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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.

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.

The UK has a long and rich history of longitudinal studies, including cohort studies and longitudinal panels, and we summarise the main studies and their key features. We go on to identify the key differences between cohort and panel approaches, in the population coverage and whether refreshment samples are expected; what types of questions can be used; the sample design and sample size; and the choice of spacing of waves. Over a long period panels and cohorts collect similar amounts of information, but in a single cohort there is no way to measure the cohort effect separately from time effects. Early in the studies, the differences are greater. Cohort studies can have questionnaires much more tailored to the age and expected life course events of the respondents than a panel study, which must cater for many ages. Accelerated cohort designs offer an intermediate solution, where the collection can be tailored, but where cohort effects can be estimated, as long as there is some overlap between the ages in the cohorts. There are several additional factors to consider in an accelerated cohort design. Making choices about which features are important in a new cohort study depends on the answers to many questions about what the intended analytical outcomes are, and how long the study will run

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.

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.

The smartphone as an optional survey device is one of the recent challenges survey research faces. Various problems have been identified. Smartphone-optimized surveys are being created and investigated as a possible solution. No consensus has yet been established on whether these optimized surveys solve all existing problems. In this study, a new way of smartphone survey optimization is investigated. Not only do we look at the smartphone as a device, but more importantly as a communicational tool. We lay emphasis on open narrative questions and instant messaging in smartphone surveys. In an experimental design, we investigated whether an instant research messaging survey style positively influenced open narrative answer length and quality and the survey evaluation in comparison to a traditional web survey.

The smartphone as an optional survey device is one of the recent challenges survey research faces. Various problems have been identified. Smartphone-optimized surveys are being created and investigated as a possible solution. No consensus has yet been established on whether these optimized surveys solve all existing problems. In this study, a new way of smartphone survey optimization is investigated. Not only do we look at the smartphone as a device, but more importantly as a communicational tool. We lay emphasis on open narrative questions and instant messaging in smartphone surveys. In an experimental design, we investigated whether an instant research messaging survey style positively influenced open narrative answer length and quality and the survey evaluation in comparison to a traditional web survey.

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.

Binnen dit exploratieve onderzoek wordt onderzocht hoe GPS-data kunnen bijdragen aan sociaal netwerkonderzoek. Deze vorm van data is een relatief nieuw verschijnsel binnen de (sociale) wetenschap. In samenwerking met het Centraal Bureau voor de Statistiek is een meetinstrument voor dit onderzoek ontwikkeld. Dit meetinstrument, in de vorm van een mobiele applicatie, heeft bijgedragen aan het verzamelen van de verplaatsingscoördinaten van de respondenten. Binnen dit onderzoek zijn ook de errors en toenmalige zwakheden van dit meetinstrument aan bod gekomen. Op basis van de GPS-data kan gesteld worden dat triviale verplaatsingen (woon, werk, winkel) zichtbaar worden. Echter blijkt het moeilijk, met de vorm van meten binnen dit onderzoek, diverse informatie over de karakteristieken van een persoonlijk sociaal netwerk af te leiden.

Binnen dit exploratieve onderzoek wordt onderzocht hoe GPS-data kunnen bijdragen aan sociaal netwerkonderzoek. Deze vorm van data is een relatief nieuw verschijnsel binnen de (sociale) wetenschap. In samenwerking met het Centraal Bureau voor de Statistiek is een meetinstrument voor dit onderzoek ontwikkeld. Dit meetinstrument, in de vorm van een mobiele applicatie, heeft bijgedragen aan het verzamelen van de verplaatsingscoördinaten van de respondenten. Binnen dit onderzoek zijn ook de errors en toenmalige zwakheden van dit meetinstrument aan bod gekomen. Op basis van de GPS-data kan gesteld worden dat triviale verplaatsingen (woon, werk, winkel) zichtbaar worden. Echter blijkt het moeilijk, met de vorm van meten binnen dit onderzoek, diverse informatie over de karakteristieken van een persoonlijk sociaal netwerk af te leiden.

Binnen dit exploratieve onderzoek wordt onderzocht hoe GPS-data kunnen bijdragen aan sociaal netwerkonderzoek. Deze vorm van data is een relatief nieuw verschijnsel binnen de (sociale) wetenschap. In samenwerking met het Centraal Bureau voor de Statistiek is een meetinstrument voor dit onderzoek ontwikkeld. Dit meetinstrument, in de vorm van een mobiele applicatie, heeft bijgedragen aan het verzamelen van de verplaatsingscoördinaten van de respondenten. Binnen dit onderzoek zijn ook de errors en toenmalige zwakheden van dit meetinstrument aan bod gekomen. Op basis van de GPS-data kan gesteld worden dat triviale verplaatsingen (woon, werk, winkel) zichtbaar worden. Echter blijkt het moeilijk, met de vorm van meten binnen dit onderzoek, diverse informatie over de karakteristieken van een persoonlijk sociaal netwerk af te leiden.

Binnen dit exploratieve onderzoek wordt onderzocht hoe GPS-data kunnen bijdragen aan sociaal netwerkonderzoek. Deze vorm van data is een relatief nieuw verschijnsel binnen de (sociale) wetenschap. In samenwerking met het Centraal Bureau voor de Statistiek is een meetinstrument voor dit onderzoek ontwikkeld. Dit meetinstrument, in de vorm van een mobiele applicatie, heeft bijgedragen aan het verzamelen van de verplaatsingscoördinaten van de respondenten. Binnen dit onderzoek zijn ook de errors en toenmalige zwakheden van dit meetinstrument aan bod gekomen. Op basis van de GPS-data kan gesteld worden dat triviale verplaatsingen (woon, werk, winkel) zichtbaar worden. Echter blijkt het moeilijk, met de vorm van meten binnen dit onderzoek, diverse informatie over de karakteristieken van een persoonlijk sociaal netwerk af te leiden.

This article describes the application of the total survey error paradigm to longitudinal surveys. Several aspects of survey error, and of the interactions between different types of error, are distinct in the longitudinal survey context. Furthermore, error trade-off decisions in survey design and implementation are subject to some unique considerations. Previous literature on total survey error mostly fails to explicitly consider uniquely longitudinal issues. We aim to show how uniquely longitudinal sources of error in surveys should be understood within the Total Survey Error framework and we provide examples of studies of some of the unique interactions between errors.

De aanleiding voor deze literatuurstudie was een motie van Segers/van der Staaij, waarin gevraagd werd om een wetenschappelijke vergelijking te maken van het prostitutiebeleid tussen landen in Noordwest-Europa en het effect van dat beleid op de omvang van mensenhandel in de betreffende landen. De onderzoeksvraag in deze studie luidt: "Wat is er bekend over de relatie tussen prostitutiebeleid in Noordwest-Europa en de omvang van mensenhandel en wat valt er vanuit wetenschappelijk oogpunt te zeggen over de kwaliteit van de bestaande onderzoeken waarop de conclusies over deze relatie zijn gebaseerd?" Om een zo goed mogelijk inzicht in bovengenoemde relatie(s) te krijgen, is dit onderzoek specifiek gericht op landen in Noordwest-Europa: Nederland, België, Noorwegen, Zweden, Finland, Denemarken, Duitsland en Groot Brittannië. Deze landen dekken samen de belangrijkste, voor vergelijkend onderzoek relevante, vormen van prostitutiebeleid, en hebben een infrastructuur die in principe het best mogelijke onderzoek naar de omvang van mensenhandel mogelijk maakt.

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.