WEB SURVEY GAMIFICATION – INCREASING DATA QUALITY IN WEB SURVEYS BY USING GAME DESIGN ELEMENTS

Researchers and survey designers face the challenge of low data quality as web surveys are often not compelling. Thus, participants’ engagement declines while completing a survey resulting in participants tend to apply satisficing behavior (e.g., speeding, straight-lining) in order to complete the questionnaire or even break-off the completion of the questionnaire. Due to satisficing behavior, researchers are faced with the challenge of low data quality. Addressing this challenge, survey gamification promises to make web survey participation enjoyable, which might also engage participants to complete questionnaires by providing high-quality data. However, the research on the effects of gamifying web surveys (in particular on behavioral outcomes) is still inconclusive. Addressing this short-coming, we propose to examine the effects of two common game design elements – badges and a meaningful story – in an experimental study. Based on the theoretical background of gamification and the theory of cognitive absorption, we derive hypotheses and outline in detail our experimental design in this research-in-progress paper. Our proposed research study will contribute to research and practice by addressing an important challenge when conducting online surveys: the motivation to process surveys accurately

The second-level smartphone divide: A typology of smartphone use based on frequency of use, skills, and types of activities

This paper examines inequalities in the usage of smartphone technology based on five samples of smartphone owners collected in Germany and Austria between 2016 and 2020. We identify six distinct types of smartphone users by conducting latent class analyses that classify individuals based on their frequency of smartphone use, self-rated smartphone skills, and activities carried out on their smartphone. The results show that the smartphone usage types differ significantly by sociodemographic and smartphone-related characteristics: The types reflecting more frequent and diverse smartphone use are younger, have higher levels of educational attainment, and are more likely to use an iPhone. Overall, the composition of the latent classes and their characteristics are robust across samples and time

Is satisficing responsible for response order effects in rating scale questions?

Rating scales are used extensively in surveys. A rating scale can descend from the highest to the lowest point or from the positive to the negative pole. A rating scale can also start with the lowest point (or the negative pole) and ascend to the highest point (or the positive pole). Previous research has shown that the direction of the scale, i.e., the order of the response options, has an impact on responses, and that respondents are more likely to select response options close to the starting point of the scale, regardless of whether the scale ascends or descends. This paper advances the literature by examining empirically whether or not the response order effect in rating scale questions is driven by satisficing. Drawing on data from five experiments, we found that scale direction had a significant and extreme impact on response distributions. Although the effect of scale direction was stronger among speeders than non-speeders, the effect was observed across the board among those who were at a high risk of satisficing and those who were not

Collecting Survey and Smartphone Sensor Data With an App: Opportunities and Challenges Around Privacy and Informed Consent

The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability app study in which participants were asked through a GDPR compliant consent process to share a series of digital trace data, including geolocation, accelerometer data, phone and text messaging logs, app usage, and access to their address books. With about 4,300 invitees and about 650 participants, we demonstrate (1) people were just as willing to share such extensive digital trace data as they were in studies with far more limited requests; (2) despite being provided more decision-related information, participants hardly differentiated between the different data requests made; and (3) once participants gave consent, they did not tend to revoke it. We also show (4) evidence for a widely-held belief that explanations regarding data collection and data usage are often not read carefully, at least not within the app itself, indicating the need for research and user experience improvement to adequately inform and protect participants. We close with suggestions to the field for creating a seal of approval from professional organizations to help the research community promote the safe use of data

Augmenting surveys with data from sensors and apps: Opportunities and challenges

The increasing volume of “Big Data” produced by sensors and smart devices can transform the social and behavioral sciences. Several successful studies used digital data to provide new insights into social reality. This special issue argues that the true power of these data for the social sciences lies in connecting new data sources with surveys. While new digital data are rich in volume, they seldomly cover the full population nor do they provide insights into individuals’ feelings, motivations, and attitudes. Conversely, survey data, while well suited for measuring people’s internal states, are relatively poor at measuring behaviors and facts. Developing a methodology for integrating the two data sources can mitigate their respective weaknesses. Sensors and apps on smartphones are useful for collecting both survey data and digital data. For example, smartphones can track people’s travel behavior and ask questions about its motives. A general methodology on the augmentation of surveys with data from sensors and apps is currently missing. Issues of representativeness, processing, storage, data linkage, and how to combine survey data with sensor and app data to produce one statistic of interest pertain. This editorial to the special issue on “Using Mobile Apps and Sensors in Surveys” provides an introduction to this new field, presents an overview of challenges, opportunities, and sets a research agenda. We introduce the four papers in this special issue that focus on these opportunities and challenges and provide practical applications and solutions for integrating sensor- and app-based data collection into surveys

Chapter 10, Google trends as a tool for public opinion research: An illustration of the perceived threats of immigration

To gather public opinion data on sensitive topics in real-time, researchers are exploring the use of Internet search data such as Google Trends (GT). First, this chapter describes the characteristics and nature of GT data, and then provides a case study that examines the salience of perceived threats related to immigration in Germany based on the share of Google search queries that include language about these threats. Last, we discuss the advantages and possible challenges of utilizing GT data in social scientific research. We used the national polling results for the German right-wing party Alternative für Deutschland (AfD)—which runs on a largely anti-immigrant platform—as a criterion measure. GT data did not consistently predict polling data in the expected direction in real-time, but it was consistently predictive of future polling trends (35–104 weeks later) at a moderate level (r = .25–.50), although the size of the correlations varied across time periods and groups of keywords. Our mixed results highlight the low reliability of GT data, but also its largely untapped potential as a leading indicator of public opinion, especially on sensitive topics such as the perceived threats of immigration