Testing the Use of Voice Input in a Smartphone Web Survey

Revilla, Mélanie; Couper, Mick P.; Bosch, Oriol J.; Asensio, Marc

doi:10.1177/0894439318810715

Cited by 31 publications

(32 citation statements)

References 21 publications

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“…Results from previous literature suggest that using VI in web surveys could lead to improvements but also that a number of challenges remain. In particular, what seems to be the most problematic but also the most promising option (Revilla, Couper, Bosch, & Asensio, 2018) is voice recording (Android): Indeed, it leads to very high nonresponse and significantly lower survey evaluation, but also to longer and more elaborated answers. Therefore, in this article, we focus on voice recording in a smartphone survey (Android only) and focus on improving the implementation of this tool, building on the experiment of .…”

Section: Goals and Hypothesesmentioning

confidence: 99%

Improving the Use of Voice Recording in a Smartphone Survey

Revilla

Couper

2019

Social Science Computer Review

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More and more respondents are answering web surveys using mobile devices. Mobile respondents tend to provide shorter responses to open questions than PC respondents. Using voice recording to answer open-ended questions could increase data quality and help engage groups usually underrepresented in web surveys. Revilla, Couper, Bosch, and Asensio showed that in particular the use of voice recording still presents many challenges, even if it could be a promising tool. This article reports results from a follow-up experiment in which the main goals were to (1) test whether different instructions on how to use the voice recording tool reduce technical and understanding problems, and thereby reduce item nonresponse while preserving data quality and the evaluation of the tool; (2) test whether nonresponse due to context can be reduced by using a filter question, and how this affects data quality and the tool evaluation; and (3) understand which factors affect nonresponse to open-ended questions using voice recording, and if these factors also affect data quality and the evaluation of the tool. The experiment was implemented within a smartphone web survey in Spain focused on Android devices. The results suggest that different instructions did not affect nonresponse to the open questions and had little effect on data quality for those who did answer. Introducing a filter to ensure that people were in a setting that permits voice recording seems useful. Despite efforts to reduce problems, a substantial proportion of respondents are still unwilling or unable to answer open questions using voice recording.

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Section: Goals and Hypothesesmentioning

confidence: 99%

Improving the Use of Voice Recording in a Smartphone Survey

Revilla

Couper

2019

Social Science Computer Review

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“…The supplemental material for this article is available online. Revilla et al 2020). This line of research will certainly be extended to cognitive probes in the future, too.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 96%

Open-ended versus Closed Probes: Assessing Different Formats of Web Probing

Neuert

Meitinger

Behr

2021

Sociological Methods & Research

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The method of web probing integrates cognitive interviewing techniques into web surveys and is increasingly used to evaluate survey questions. In a usual web probing scenario, probes are administered immediately after the question to be tested (concurrent probing), typically as open-ended questions. A second possibility of administering probes is in a closed format, whereby the response categories for the closed probes are developed during previously conducted qualitative cognitive interviews. Using closed probes has several benefits, such as reduced costs and time efficiency, because this method does not require manual coding of open-ended responses. In this article, we investigate whether the insights gained into item functioning when implementing closed probes are comparable to the insights gained when asking open-ended probes and whether closed probes are equally suitable to capture the cognitive processes for which traditionally open-ended probes are intended. The findings reveal statistically significant differences with regard to the variety of themes, the patterns of interpretation, the number of themes per respondent, and nonresponse. No differences in number of themes across formats by sex and educational level were found.

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“…Smartphones offer many opportunities for innovative data collection that can be used for studying social science research questions as well as for methodological research. Examples include collecting expenditure data with receipt scanning apps (Ja ¨ckle et al, 2019), offering voice input options for answering open-ended questions (Revilla et al, 2020;Revilla & Couper, 2019), and asking respondents to take photographs for supplementing survey responses (Bosch et al, 2019a). Particularly, smartphone sensors, such as accelerometer, gyroscope, and GPS, allow the collection of detailed information that can be used to enrich and augment web survey data from smartphones.…”

Section: Introductionmentioning

confidence: 99%

“…If a person moves or walks, they are creating acceleration (He et al, 2016), which is detected by the smartphone that is commonly worn on the body (e.g., in the pocket). This situation can be applied to respondents that have the smartphone in their hands and complete a web survey, referring to a "respondent-device" link (Ho ¨hne, Revilla, et al, 2020;Ho ¨hne & Schlosser, 2019). To put it differently, respondents' motions are detectable by the acceleration sensor of smartphones, allowing researchers to classify smartphone respondents on the basis of their motion levels (i.e., acceleration).…”

Section: Introductionmentioning

confidence: 99%

Completion Conditions and Response Behavior in Smartphone Surveys: A Prediction Approach Using Acceleration Data

Kern

Höhne

Schlosser

et al. 2020

Social Science Computer Review

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This study utilizes acceleration data from smartphone sensors to predict motion conditions of smartphone respondents. Specifically, we predict whether respondents are moving or nonmoving on a survey page level to learn about distractions and the situational conditions under which respondents complete smartphone surveys. The predicted motion conditions allow us to (1) estimate the proportion of smartphone respondents who are moving during survey completion and (2) compare the response behavior of moving and nonmoving respondents. Our analytical strategy consists of two steps. First, we use data from a lab experiment that systematically varied motion conditions of smartphone respondents and train a prediction model that is able to accurately infer respondents’ motion conditions based on acceleration data. Second, we use the trained model to predict motion conditions of respondents in two cross-sectional surveys in order to compare response behavior of respondents with different motion conditions in a field setting. Our results indicate that active movement during survey completion is a relatively rare phenomenon, as only about 3%–4% of respondents were predicted as moving in both cross-sectional surveys. When comparing respondents based on their predicted motion conditions, we observe longer completion times of moving respondents. However, we observe little differences when comparing moving and nonmoving respondents with respect to indicators of superficial responding, indicating that moving during survey completion does not pose a severe threat to data quality.

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Testing the Use of Voice Input in a Smartphone Web Survey

Cited by 31 publications

References 21 publications

Improving the Use of Voice Recording in a Smartphone Survey

Improving the Use of Voice Recording in a Smartphone Survey

Open-ended versus Closed Probes: Assessing Different Formats of Web Probing

Completion Conditions and Response Behavior in Smartphone Surveys: A Prediction Approach Using Acceleration Data

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