Does the Recruitment of Offline Households Increase the Sample Representativeness of Probability-Based Online Panels? Evidence From the German Internet Panel
Options
BORIS DOI
Date of Publication
2017
Publication Type
Article
Division/Institute
Contributor
Blom, Annelies G. | |
Herzing, Jessica | |
Cornesse, Carina | |
Sakshaug, Joseph W. | |
Krieger, Ulrich | |
Bossert, Dayana |
Series
Social science computer review
ISSN or ISBN (if monograph)
0894-4393
Publisher
Sage
Language
English
Publisher DOI
Description
The past decade has seen a rise in the use of online panels for conducting survey research. However, the popularity of online panels, largely driven by relatively low implementation costs and high rates of Internet penetration, has been met with criticisms regarding their ability to accurately represent their intended target populations. This criticism largely stems from the fact that (1) non-Internet (or offline) households, despite their relatively small size, constitute a highly selective group unaccounted for in Internet panels, and (2) the preeminent use of nonprobability-based recruitment methods likely contributes a self-selection bias that further compromises the representativeness of online panels. In response to these criticisms, some online panel studies have taken steps to recruit probability-based samples of individuals and providing them with the means to participate online. Using data from one such study, the German Internet Panel, this article investigates the impact of including offline households in the sample on the representativeness of the panel. Consistent with studies in other countries, we find that the exclusion of offline households produces significant coverage biases in online panel surveys, and the inclusion of these households in the sample improves the representativeness of the survey despite their lower propensity to respond.
File(s)
File | File Type | Format | Size | License | Publisher/Copright statement | Content | |
---|---|---|---|---|---|---|---|
BlomEtAl_SSCR_OnOffliner_2016.pdf | text | Adobe PDF | 608.93 KB | publisher | published |