PUMA Survey 5.2. Insights in societal changes in Austria

Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of three modules: MODULE 1 "Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability", MODULE 2 "Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys", MODULE 3 "Concerns of Smartphone Owners When Using their Device for Research". Fieldwork was conducted by Statistics Austria.<br><br/>MODULE 1: Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability (Patrick Lazarevič, Martina Brandt, Marc Luy, Caroline Berghammer) <br>
Self-rated health (SRH) is the most widely used single-indicator of health in many scientific disciplines (Jylhä 2009). Even though more comprehensive approaches to measure generic health exist, they are often too time consuming for survey interviews, especially in multi-thematic surveys, due to time limitations. Research in this regard has shown that, even when controlling for comprehensive health information, SRH is noticeably and independently influenced by non-health factors like satisfaction with life or social participation (e.g., Lazarevič 2018). While these results illustrate that health ratings are influenced by non-health factors, the personality traits that are assumed to bias SRH (e.g., optimism, social desirability, or hypochondriasis) are typically not directly measured. The Minimum European Health Module (MEHM), as proposed by Robine & Jagger (2003), complements SRH with the questions whether the respondent suffers from a chronic disease and whether and to what extent they are limited in their usual activities due to a health problem. Thus, MEHM can be seen as a compromise between using SRH as a single-indicator and a comprehensive scale while covering the two most relevant factors for health ratings, i.e., chronic diseases and the functional status (Lazarevič 2018). While MEHM is obviously less time- and cost-intensive than more comprehensive approaches to measure health and there was some research done on its components separately (e.g., Berger et al. 2015), hardly anything is known about its usefulness as a short-scale of generic health, its overall psychometric properties, and its susceptibility to non-health factors potentially biasing the health measurement. This module tested the feasibility and utility of using the Minimum European Health Module (MEHM) as a short scale for measuring generic health. We demonstrate the feasibility of extracting a factor score from MEHM utilizing confirmatory factor analyses based on polychoric correlations. Further analyses suggest that this factor score might be useful in reducing bias in generic health measurement due to optimism and social desirability. <br/><br>MODULE 2: Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys (Markus Hadler, Franz Höllinger, Anja Eder) <br>
Collecting data online is a promising tool, given the problems survey research faces in terms of lowering response rates and increasing costs. Yet, the results on the comparability of online and face-to-face surveys are ambiguous (see Roberts et al. 2016). Therefore, the aim of our research is to test differences in responses when completing surveys online compared to collecting the same data face-to-face. Our PUMA-module collects some of the core ISSP questions online, which were asked face-to-face (CAPI) in the same time-period. The topics of the ISSP questionnaires 2017 and 2018 are “Social Networks” and “Religion.” At face value, we expect that these two areas may attract different respondents when conducted online as compared to face-to-face. Online networking should be more prevalent and traditional religious activities less common among the online respondents. If there are no significant differences between these two samples, our study will be a strong indicator that online tools are valid instruments. Therefore, the mixed modes design aims to break new ground in understanding the advantages and limitations, the costs and benefits of combining online and face-to-face interviews in Austria on the basis of two prominent survey modules from the International Social Survey Programme. <br/><br>MODULE 3: Concerns of Smartphone Owners When Using their Device for Research (Florian Keusch, Martin Weichbold) <br>
Smartphone use is on the rise worldwide (Pew Research Center 2017). Survey researchers are aware that smartphone users increasingly complete online surveys on their mobile devices and have investigated the quality of survey data provided via smartphones (e.g., Couper et al. 2017; Keusch & Yan 2017). At the same time, the rising penetration of smartphones also gives researchers the chance to collect data from smartphone users that goes beyond self-reporting through surveys. Smartphones can be used to collect a variety of data about respondents such as geolocation, measures of physical activity, online behavior and browser history, app usage, call logs, or photos (Link et al. 2014). These data would allow researchers to make inferences about, among others, users’ mobility patterns, consumer behavior, health, and social interactions. Compared to surveys, which rely on self-reports, passive mobile data collection has the potential to provide richer data (because it can be collected in much higher frequencies), to decrease respondent burden (because fewer survey questions need to be asked), and to reduce measurement error (because of reduction in recall errors and social desirability). However, agreeing to allow for passive collection of data from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing these data with researchers due to security, privacy, and confidentiality concerns. In addition, different subgroups might differ in their skills of smartphone use and thus feel more or less comfortable using smartphones for research, leading to bias due to differential nonresponse of specific groups. This module wants to find out whether new forms of smartphone data collection (using sensors, apps, and camera) could be a supplement to survey research as they provide rich data and could enlarge our knowledge about people’s behavior while reducing respondent burden. Collecting these data has ethical and practical implications: agreeing to collect data from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing these data with researchers due to security, privacy, and confidentiality concerns. In addition, different subgroups might differ in their skills of smartphone use and thus feel more or less comfortable using smartphones for research, leading to bias due to differential nonparticipation of specific groups. We find that concern for using smartphones for research differs by research task, and that the diversity of smartphone activities correlates with concern.