Does Increasing Mobile Device Coverage Reduce Heterogeneity in Completing Web Surveys on Smartphones?

Gummer, Tobias; Quoß, Franziska; Roßmann, Joss

doi:10.1177/0894439318766836

Cited by 30 publications

(36 citation statements)

References 26 publications

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“…From the 3,030 panelists invited to participate in the survey, 498 were screened out and 2,247 completed the survey, resulting in a completion rate of 89% (Callegaro & DiSogra, 2008). The breakoff rate was 7%, which can be considered rather low, as it was less than half the average breakoff rate in web surveys of similar length, samples, and topic in Germany (16% based on 18 comparable web surveys; Gummer, Quoß, & Roßmann, 2019). Of those who completed the survey, 51% were female, the average age was 45 years, and 34% had a high level of education (university entrance qualification).…”

Section: Methodsmentioning

confidence: 77%

Understanding Respondents’ Attitudes Toward Web Paradata Use

Kunz

Gummer

2019

Social Science Computer Review

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The collection and use of paradata is gaining in importance, especially in web surveys. From a research ethics’ perspective, respondents should be asked for their consent to the collection and use of web paradata. In this context, a positive attitude toward paradata use has been deemed to be a prerequisite for respondents’ willingness to share their paradata. The present study aimed to identify factors affecting respondents’ attitudes toward paradata use. Our findings revealed that adequately informing survey respondents about what paradata are and why they are used was an important determinant of their attitudes toward paradata use. Moreover, we found that respondents with a positive attitude toward the survey were more likely to have a favorable opinion of paradata use. Our findings suggest that a thorough understanding of the factors that contribute to a positive attitude toward paradata use provides the basis for improved paradata consent procedures, which in turn will increase rates of consent to paradata use and help attenuate the risk of consent bias in web surveys.

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Section: Methodsmentioning

confidence: 77%

Understanding Respondents’ Attitudes Toward Web Paradata Use

Kunz

Gummer

2019

Social Science Computer Review

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“…As such, this study focuses on cluster analysis results and bivariate relationships between variables, rather than either focusing on univariate descriptive results or expanding findings to a broader population—which would require more careful weighting to ensure representativeness (Van Acker et al 2014; Gim 2019). While differences between participants reached through online and conventional surveys have become more similar over time, structural reasons remain for online panels to over‐represent towards high‐income and highly educated individuals who are more likely to have access to technologies needed for online surveys (Sterrett et al 2017; Gummer et al 2019; Herzing and Blom 2019).…”

Section: Methodsmentioning

confidence: 99%

Who uses ride‐hailing? Policy implications and evidence from the Greater Toronto and Hamilton Area

Shi

Sweet

2020

Canadian Geographies / Géographies canadiennes

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While many are eager to guide policy decisions on ride‐hailing, understanding the broader social and travel implications hinges on local contexts. Towards providing policy guidance in the Canadian context, this paper explores how mobility sub‐markets are related to ride‐hailing use in the Greater Toronto and Hamilton Area. Using data from a 2018 travel survey, cluster analysis is used to identify four traveller sub‐markets which are compared with ride‐hailing use. The first comprises “multi‐modalists,” who are younger, mobile, educated, and represent the largest group of ride‐hailing users. Second are users in two auto‐oriented markets—“private modalists” and “auto dependents”—who are older and least likely to engage in ride‐hailing. Finally, “low‐mobility travellers” are the most socio‐economically vulnerable and have lower household incomes, lower education attainment, lower likelihoods of being employed, fewer cars per household, and the highest reliance on public transportation among users in the traveller sub‐markets. Nevertheless, this group represents the second largest market for ride‐hailing, which fills a mobility gap in the absence of auto access. Understanding the social and travel behaviour implications from ride‐hailing is important towards crafting context‐appropriate public policy. Results from this study suggest that ride‐hailing is primarily used by those who are already highly mobile (multi‐modalists) and those who are most vulnerable (low‐mobility travellers).

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“…However, there are at least two reasons why this prediction may not hold. There are signs that smartphone ownership and use is starting to level off (Gummer et al, 2018; Pew Research Center, 2018). Secondly, some respondents may choose to use a smartphone for reasons unrelated to their level of comfort and familiarity, for example, it happens to be nearby, they are on the go, or it is their only device.…”

Section: Discussionmentioning

confidence: 99%

“…The substantial increase in smartphone use over the past decade has corresponded with an increase in smartphone use among online survey respondents (see, e.g., Gummer, Quoß, & Roßmann, 2018). Survey researchers studying the implications of this shift away from personal computers (PCs) have mostly focused on response quality (i.e., whether respondents can provide high-quality answers when using mobile devices) and to a lesser extent on response rates by device (for reviews, see Couper, Antoun, & Mavletova, 2017; Link et al, 2014).…”

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confidence: 99%

Factors Affecting Completion Times: A Comparative Analysis of Smartphone and PC Web Surveys

Antoun

Cernat

2019

Social Science Computer Review

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This article compares the factors affecting completion times (CTs) to web survey questions when they are answered using two different devices: personal computers (PCs) and smartphones. Several studies have reported longer CTs when respondents use smartphones than PCs. This is a concern to survey researchers because longer CTs may increase respondent burden and the risk of breakoff. However, few studies have analyzed the specific reasons for the time difference. In this analysis, we analyzed timing data from 836 respondents who completed the same web survey twice, once using a smartphone and once using PC, as part of a randomized crossover experiment in the Longitudinal Internet Studies for the Social Sciences panel. The survey contained a mix of questions (single choice, numeric entry, and text entry) that were displayed on separate pages. We included both page-level and respondent-level factors that may have contributed to the time difference between devices in cross-classified multilevel models. We found that respondents took about 1.4 times longer when using smartphones than PCs. This difference was larger when a page had more than one question or required text entry. The difference was also larger among respondents who had relatively low levels of familiarity and experience using smartphones. Respondent multitasking was associated with slower CTs, regardless of the device used. Practical implications and avenues for future research are discussed.

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Does Increasing Mobile Device Coverage Reduce Heterogeneity in Completing Web Surveys on Smartphones?

Cited by 30 publications

References 26 publications

Understanding Respondents’ Attitudes Toward Web Paradata Use

Understanding Respondents’ Attitudes Toward Web Paradata Use

Who uses ride‐hailing? Policy implications and evidence from the Greater Toronto and Hamilton Area

Factors Affecting Completion Times: A Comparative Analysis of Smartphone and PC Web Surveys

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