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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.

In this paper we discuss the implications of using mobile devices for online survey completion. With more and more people accessing online surveys on mobile devices, online surveys need to be redesigned in order to be able to meet the characteristics of mobile device usage, such as small screens and short messaging. We discuss mobile friendly design by focussing on survey layout, the length of the survey, special features, and the decision of making the survey app or browser based. Further, we discuss the different sensors that can be used to augment or replace survey questions, and respondents’ willingness to share sensor data. We end with three examples of surveys conducted by Statistics Netherlands, where sensors are used for active and passive measurement in mobile surveys.

Smartphones enable passive collection of sensor data alongside survey participation. Location data add context to people’s reports about their time use. In addition, linking global positioning system data to self-reported time use surveys (TUSs) can be valuable for understanding how people spend their time. This article investigates whether and how passive collection of geographical locations (coordinates) proves useful for deriving respondents’ functional locations. Participants of the ongoing Children of Immigrants Longitudinal Survey in the Netherlands were invited to participate in a TUS administered with a smartphone app that also unobtrusively tracked respondents’ locations. Respondents reported their activities per 10-min interval in a smartphone diary app ( n = 1,339) and shared their geographical location data ( n = 1,264). The correspondence between the functional locations derived from the time use data and those derived from the geographical location data was assessed by calculating the percentage of intervals in which both measures are similar. Overall, results show that home locations can be automatically assigned reliably but that respondent information is required to reliably assign work or school locations. In addition, location tracking data contain many measurement errors, making it difficult to record valid locations. Multilevel models show that the variability in correct classifications is intrapersonal and largely predicted by phone type, which determines location measurement frequency.

The increasing use of smartphones opens up opportunities for novel ways of survey data collection, but also poses new challenges. Collecting more and different types of data means that studies can become increasingly intrusive. We risk over-asking participants, leading to nonresponse. This study documents nonresponse and nonresponse bias in a smartphone-only version of the Dutch Time Use Survey (TUS). Respondents from the Dutch LISS panel were asked to perform five sets of tasks to complete the whole TUS: 1) accept an invitation to participate in the study and install an app, 2) fill out a questionnaire on the web, 3) participate in the smartphone time use diary on their smartphone, 4) answer pop-up questions and 5) give permission to record sensor data (GPS locations and call data). Results show that 42.9% of invited panel members responded positively to the invitation to participate in a smartphone survey. However, only 28.9% of these willing panel members completed all stages of the study. Predictors of nonresponse are somewhat different at every stage. In addition, respondents who complete all smartphone tasks are different from groups who do not participate at some or any stage of the study. By using data collected in previous waves we show that nonresponse leads to nonresponse bias in estimates of time use. We conclude by discussing implications for using smartphone apps in survey research., Survey Research Methods, Vol 13 No 2 (2019)

In this study, we investigate whether mobile device use in surveys can be predicted. We aim to identify possible motives for device use and build a model by drawing on theory from technology acceptance research and survey research. We then test this model with a Structural Equation Modeling approach using data of seven waves of the GESIS panel. We test whether our theoretical model fits the data by focusing on measures of fit, and by studying the standardized effects of the model. Results reveal that intention to use a particular device can predict actual use quite well. Ease of smartphone use is the most meaningful variable: if people use a smartphone for specific tasks, their intention to use a smartphone for survey completion is also more likely. In conclusion, investing in ease of use of mobile survey completion could encourage respondents to use mobile devices. This can foremost be established by building well-designed surveys for mobile devices.

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.

Web surveys are no longer completed on just a desktop or laptop computer. Respondents increasingly use mobile devices, such as tablets and smartphones to complete web surveys. In this article, we study how respondents in the American Life Panel complete surveys using varying devices. We show that about 30 percent of respondents sometimes complete surveys on a mobile device, and about 12 percent always use a mobile device. We study the characteristics of these “mobile-only” web survey respondents and find that they share many characteristics of typically hard-to-recruit survey respondents. They are more likely to be non-white, young, and not have a higher education. In terms of voting behavior, we find no differences between the groups of survey respondents who use different devices. This suggests that biases in political polls conducted through web-surveys are unlikely to occur when mobile-only respondents are underrepresented.

Different types of nonresponse threaten the validity of longitudinal studies: first, initial nonresponse during the recruitment in the baseline survey and second, successive dropout at each time point. Dropout in longitudinal surveys has three separate sources: failure to locate research participants, failure to contact participants, and failure to achieve cooperation. In this entry, specific attention is given to existing knowledge on the correlates of dropout and strategies to limit the problem. To limit nonresponse, a total design approach is advocated with specific attention to each source. That is to limit both noncontact (i.e., failure to locate and subsequent failure to contact a located research participant) and noncooperation.

Smartphones have a large potential for improving data collection by using research apps and collecting sensor data. This brings opportunities to enhance or extend measurement and to simplify the response task for respondents. Sensor data can (partly) replace survey questions, and these sensors potentially generate better data than respondents can provide themselves. This seems very promising, but many methodological questions arise related to representation and measurement in smartphone surveys; are respondent willing and able to participate and share sensor data, and how useful are the additional data collected via sensors and apps? In this dissertation we investigated the effect of smartphone surveys in terms of reducing (or enlarging) error components. These errors can be divided into errors in the measurement and the representation. In Chapter 2 we perform a systematic review and meta-analysis to investigate how to improve the effectiveness of the consent to data linkage question. In Chapter 3 we study nonresponse and nonresponse bias in the smartphone-only version of the Dutch Time Use Survey (TUS). In Chapter 4 we focus on measurement error when collecting GPS data in a smartphone survey. In Chapter 5 we investigate both representation and measurement in an innovative and experimental study on the use of sensor data in fitness and health research.