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As survey costs increase and response rates decrease, researchers are looking for alternative
methods to collect data from study subjects. Passively collected data may offer a way to reduce
the burden on research subjects while also collecting high-quality data needed for social science
research. Examples of passive data collection tools are applications installed on mobile devices
and sensors in subjects' homes or worn on the body. In this study, we focus on always-on location
data collected from subjects' iPhones. To explore the promise of passively collected data to
augment and improve survey data, we conducted a 2-week pilot study with 24 subjects. We
discuss the utility of always-on location data and the challenges researchers may encounter when
they incorporate location data in their analyses.

AbstractAlumni tracking surveys are critical for evaluating and improving youth employment training programmes. However, there is little evidence about how to design these surveys, especially in less developed countries. We conducted short message service and Web surveys of alumni of a South African employment training programme. Short message service surveys returned higher response rates and more representative data than Web surveys, albeit at three times the cost. Combining modes increased data quality but may not be warranted, given the additional cost and complexity. This evidence suggests that training programmes can feasibly collect quality, actionable data on their alumni, even with constrained budgets and staff. Copyright © 2018 John Wiley & Sons, Ltd.

Abstract
As survey methods evolve, researchers require a comprehensive understanding of the error sources in their data. Comparative studies, which assess differences between the estimates from emerging survey methods and those from traditional surveys, are a popular tool for evaluating total error; however, they do not provide insight on the contributing error sources themselves. The Total Survey Error (TSE) framework is a natural fit for evaluations that examine survey error components across multiple data sources. In this article, we present a case study that demonstrates how the TSE framework can support both qualitative and quantitative evaluations comparing probability and nonprobability surveys. Our case study focuses on five internet panels that are intended to represent the US population and are used to measure health statistics. For these panels, we analyze the total survey error in two ways: (1) using a qualitative assessment that describes how panel construction and management methods may introduce error and (2) using a quantitative assessment that estimates and partitions the total error for two probability-based panels into coverage error and nonresponse error. This work can serve as a "proof of concept" for how the TSE framework may be applied to understand and compare the error structure of probability and nonprobability surveys. For those working specifically with internet panels, our findings will further provide an example of how researchers may choose the panel option best suited to their study aims and help vendors prioritize areas of improvement.