial, integrating them with surveys can give rise to ethical and data privacy issues that have not been completely resolved. In this research synthesis, we review ethical considerations and empirical evidence on how privacy concerns impact participation in studies that collect these novel data sources to supplement surveys. We further discuss potential approaches for safeguarding participants' data privacy during data collection and dissemination that may assuage their concerns. Finally, we conclude with open questions and suggested avenues for future research.

Abstract
Longitudinal surveys are a key data collection tool used to estimate social change. Recent developments have accelerated the move from traditional single-mode longitudinal designs to mixed-mode designs. Nevertheless, there are concerns that mixing survey modes may affect coefficients of change at the individual level. We investigate the impact of mixing survey modes on estimates of change using a quasi-experimental design implemented in a long-running UK panel study. Two types of comparisons are carried out: single-mode (face-to-face) design versus sequential mixed-mode (Web–face-to-face) design, and Web versus face to face. Across 41 variables, we find no differences in estimates of individual-level change across modes (designs). However, correlations between intercepts and slopes, an estimate of convergence of respondents, were significantly different for most variables, which led to some biases in estimates of change. Applied researchers are encouraged to do sensitivity checks to ensure their results are robust to mode effects.

Abstract
Policy decisions in business and economic fields are often informed by surveys of employees. Many employee surveys use costly interviewer-administered modes to reach this special population. However, certain employee subgroups may be especially hard to reach using these modes. Thus, besides high administration costs, nonresponse bias is a concern. To reduce costs and potential nonresponse bias, some employee surveys have introduced web as part of a sequential mixed-mode design. However, the impact of introducing web on response rates, nonresponse bias, and costs in employee surveys is understudied. The present study addresses this research gap by analyzing a mode design experiment in which employees selected for a national survey in Germany were randomly assigned to a single-mode telephone design or a sequential web-telephone mixed-mode design. The study revealed four main findings. First, introducing the web mode significantly increased the response rate compared to the single-mode design. Second, despite the higher response rate, aggregate nonresponse bias was higher in the mixed-mode design than in the single-mode design. Third, the likelihood of web participation varied across certain employee subgroups, including occupation type and employment contract. Lastly, potential cost savings were evident under the mixed-mode design.

Biological measurements (or biomeasures) are increasingly being collected in large longitudinal
biosocial surveys, enabling researchers to exploit the advantages of social science data with
objective health measures to better understand how health and social behaviour interact over time.
However, not all survey respondents are willing to take part in the biomeasure component of
biosocial surveys, even when the measures are administered by certified medical professionals,
such as nurses. Thus, understanding factors which affect participation in biomeasure collection is
essential for making valid biosocial inferences about the population. Previous research has shown
that interviewer continuity can be useful for optimizing longitudinal survey participation, but it is yet
unknown if nurse continuity impacts the likelihood of participation in biomeasure collection. We
investigated the impact of nurse continuity on nonresponse to biomeasure collection in waves 4
and 6 of the English Longitudinal Study of Ageing (ELSA). Using cross-classified multilevel
models, we find that switching nurses between waves does not negatively impact participation in
biomeasure collection, and sometimes can be beneficial, particularly for previous wave
nonrespondents. The practical implication is that biosocial surveys may not need to employ strict
nurse continuity protocols to maximize participation in subsequent waves of biomeasure data
collection.

Survey researchers are actively seeking powerful auxiliary data sources capable of correcting for possible nonresponse bias in survey estimates of the general population. While several auxiliary data options exist, concerns about their usefulness for addressing nonresponse bias remain. One underutilized—but potentially rich—source of auxiliary data for nonresponse bias adjustment is federal administrative records. While federal records are routinely used to study nonresponse in countries where it is possible to directly link them (via a unique identifier) to population-based samples, such records are not widely used for this purpose in countries which lack a unique identifier to facilitate direct linkage. In this article, we examine the utility of indirectly linked administrative data from a federal employment database for nonresponse bias adjustment in a general population survey in Germany. In short, we find that the linked administrative variables have stronger correlations with the substantive survey variables than do standard paradata variables and that incorporating linked administrative data in nonresponse weighting adjustments reduces relative nonresponse bias to a greater extent than paradata-only weighting adjustments. However, for the majority of weighted survey estimates, including the administrative variables in the weighting adjustment procedure has minimal impact on the point estimates and their variances. We conclude with a general discussion of these findings and comment on the logistical issues associated with this type of linkage relevant to survey practice.

"Administrative records are increasingly being linked to survey records to highten the utility of the survey data. Respondent consent is usually needed to perform exact record linkage; however, not all respondents agree to this request and several studies have found significant differences between consenting and non-consenting respondents on the survey variables. To the extent that these survey variables are related to variables in the administrative data, the resulting administrative estimates can be biased due to non-consent. Estimating non-consent biases for linked administrative estimates is complicated by the fact that administrative records are typically not available for the non-consenting respondents. The present study can overcome this limitation by utilizing a unique data source, the German Panel Study 'Labor Market and Social Security' (PASS), and linking the consent indicator to the administrative records (available for the entire sample). This situation permits the estimation of non-consent biases for administrative variables and avoids the need to link the survey responses. The impact of non-consent bias can be assessed relative to other sources of bias (nonresponse, measurement) for several administrative estimates. The results show that non-consent biases are present for few estimates, but are generally small relative to other sources of bias." (author's abstract)

AbstractThe availability of both survey and non-survey data sources, such as administrative data, social media data, and digital trace data, has grown rapidly over the past decade. With this expansion in data, the statistical, methodological, computational, and ethical challenges around integrating multiple data sources have also grown. This special issue addresses these challenges by highlighting recent innovations and applications in data integration and related topics.

The importance of correctly accounting for complex sampling features when generating finite population inferences based on complex sample survey data sets has now been clearly established in a variety of fields, including those in both statistical and non-statistical domains. Unfortunately, recent studies of analytic error have suggested that many secondary analysts of survey data do not ultimately account for these sampling features when analyzing their data, for a variety of possible reasons (e.g., poor documentation, or a data producer may not provide the information in a public-use data set). The research in this area has focused exclusively on analyses of household survey data, and individual respondents. No research to date has considered how analysts are approaching the data collected in establishment surveys, and whether published articles advancing science based on analyses of establishment behaviors and outcomes are correctly accounting for complex sampling features. This article presents alternative analyses of real data from the 2013 Business Research and Development and Innovation Survey (BRDIS), and shows that a failure to account for the complex design features of the sample underlying these data can lead to substantial differences in inferences about the target population of establishments for the BRDIS.