Response Burden: What Is It and What Predicts It?

2019 ◽  
pp. 193-212
Author(s):  
Ting Yan ◽  
Scott Fricker ◽  
Shirley Tsai
Keyword(s):  
Response Burden

Examining Response Categories: Does Minimizing Response Burden Maintain the Instrument?

2013 ◽  
Author(s):  
Anneliese C. Bolland ◽  
John M. Bolland ◽  
Sara E. Tomek ◽  
Heather M. Moore
Keyword(s):  
Response Burden

Measuring the response burden of official statistical surveys for businesses

2015 ◽  
Vol 9 (1) ◽  
pp. 59-71 ◽  
Author(s):  
Daniel Vorgrimler ◽  
Gorja Bartsch ◽  
Florian Spengler ◽  
Daniel Kuehnhenrich
Keyword(s):  
Statistical Surveys ◽  
Response Burden

scholarly journals Is There More Than the Answer to the Question? Device Use and Completion Time as Indicators for Selectivity Bias and Response Convenience in Online Surveys

2021 ◽  
pp. 309-324
Author(s):  
Jean Philippe Décieux
Keyword(s):  
Panel Study ◽  
Auxiliary Information ◽  
Specific Response ◽  
Online Surveys ◽  
Data Set ◽  
Life Trajectories ◽  
Specific Selectivity ◽  
Meta Information ◽  
Device Use ◽  
Response Burden

AbstractThe main objective of the German Emigration and Remigration Panel Study (GERPS) is to establish a longitudinal data set that provides information on life trajectories of international migrants. However, a large amount of paradata were also collected in order to obtain meta-information on respondents’ survey participation. This auxiliary information can help to optimize data quality at all stages of the survey process. By continuing the existing discussion in the field of online surveys, this chapter pursues a twofold objective: it reflects device usage (mobile vs. computer) and elucidates determinants of device choice. In particular, it analyses whether selectivity effects due to respondent’s device choices bias the sample. Moreover, this chapter investigates differences in response time between devices to detect differences in response burden. The analysis of response burden differences by device is an important issue, since an increased device-specific response burden can be a predictor of actual and further panel dropouts. In both device-specific selectivity and survey burden, only slight differences were found between mobile and desktop devices. Using these data, the following paper addresses the need to analyse potential sources of survey error and provides evidence that GERPS data do not appear to contain noteworthy bias attributed to device usage.

scholarly journals Association of Herpesvirus Response Burden with Long-Term Mortality Differs Between Older Males and Females

2020 ◽  
Author(s):  
Timo E Strandberg ◽  
Mikko Seppänen ◽  
Kaisu H Pitkälä ◽  
Mika Kivimäki ◽  
Pentti J Tienari
Keyword(s):  
Immune Responses ◽  
Cox Regression ◽  
Total Mortality ◽  
List Type ◽  
Baseline Serum ◽  
Bacterial Response ◽  
Older Males ◽  
Response Burden ◽  
Long Term Mortality

AbstractBackgroundSex-specific immune responses may contribute to variable vulnerability for Covid-19 between females and males. We tested whether there is a long-term mortality difference between sexes for other microbes (viral and bacterial) response burden among older people.MethodsSeven-year follow-up study consisted of 382 home-dwelling people aged 75-90 years (65.2% females) with a history cardiovascular disease. At baseline, serum immunoglobulin G antibodies were assayed against herpesviruses (CMV, HSV-1 and HSV-2) and bacteria (Chlamydophila pneumoniae, Mycoplasma pneumonia, and Helicobacter pylori). Titers were summed up as herpes (HB) or bacterial response burden (BB) and divided into tertiles. Hazard ratios (HR) of total mortality with 95% CIs were calculated using Cox regression.ResultsThe overall HB was lower and BB higher among males than females (P<0.001). There was a significant sex/HB (P=0.01) and sex/BB (P=0.03) interaction with mortality. Multivariable-adjusted (age, body mass index, C-reactive protein, and comorbidity index) mortality HRs for increasing HB sex-specific tertiles were 1.0 (reference), 1.34 (95% CI 0.62-2.88), and 2.66 (1.25-5.64) for males and 1.0, 1.30 (0.76-2.21), and 1.30 (0.77-2.22) for females. The significant age-adjusted association between BB and mortality in males attenuated after multivariable adjustments, HR (top-vs-bottom tertile) 1.74 (0.93-3.25). In females, no association with BB was observed. Using HB and BB as continuous variables supported the findings with tertiles.ConclusionsAlthough being lower in older males than females, higher Herpesviridae response burden was associated with increased 7-year mortality risk among males, not among females. Immune responses to common microbes may contribute to sex differences in longevity and mortality.Key points-Gender differences in vulnerability during Covid-19 has increased interested in sex-related responses to infections-We used IgG titers of Herpesviridae and bacteria as surrogate markers for variably recurrent reactivation-Although Herpesviridae response burden was generally lower among males than females, within sexes higher Herpesviridae burden strongly predicted 7-year mortality among males but not females-Long-term virus burden, like Herpesviridae, may partly explain shorter longevity and higher mortality among males with weaker immune systems

scholarly journals Response Burden Management for Establishment Surveys at Four National Statistical Institutes

2018 ◽  
Vol 34 (2) ◽  
pp. 397-418 ◽  
Author(s):  
Deirdre Giesen ◽  
Mario Vella ◽  
Charles F. Brady ◽  
Paul Brown ◽  
Daniela Ravindra ◽  
...  
Keyword(s):  
New Zealand ◽  
Essential Element ◽  
Census Bureau ◽  
Official Statistics ◽  
Internal Resources ◽  
Statistics Canada ◽  
The U.S ◽  
Response Burden

Abstract Managing response burden is key to ensuring an ongoing and efficient supply of fit-forpurpose data. While statistical organizations use multi-faceted approaches to achieve this, response burden management has become an essential element of the strategy used by the U.S. Census Bureau, Statistics New Zealand, Statistics Canada, and Statistics Netherlands. Working in collaboration with respondents, with internal resources dedicated to provide customized approaches for large respondents and with other stakeholders (constituency representatives, associations, etc.) response burden management endeavors to minimize burden and educate stakeholders on the benefit of official statistics. The role continues to evolve with important initiatives regarding the compilation of burden metrics, improvements to existing tracking tools, and an expanded communication role.

Using Only Numeric Labels Instead of Verbal Labels: Stripping Rating Scales to Their Bare Minimum in Web Surveys

2020 ◽  
pp. 089443932095176
Author(s):  
Tobias Gummer ◽  
Tanja Kunz
Keyword(s):  
Rating Scales ◽  
Rating Scale ◽  
Cognitive Effort ◽  
Web Surveys ◽  
Cognitive Response ◽  
Design Elements ◽  
Verbal Labels ◽  
Screen Space ◽  
First Time ◽  
Response Burden

With the increasing use of smartphones in web surveys, considerable efforts have been devoted to reduce the amount of screen space taken up by questions. An emerging stream of research in this area is aimed at optimizing the design elements of rating scales. One suggestion that has been made is to completely abandon verbal labels and use only numeric labels instead. This approach deliberately shifts the task of scale interpretation to the respondents and reduces the information given to them with an intention to reduce their response burden while still preserving the scale meaning. Following prior research, and by drawing on the established model of the cognitive response process, we critically tested these assumptions. Based on a web survey experiment, we found that omitting verbal labels and using only numeric labels instead pushed respondents to focus their responses on the endpoints of a rating scale. Moreover, drawing on response time paradata, we showed that their response burden was not reduced when presented with only numeric labels; quite the opposite was the case, especially when respondents answered the scale with only numeric labels for the first time, which seemed to entail additional cognitive effort. Based on our findings, we advise against using only numeric labels for rating scales in web surveys.

Sign in / Sign up

Export Citation Format

Share Document