Does the use of mobile devices (tablets and smartphones) affect survey quality and choice behaviour in web surveys?

Liebe, Ulf; Glenk, Klaus; Oehlmann, Malte; Meyerhoff, Jürgen

doi:10.1016/j.jocm.2015.02.002

Cited by 40 publications

(34 citation statements)

References 28 publications

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“…More exact comparisons of preferences across the treatments could be employed, for example, through sample matching techniques (e.g. Liebe et al 2015). More generally, however, examining the effects of space on choices in stated preference studies requires further effort that looks at an interplay between inter alia presentation of space in the surveys, cognitive demands on respondents and scenario credibility.…”

Section: Conclusion: Ce Survey Design For Spatial Preferencesmentioning

confidence: 99%

Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

Baďura

Ferrini

Burton

et al. 2019

Environ Resource Econ

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Understanding how the value of environmental goods and services is influenced by their location relative to where people live can help identify the economically optimal spatial distribution of conservation interventions across landscapes. However, capturing these spatial relationships within the confines of a stated preference study has proved challenging. We propose and implement a novel approach to incorporating space within the design and presentation of stated preference choice experiments (CE). Using an investigation of preferences concerning land use change in Great Britain, CE scenarios are presented through individually generated maps, tailored to each respondent's home location. Each choice situation is generated in real time and is underpinned by spatially tailored experimental designs that reflect current British land uses and incorporate locational attributes relating to physical and administrative dimensions of space. To the best of our knowledge, this represents the first CE study to integrate space into both the survey design and presentation of choice tasks in this way. Presented methodology provides means for testing how presentation of spatial information influence stated preferences. We contrast our spatially explicit (mapped) approach with a commonly applied tabular CE approach finding that the former exhibits a number of desirable characteristics relative to the latter.

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Section: Conclusion: Ce Survey Design For Spatial Preferencesmentioning

confidence: 99%

Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

Baďura

Ferrini

Burton

et al. 2019

Environ Resource Econ

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“…Early research on the effects of matrix formatting for online surveys focused mainly on computer users. However, this focus has expanded recently given the rapid spread of surveys completed on mobile devices, with concerns about how small screen size and other mobile-specific factors may negatively impact data quality (Liebe, Glenk, Oehlmann, & Meyerhoff, 2015). Revilla, Toninelli, and Ochoa (2017) directly compared surveys completed on a computer to those completed on optimized and nonoptimized smartphone layouts.…”

Section: Device Typementioning

confidence: 99%

What Is the Best Size for Matrix-Style Questions in Online Surveys?

Grady

Greenspan

Liu

2018

Social Science Computer Review

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Across two studies, we aimed to determine the row and column size in matrix-style questions that best optimizes participant experience and data quality for computer and mobile users. In Study 1 (N ¼ 2,492), respondents completed 20 questions (comprising four short scales) presented in a matrix grid (converted to item-by-item format on mobile phones). We varied the number of rows (5, 10, or 20) and columns (3, 5, or 7) of the matrix on each page. Outcomes included both data quality (straightlining, item skip rate, and internal reliability of scales) and survey experience measures (dropout rate, rating of survey experience, and completion time). Results for row size revealed dropout rate and reported survey difficulty increased as row size increased. For column size, seven columns increased the completion time of the survey, while three columns produced lower scale reliability. There was no interaction between row and column size. The best overall size tested was a 5 Â 5 matrix. In Study 2 (N ¼ 2,570), we tested whether the effects of row size replicated when using a single 20-item scale that crossed page breaks and found that participant survey ratings were still best in the five-row condition. These results suggest that having around five rows or potentially fewer per page, and around five columns for answer options, gives the optimal survey experience, with equal or better data quality, when using matrix-style questions in an online survey. These recommendations will help researchers gain the benefits of using matrices in their surveys with the least downsides of the format.

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“…Several empirical applications rely on the Poe test to establish if there are differences in WTP between samples. See, for example, Liebe et al (2015) or Glenk et al (2019) for methodological applications, and Brouwer et al (2010Brouwer et al ( , 2016 and Knoefel et al (2018) for empirical applications.…”

Section: Wtp Comparisonmentioning

confidence: 99%

Calculating Marginal and Non-marginal Welfare Measures

Mariel

Hoyos

Meyerhoff

et al. 2020

Environmental Valuation With Discrete Choice Experiments

Self Cite

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This chapter focuses on the calculation of marginal and non-marginal welfare measures. It outlines how the calculation of welfare measures is related to the specified model and the assumptions underlying that model. It further describes how the calculation of these measures is affected by the inclusion of preference heterogeneity, including the incorporation of interaction terms to capture observed preference heterogeneity or random parameters to capture unobserved preference heterogeneity. Finally, it discusses how these measures can be aggregated and compared.

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Does the use of mobile devices (tablets and smartphones) affect survey quality and choice behaviour in web surveys?

Cited by 40 publications

References 28 publications

Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

What Is the Best Size for Matrix-Style Questions in Online Surveys?

Calculating Marginal and Non-marginal Welfare Measures

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