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With the rise of mobile surveys comes the need for shorter questionnaires. We investigate the modularization of an existing questionnaire in the Longitudinal Internet Study for the Social Sciences (LISS) Panel in the Netherlands. We randomly divided respondents into a normal length survey condition, a condition where the same survey was split into 3 parts, and a condition where the survey was split into 10 parts. Respondents received the parts consecutively at regular intervals over a 1-month period. We discuss response rates, data quality measures, and respondents' evaluation of the questionnaire. Our results indicate higher start rates when the survey is cut into smaller parts but also higher dropout rates. However, the fraction of missing information is lower in the 3- and 10-part conditions. More respondents use their mobile phone for survey completion when the survey is shorter. We find fewer item missings and satisficing in shorter surveys. We find no effect on neutral and extreme responding nor on estimates of the validity of answers. People with low and high education and young and old evaluate shorter surveys better than the normal length survey. [ABSTRACT FROM AUTHOR]

This study explores some features of slider bars in the context of a multi-device web survey. Using data collected among the students of the University of Trento in 2015 and 2016 by means of two web surveys (N = 6,343 and 4,124) including two experiments, we investigated the effect of the initial position of the handle and the presence of numeric labels on answers provided using slider bars. It emerged that the initial position of the handle affected answers and that the number of rounded scores increased with numeric feedback. Smartphone respondents appeared more sensitive to the initial position of the handle but also less affected by the presence of numeric labels resulting in a lower tendency to rounding. Yet, outcomes on anchoring were inconclusive. Overall, no relevant differences have been detected between tablet and PC respondents. Understanding to what extent interactive and engaging tools such as slider bars can be successfully employed in multi-device surveys without affecting data quality is a key challenge for those who want to exploit the potential of web-based and multi-device data collection without undermining the quality of measurement. [ABSTRACT FROM AUTHOR]

Mobile hydroacoustic surveys are routinely used to estimate pelagic fish abundance. In the Great Lakes, alewife are commonly surveyed with mobile hydroacoustics, however, their behavior often has them associated with epilimnetic habitats which increases the potential for vessel avoidance to bias hydroacoustic observations. Abundance estimates from mobile hydroacoustic surveys are typically made using depth and size thresholds to classify targets to species. In hydroacoustic surveys, fish can appear to be smaller when oriented off horizontal axis, as is common with fish displaying vessel avoidance behaviour. This presents a problem where alewives that are diving may appear too small to be correctly classified. We compared alewife swimming behaviours and target strength distributions between stationary up-looking and mobile down-looking hydroacoustics to quantify how vessel avoidance may bias hydroacoustic observations. Alewives from the mobile survey were found to be diving away from the surface, moving faster and in more linear paths than the undisturbed fish from the stationary survey. In the mobile survey, alewives were observed at smaller target strength values than would be expected if boat avoidance was not occurring. Our results suggest that alewife behavioral changes associated with vessel avoidance affect the number and size of targets classified as alewife from mobile hydroacoustic surveys in Lake Ontario. [ABSTRACT FROM AUTHOR]

To mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S., where significant emissions were found previously, mobile measurements were performed in 12 cities across eight countries. The surveyed cities range from medium size, like Groningen, NL, to large size, like Toronto, CA, and London, UK. Furthermore, this survey spanned across European regions from Barcelona, ES, to Bucharest, RO. The joint analysis of all data allows us to focus on general emission behavior for cities with different infrastructure and environmental conditions. We find that all cities have a spectrum of small, medium, and large methane sources in their domain. The emission rates found follow a heavy-tailed distribution, and the top 10% of emitters account for 60-80% of total emissions, which implies that strategic repair planning could help reduce emissions quickly. Furthermore, we compare our findings with inventory estimates for urban natural gas-related methane emissions from this sector in Europe. While cities with larger reported emissions were found to generally also have larger observed emissions, we find clear discrepancies between observation-based and inventory-based emission estimates for our 12 cities.

Mixed-mode surveys have been around since the late 1980s. In the past thirty years, major changes in technology and society influenced and changed data collection and survey methodology. However, in those years, mixed-mode strategies remained part of the daily survey practice, although the type of mix implemented followed the changes in technology and data collection methods. In this paper, I summarize the state of the art in traditional mixed-mode surveys and discuss implications for mixed device surveys.

This paper shows that computer users and smartphone users taking part in a web survey optimized for smartphones give responses of almost the same quality. Combining a design of one question in each page and innovative page navigation methods, we can get high quality data by both computer and smartphone users. The two groups of users are also compared with regard to their precisely measured item response times. The analysis shows that using a smartphone instead of a computer increases about 20% the geometric mean of item response times. The data analyzed in this paper were collected by a smartphone-friendly web survey. All question texts are short and the response buttons are large and easy to use. As a result, there are no significant interactions between smartphone use and either the length of the question or the age of the respondent. Thus, the longer response times among smartphone users should be explored in other causes, such as the likelihood of smartphone users being distracted by their environment.

The number of satellites visible to a Global Navigation Satellite System (GNSS) receiver is important for high accuracy surveys. To aid with this, there are software packages capable of predicting GNSS visibility at any location of the globe at any time of day. These prediction packages operate by using regularly updated almanacs containing positional data for all navigation satellites; however, one issue that restricts their use is that most packages assume that there are no obstructions on the horizon. In an attempt to improve this, certain planning packages are now capable of modelling simple obstructions whereby portions of the horizon visible from one location can be blocked out, thereby simulating buildings or other vertical structures. While this is useful for static surveys, it is not applicable for dynamic surveys when the GNSS receiver is in motion. This problem has been tackled in the past by using detailed, high-accuracy building models and designing novel methods for modelling satellite positions using GNSS almanacs, which is a time-consuming and costly approach. The solution proposed in this paper is to use a GIS to combine existing, freely available GNSS prediction software to predict pseudo satellite locations, incorporate a 2.5D model of the buildings in an area created with national mapping agency 2D vector mapping and low density elevation data to minimise the need for a full survey, thereby providing savings in terms of cost and time. Following this, the ESRI ArcMap viewshed tool was used to ascertain what areas exhibit poor GNSS visibility due to obstructions over a wide area, and an accuracy assessment of the procedure was made.

Abstract: Bigheaded carp (Hypophthalmichthys spp.) occur throughout much of the Mississippi Basin, USA. Efforts to control the spread of these invasive species require information on their spatial ecology, though sampling is hindered by their broad extent, habitat tolerances, and species‐specific behaviour. Mobile hydroacoustics was used to quantify habitat and depth use of bigheaded carp over four years in the heavily invaded Lower Illinois River, a major Mississippi tributary and potential dispersal pathway to the Great Lakes. Horizontally oriented transducers (combined with capture gear for species designation) enabled sampling of the main habitat features in this large flood plain river. Silver carp (Hypophthalmichthys molitrix) were dominant over bighead carp (Hypophthalmichthys nobilis) at all but one site, although habitat use was similar for both species. Densities were highest in lotic backwaters, followed by lentic backwaters and nearshore main channel, with lowest densities in the mid main channel. Bigheaded carp size and species composition were independent of habitat type. Depth associations were similar for both species, with average occurrence at 2.5–3.5 m in the main channel and 1–2 m in backwaters. However, depth relative to the river bed was largely similar across habitat types. Bigheaded carp density and depth use in the main channel were linked non‐linearly to river discharge and water temperature, respectively; densities were reduced during high discharge, whereas depth use became shallower at higher temperatures. Density–hydrology trends were less apparent in backwaters. These findings highlight critical aspects of bigheaded carp spatial ecology that will facilitate effective management in invaded and at‐risk ecosystems. [ABSTRACT FROM AUTHOR]

In an experiment dealing with the use of personal computer, tablet, or mobile, scale points (up to 5, 7, or 11) and response formats (bars or buttons) are varied to examine differences in mean scores and nonresponse. The total number of “not applicable” answers does not vary significantly. Personal computer has the lowest item nonresponse, followed by mobile and tablet, and a lower mean score than for mobile. Slider bars showed lower mean scores and more nonresponses than buttons, indicating that they are more prone to bias and difficult in use. Sider bars, which work with a drag-and-drop principle, perform worse than visual analogue scales working with a point-and-click principle and buttons. Five-point scales have more nonresponses than eleven-point scales. Respondents evaluate 11-point scales more positively than shorter scales. [ABSTRACT FROM AUTHOR]

The number of satellites visible to a Global Navigation Satellite System (GNSS) receiver is important for high accuracy surveys. To aid with this, there are software packages capable of predicting GNSS visibility at any location of the globe at any time of day. These prediction packages operate by using regularly updated almanacs containing positional data for all navigation satellites; however, one issue that restricts their use is that most packages assume that there are no obstructions on the horizon. In an attempt to improve this, certain planning packages are now capable of modelling simple obstructions whereby portions of the horizon visible from one location can be blocked out, thereby simulating buildings or other vertical structures. While this is useful for static surveys, it is not applicable for dynamic surveys when the GNSS receiver is in motion. This problem has been tackled in the past by using detailed, high-accuracy building models and designing novel methods for modelling satellite positions using GNSS almanacs, which is a time-consuming and costly approach. The solution proposed in this paper is to use a GIS to combine existing, freely available GNSS prediction software to predict pseudo satellite locations, incorporate a 2.5D model of the buildings in an area created with national mapping agency 2D vector mapping and low density elevation data to minimise the need for a full survey, thereby providing savings in terms of cost and time. Following this, the ESRI ArcMap viewshed tool was used to ascertain what areas exhibit poor GNSS visibility due to obstructions over a wide area, and an accuracy assessment of the procedure was made. [ABSTRACT FROM AUTHOR]

In this paper I review three key technology-related trends: 1) big data, 2) non-probability samples, and 3) mobile data collection. I focus on the implications of these trends for survey research and the research profession. With regard to big data, I review a number of concerns that need to be addressed, and argue for a balanced and careful evaluation of the role that big data can play in the future. I argue that these developments are unlikely to replace transitional survey data collection, but will supplement surveys and expand the range of research methods. I also argue for the need for the survey research profession to adapt to changing circumstances.

The dramatic rise of smartphones has profound implications for survey research. Namely, can smartphones become a viable and comparable device for self-administered surveys? The current study is based on approximately 1,500 online U.S. panelists who were smartphone users and who were randomly assigned to the mobile app or online computer mode of a survey. Within the survey, we embedded several experiments that had been previously tested in other modes (mail, PC web, mobile web). First, we test whether responses in the mobile app survey are sensitive to particular experimental manipulations as they are in other modes. Second, we test whether responses collected in the mobile app survey are similar to those collected in the online computer survey. Our mobile survey experiments show that mobile survey responses are sensitive to the presentation of frequency scales and the size of open-ended text boxes, as are responses in other survey modes. Examining responses across modes, we find very limited evidence for mode effects between mobile app and PC web survey administrations. This may open the possibility for multimode (mobile and online computer) surveys, assuming that certain survey design recommendations for mobile surveys are used consistently in both modes. [ABSTRACT FROM PUBLISHER]

Mobile technology offers a promising means to collect survey data, though the factors that influence people's willingness to participate in mobile surveys and their actual participation remain unknown. To identify these factors, this study considers six conceptually distinct influences that may relate to the propensity to participate in mobile surveys. Some of them affect technology acceptance and usage of (mobile) technology in general; another set comes from studies of participation in computer-assisted surveys. The proposed unified framework encompasses utilitarian, affective, hedonic, social, self-expressive, and trust-related factors. An empirical study suggests that this framework explains the intention to participate and actual participation well, though of the six factors, hedonic, affective, self-expressive, and trust-related ones are most influential. Utilitarian aspects and beliefs about perceived social pressure to participate do not play significant roles. The authors discuss the practical implications of these results and outline some further research avenues. [ABSTRACT FROM AUTHOR]

Background: Health care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients., Objective: The aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery., Methods: The study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires., Results: A total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions through an automated bidirectional SMS text messaging system (n=64). Gender, age, number of comorbidities, and opioid prescriptions were similar across messaging arms. Patients receiving the mobile survey were more likely to have had a knee-related surgery (n=50, 83.3% vs n=40, 62.5%; P=.02) but less likely to have had an invasive procedure (n=26, 41.3% vs n=39, 60.9%; P=.03). Overall engagement over the immediate postoperative period was similar. Prolonged engagement for patients taking opioids past postoperative day 4 was higher in the mobile survey arm at day 7 (18/19, 94.7% vs 9/16, 56.3%). Patients with more invasive procedures showed a trend toward being responsive at day 4 as compared to not responding (n=41, 59.4% vs n=24, 41.4%; P=.05)., Conclusions: As mobile patient engagement becomes more common in health care, testing the various options to engage patients to gather data is crucial to inform future care and research. We found that bidirectional SMS text messaging and mobile surveys were comparable in response and engagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time., Trial Registration: ClinicalTrials.gov NCT03532256; https://clinicaltrials.gov/ct2/show/NCT03532256., (©Anish K Agarwal, Zarina S Ali, Frances Shofer, Ruiying Xiong, Jessica Hemmons, Evan Spencer, Dina Abdel-Rahman, Brian Sennett, Mucio K Delgado. Originally published in JMIR Formative Research (https://formative.jmir.org), 17.03.2022.)

Background: Personal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky., Objective: We explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky., Methods: A total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio., Results: OpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher's perspective, and hindering practicality in the field., Conclusions: OpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations., (©Katherine S Eddens, Jesse M Fagan, Tom Collins. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.06.2017.)

Background: We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment., Objective: Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps., Methods: Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information., Results: The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects., Conclusions: The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes.