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Abstract
Text message surveys enable cost-efficient data collection and can be applied with hard-to-reach populations while other survey modes suffer from undercoverage in regions with low landline and/or internet penetration. Little is known about how to best administer surveys in this mode. We experimentally compared two different designs of automated text message surveys in terms of response rate, nonresponse bias, and participation in a follow-up survey in Egypt. In the single-sitting design, respondents automatically received a text message with a new question once they replied to a question. In the modular design, respondents received a new question each day, regardless of whether they had responded to the previous question. We invited 1,081 Egyptian parents of kindergarten children who owned a mobile phone to participate in a text message survey with eight questions on the nutrition behavior of their children. We found that, compared to the single-sitting design, the modular design yielded a higher number of answered questions but had fewer fully completed questionnaires. We found no nonresponse bias in either group and no difference in the probability of responding to a follow-up survey. Our results will help researchers make design decisions about how to implement text message surveys.

Within the survey context, a geofence can be defined as a geographical area that triggers a survey invitation when an individual enters the area, dwells in the area for a defined amount of time or exits the area. Geofences may be used to administer context-specific surveys, such as an evaluation survey of a shopping experience at a specific retail location. While geofencing is already used in other contexts (e.g., marketing and retail), this technology seems so far to be underutilized in survey research. We implemented a geofence survey in a smartphone data collection project and geofenced 410 job centers with the Google Geofence API. Overall, the app sent 230 geofence-triggered survey invitations to 107 participants and received 224 responses from 104 participants. This article provides an overview of our geofence survey, including our experiences analyzing the data. We highlight the limitations in our design and examine how those shortcomings affect the number of falsely triggered surveys. Subsequently, we formulate the lessons learned that will help researchers improve their own geofence studies.

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.

Abstract
Linking digital trace data to existing panel survey data may increase the overall analysis potential of the data. However, producing linked products often requires additional engagement from survey participants through consent or participation in additional tasks. Panel operators may worry that such additional requests may backfire and lead to lower panel retention, reducing the analysis potential of the data. To examine these concerns, we conducted an experiment in the German PASS panel survey after wave 11. Three quarters of panelists (n = 4,293) were invited to install a research app and to provide sensor data over a period of 6 months, while one quarter (n = 1,428) did not receive an invitation. We find that the request to install a smartphone app and share data significantly decreases panel retention in the wave immediately following the invitation by 3.3 percentage points. However, this effect wears off and is no longer significant in the second and third waves after the invitation. We conclude that researchers who run panel surveys have to take moderate negative effects on retention into account but that the potential gain likely outweighs these moderate losses.

Since January 2020, the COVID-19 crisis has affected everyday life around the world, and rigorous government lockdown restrictions have been implemented to prevent the further spread of the pandemic. The consequences of the corona crisis and the associated lockdown policies for public health, social life, and the economy are vast. In view of the rapidly changing situation during this crisis, policymakers require timely data and research results that allow for informed decisions. Addressing the requirement for adequate databases to assess people's life and work situations during the pandemic, the Institute for Employment Research (IAB) developed the High-frequency Online Personal Panel (HOPP). The HOPP study started in May 2020 and is based on a random sample of individuals drawn from the administrative data of the Federal Employment Agency in Germany, containing information on all labour market participants except civil servants and self-employed. The main goal of the HOPP study is to assess the short-term as well as long-term changes in people's social life and working situation in Germany due to the corona pandemic. To assess individual dynamics the HOPP collected data on a monthly (wave one to four) and bi-monthly (wave five to seven) basis. Furthermore, respondents were divided into four groups. The different groups of a new wave were invited to the survey at weekly intervals (wave two to four) or bi-weekly intervals (wave five to seven). This gives us the advantage of being able to provide weekly data while each participant only had to participate on a monthly or bi-monthly basis. In this article, we delineate the HOPP study in terms of its main goals and features, topics, and survey design. Furthermore, we provide a summary of results derived from HOPP and the future prospects of the study.

Since January 2020, the COVID-19 crisis has affected everyday life around the world, and rigorous government lockdown restrictions have been implemented to prevent the further spread of the pandemic. The consequences of the corona crisis and the associated lockdown policies for public health, social life, and the economy are vast. In view of the rapidly changing situation during this crisis, policymakers require timely data and research results that allow for informed decisions. Addressing the requirement for adequate databases to assess people's life and work situations during the pandemic, the Institute for Employment Research (IAB) developed the High-frequency Online Personal Panel (HOPP). The HOPP study started in May 2020 and is based on a random sample of individuals drawn from the administrative data of the Federal Employment Agency in Germany, containing information on all labour market participants except civil servants and self-employed. The main goal of the HOPP study is to assess the short-term as well as long-term changes in people's social life and working situation in Germany due to the corona pandemic. To assess individual dynamics the HOPP collected data on a monthly (wave one to four) and bi-monthly (wave five to seven) basis. Furthermore, respondents were divided into four groups. The different groups of a new wave were invited to the survey at weekly intervals (wave two to four) or bi-weekly intervals (wave five to seven). This gives us the advantage of being able to provide weekly data while each participant only had to participate on a monthly or bi-monthly basis. In this article, we delineate the HOPP study in terms of its main goals and features, topics, and survey design. Furthermore, we provide a summary of results derived from HOPP and the future prospects of the study.

Das hochfrequente Online Personen Panel (IAB-HOPP) ist auch unter dem Namen der Studie "Leben und Erwerbstätigkeit in Zeiten von Corona" bekannt und hat im Zeitraum von Mai 2020 bis März 2021 Personen zu ihrer Lage in Zeiten von Corona befragt. Insgesamt wurden 7 Wellen durchgeführt. Die Daten sollen Wissenschaftlerinnen und Wissenschaftlern helfen die Veränderungen des Sozial- und Arbeitslebens im Zuge der COVID-19-Pandemie zu evaluieren und längerfristige Forschungsmöglichkeiten zu den Auswirkungen der Krise zu ermöglichen. Beispielhafte Aspekte sind die Entwicklung von Beschäftigungsverhältnissen, Kurzarbeit, soziale und finanzielle Absicherung, Arbeitszeiten, Homeoffice und Kinderbetreuung.

The high-frequency online panel (IAB-HOPP) is also known as the "Life and Work Situations in Times of Corona" study and surveyed individuals from May 2020 to March 2021. Overall, seven waves were conducted. The data will help researchers to evaluate changes in peoples' life during the COVID-19 pandemic, and provide long-term research opportunities on labour market consequences of the crisis. Example aspects include employment trends, short-time work, social and financial security, working hours, home office, and child care.