
Despite the increasing use of panel surveys, little is known about the differences in data quality across panels.The aim of this study was to characterize panel survey companies and their respondents based on (1) the timeliness of response by panelists, (2) the reliability of the demographic information they self-report, and (3) the generalizability of the characteristics of panelists to the US general population. A secondary objective was to highlight several issues to consider when selecting a panel vendor.We recruited a sample of US adults from 7 panel vendors using identical quotas and online surveys. All vendors met prespecified inclusion criteria. Panels were compared on the basis of how long the respondents took to complete the survey from time of initial invitation. To validate respondent identity, this study examined the proportion of consented respondents who failed to meet the technical criteria, failed to complete the screener questions, and provided discordant responses. Finally, characteristics of the respondents were compared to US census data and to the characteristics of other panels.Across the 7 panel vendors, 2% to 9% of panelists responded within 2 days of invitation; however, approximately 20% of the respondents failed the screener, largely because of the discordance between self-reported birth date and the birth date in panel entry data. Although geographic characteristics largely agreed with US Census estimates, each sample underrepresented adults who did not graduate from high school and/or had annual incomes less than US $15,000. Except for 1 vendor, panel vendor samples overlapped one another by approximately 20% (ie, 1 in 5 respondents participated through 2 or more panel vendors).The results of this head-to-head comparison provide potential benchmarks in panel quality. The issues to consider when selecting panel vendors include responsiveness, failure to maintain sociodemographic diversity and validated data, and potential overlap between panels.
Internet (mesh), Adult, 330, Computer applications to medicine. Medical informatics, R858-859.7, 08 Information and Computing Sciences (for), 11 Medical and Health Sciences (for), Medical and Health Sciences, Medical Informatics (science-metrix), Health Services and Systems, Information and Computing Sciences, Health Sciences, Humans, selection bias, 17 Psychology and Cognitive Sciences (for), Data Collection (mesh), 42 Health Sciences (for-2020), Humans (mesh), Original Paper, Internet, 4203 Health services and systems (for-2020), Data Collection, Psychology and Cognitive Sciences, community surveys, 300, sampling bias, United States, 4203 Health Services and Systems (for-2020), survey methods, Adult (mesh), Public aspects of medicine, RA1-1270, data sources, Medical Informatics, United States (mesh)
Internet (mesh), Adult, 330, Computer applications to medicine. Medical informatics, R858-859.7, 08 Information and Computing Sciences (for), 11 Medical and Health Sciences (for), Medical and Health Sciences, Medical Informatics (science-metrix), Health Services and Systems, Information and Computing Sciences, Health Sciences, Humans, selection bias, 17 Psychology and Cognitive Sciences (for), Data Collection (mesh), 42 Health Sciences (for-2020), Humans (mesh), Original Paper, Internet, 4203 Health services and systems (for-2020), Data Collection, Psychology and Cognitive Sciences, community surveys, 300, sampling bias, United States, 4203 Health Services and Systems (for-2020), survey methods, Adult (mesh), Public aspects of medicine, RA1-1270, data sources, Medical Informatics, United States (mesh)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 117 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
