The Impact of Nurse Continuity on Biosocial Survey Participation

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

The Need to Account for Complex Sampling Features when Analyzing Establishment Survey Data: An Illustration using the 2013 Business Research and Development and Innovation Survey (BRDIS)

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

Obtaining Record Linkage Consent: Results from a Wording Experiment in Germany

Many sample surveys ask respondents for consent to link their survey information with administrative sources. There is significant variation in how linkage requests are administered and little experimental evidence to suggest which approaches are useful for achieving high consent rates. A common approach is to emphasize the positive benefits of linkage to respondents. However, some evidence suggests that emphasizing the negative consequences of not consenting to linkage is a more effective strategy. To further examine this issue, we conducted a gain-loss framing experiment in which we emphasized the benefit (gain) of linking or the negative consequence (loss) of not linking one’s data as it related to the usefulness of their survey responses. In addition, we explored a sunk-prospective costs rationale by varying the emphasis on response usefulness for responses that the respondent had already provided prior to the linkage request (sunk costs) and responses that would be provided after the linkage request (prospective costs). We found a significant interaction between gain-loss framing and the sunk-prospective costs rationale: respondents in the gain-framing condition consented to linkage at a higher rate than those in the loss-framing condition when response usefulness was emphasized for responses to subsequent survey items. Conversely, the opposite pattern was observed when response usefulness was emphasized for responses that had already been provided: loss-framing resulted in a higher consent rate than the gain-framing, but this result did not reach statistical significance

Chapter 16: Investigating the Use of Nurse Paradata in Understanding Nonresponse to Biological Data Collection. Appendix 16

Appendix 16A: Descriptive statistics for available call records Figure A16A.1 Histograms of wave 2 nurse visit call length by call status with outlier of 691 minutes removed from the ‘any interviewing done’ category Figure A16A.2 Histograms of wave 3 nurse visit call length by call status with outlier of 464 minutes removed from the ‘any interviewing done’ category Figure A16A.3 Bar chart of total number of calls per household at UKHLS wave 2 nurse visit Figure A16A.4 Bar chart of total number of calls per household at UKHLS wave 3 nurse visit Figure A16A.5 Histogram of total nurse visit time in minutes – wave 2 Figure A16A.6 Histogram of total nurse visit time in minutes minus the blood sample modules – wave 2 Appendix 16B: Full Results from Regression Models Corresponding to Tables 16.4-16.12 in Main Body Table A16B.1 Results from cross-classified multilevel logistic regression models for the log-odds of participating in the wave 2 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.4 in main body Table A16B.2 Results from cross-classified logistic regression models of the log-odds of consenting to the blood sample in the wave 2 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.5 in main body Table A16B.3 Results from cross-classified logistic regression models of the log-odds of obtaining the blood sample in the wave 2 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.6 in main body. Table A16.4 Results from cross-classified logistic regression models of the log-odds of participating in the wave 3 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.7 in main body. Table A16B.5 Results from cross-classified logistic regression models of the log-odds of consenting to the blood sample in the wave 3 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.8 in main body. Table A16B.6 Results from cross-classified multilevel logistic regression models of the log-odds of obtaining the blood sample in the wave 3 nurse visit using nurse characteristics and paradata variables. Corresponds to table 16.9 in main body. Table A16B.7 Results from cross-classified logistic regression models of the log-odds of participating in the wave 3 nurse visit using nurse performance indicators. Corresponds to table 16.10 in main body. Table A16B.8 Results from cross-classified logistic regression models of the log-odds of consenting to the blood sample in the wave 3 nurse visit using nurse performance indicators. Corresponds to table 16.11 in main body. Table A16B.9 Results from cross-classified logistic regression models of the log-odds of obtaining the blood sample in the wave 3 nurse visit using nurse performance indicators. Corresponds to table 16.12 in main body. Appendix 16C: Quantile-Quantile plot of nurse residuals for the model of having call records in wave 2 Figure A16C.1 Quantile-Quantile plot of nurse residuals with 95% confidence intervals from the model of having call records in wave 2. The green oval indicates the nurses with above-average call recording and the red oval indicates the nurses with below-average call recording

Investigating the Use of Nurse Paradata in Understanding Nonresponse to Biological Data Collection

The recent collection of biological data in large-scale sample surveys has opened up new possibilities for research into the interactions between physical and social mechanisms in the general population. Whilst the possibilities are undoubtedly exciting, these data can create additional challenges from the viewpoints of both collection and analysis. In particular, the extra burden of biological data collection can lead to increased incidences of nonresponse, potentially affecting the quality of the data and the robustness of results from subsequent analysis. Where the two-stage nurse visit survey design is used, such as in Understanding Society (UKHLS) and the English Longitudinal Study of Ageing (ELSA), the possible effects of the assigned nurse on patterns of nonresponse also need to be considered. In order to address nonresponse to biological data collection, researchers can build response propensity models and use these to adapt future data collection and/or produce post-survey adjustments such as weights. However, variables included in the models must be available for both respondents and nonrespondents in the sample. A recent branch of research concerns the use of new forms of data collected during the survey process, known as paradata, for this purpose. Paradata can include variables such as call histories, response timings and interviewer observations, and may provide a cost-effective way to deal with nonresponse in analyses which use survey data. In this paper, we use paradata collected during the nurse visit in UKHLS to investigate nonresponse to biological data collection and also to examine and explain the effects of the nurse. Preliminary results from UKHLS wave 2 have shown that clustering by nurse is present in responses to three conditional stages: participation in the nurse visit, consent to the blood sample and obtaining the blood sample. When quantifying the nurse effects, we estimated cross-classified multilevel models for the likelihood to respond to each of these stages to take account of the effects of geographical area. The models included a comprehensive set of explanatory variables for the respondents and households as well as data from call records. Given the amount of missing paradata, we also used the availability of call record data to estimate nurse performance indicators that could be used in response propensity models for the following wave of biological data collection. Results indicate that the nurse performance indicators derived from the availability of call record paradata are predictive of the likelihood of a sample member to participate in the first stage of the nurse visit in the following wave, given nurse age and experience. This was not the case for the subsequent two stages (consent to and obtaining the blood sample). This suggests that the recording of call histories by nurses in surveys may be an indication of their performance in the interviewer-type tasks involved in biological data collection, such as making contact and gaining cooperation from sample members. It also shows how these new forms of data may help to explain nurse effects in models of response to biological data collection

Contact Modes and Participation in App-Based Smartphone Surveys: Evidence From a Large-Scale Experiment

Smartphone apps are increasingly being used for population-based survey research. Recruiting people to sign up for an app-based survey is, however, less straightforward compared to traditional surveys, which risks inflating nonresponse as well as the potential for nonresponse bias. By means of an experiment with over 44,000 recently registered job seekers, we present causal evidence on the effects of using different contact modes (email, postal letter, or preannouncement letter and email) on participation rates in an app-based panel survey. Further, using detailed administrative register data, we investigate whether contact modes differentially affect nonresponse bias. We also examine whether the mode of making contact has a lasting effect on panel participation rates and participation rates in momentary assessments collected using the experience sampling method (ESM). Overall, the preannouncement letter and email invitation strategy maximizes participation compared to stand-alone letters and emails, which do not differ significantly in terms of participation rates. Stand-alone letters and the preannouncement approach perform better than emails when it comes to panel participation and submitted ESM episodes

Characteristics of physical measurement consent in a population-based survey of older adults

BACKGROUND: Collecting physical measurements in population-based health surveys has increased in recent years, yet little is known about the characteristics of those who consent to these measurements. OBJECTIVE: To examine the characteristics of persons who consent to physical measurements across several domains, including one’s demographic background, health status, resistance behavior toward the survey interview, and interviewer characteristics. RESEARCH DESIGN, SUBJECTS, AND MEASURES: We conducted a secondary data analysis of the 2006 Health and Retirement Study, a nationally-representative panel survey of older adults aged 50 and older. We performed multilevel logistic regressions on a sample of 7,457 respondents who were eligible for physical measurements. The primary outcome measure was consent to all physical measurements. RESULTS: Seventy-nine percent (unweighted) of eligible respondents consented to all physical measurements. In weighted multilevel logistic regressions controlling for respondent demographics, current health status, survey resistance indicators, and interviewer characteristics, the propensity to consent was significantly greater among Hispanic respondents matched with bilingual Hispanic interviewers, diabetics, and those who visited a doctor in the past 2 years. The propensity to consent was significantly lower among younger respondents, those who have several Nagi functional limitations and infrequently participate in “mildly vigorous” activities, and those interviewed by black interviewers. Survey resistance indicators, such as number of contact attempts and interviewer observations of resistant behavior in prior wave iterations of the HRS were also negatively associated with physical measurement consent. The propensity to consent was unrelated to prior medical diagnoses, including high blood pressure, cancer (excl. skin), lung disease, heart abnormalities, stroke, and arthritis, and matching of interviewer and respondent on race and gender. CONCLUSIONS: Physical measurement consent is not strongly associated with one’s health status, though the findings are somewhat mixed. We recommend that physical measurement results be adjusted for characteristics associated with the likelihood of consent, particularly functional limitations, to reduce potential bias. Otherwise, health researchers should exercise caution when generalizing physical measurement results to persons suffering from functional limitations that may affect their participation

Chapter 7: Statistical Identification of Fraudulent Interviews in Surveys: Improving Interviewer Controls Appendix 7

Table A7A.1 Number of identical response pattern

Interviewer Effects in Biosocial Survey Measurements

From SAGE Publishing via Jisc Publications RouterHistory: epub 2021-03-11Publication status: PublishedIncreasingly surveys are using interviewers to collect objective health measures, also known as biomeasures, to replace or supplement traditional self-reported health measures. However, the extent to which interviewers affect the (im)precision of biomeasurements is largely unknown. This article investigates interviewer effects on several biomeasures collected in three waves of the National Social Life, Health, and Aging Project (NSHAP). Overall, we find low levels of interviewer effects, on average. This nevertheless hides important variation with touch sensory tests being especially high with 30% interviewer variation, and smell tests and timed balance/walk/chair stands having moderate interviewer variation of around 10%. Accounting for contextual variables that potentially interact with interviewer performance, including housing unit type and presence of a third person, failed to explain the interviewer variation. A discussion of these findings, their potential causes, and their implications for survey practice is provided

Measurement equivalence in probability and nonprobability online panels

Nonprobability online panels are commonly used in the social sciences as a fast and inexpensive way of collecting data in contrast to more expensive probability-based panels. Given their ubiquitous use in social science research, a great deal of research is being undertaken to assess the properties of nonprobability panels relative to probability ones. Much of this research focuses on selection bias, however, there is considerably less research assessing the comparability (or equivalence) of measurements collected from respondents in nonprobability and probability panels. This article contributes to addressing this research gap by testing whether measurement equivalence holds between multiple probability and nonprobability online panels in Australia and Germany. Using equivalence testing in the Confirmatory Factor Analysis framework, we assessed measurement equivalence in six multi-item scales (three in each country). We found significant measurement differences between probability and nonprobability panels and within them, even after weighting by demographic variables. These results suggest that combining or comparing multi-item scale data from different sources should be done with caution. We conclude with a discussion of the possible causes of these findings, their implications for survey research, and some guidance for data users.publishedVersio