ABSTRACT
The TRI-POL project explores the triangle of interactive relationships between affective and ideological polarisation, political distrust, and the politics of party competition. In this project there are two complementary groups of datasets with individual-level survey data and digital trace data collected in five countries: Argentina, Chile, Italy, Portugal and Spain. These datasets are comprised of three waves carried out over a six-month period between late September 2021 and April 2022. In addition, the survey datasets include a series of experiments embedded in the different waves that examine social exposure, polarisation framing, and social sorting. The digital trace datasets include variables on individuals' behaviours and exposure to information received via digital media and social media. This data was collected using a combination of tracking technologies that the interviewees installed in their different devices. This digital trace data is matched with the individual-level survey data. These datasets are especially useful for researchers who wish to explore dynamics of polarisation, political attitudes, and political communication.
ABSTRACT
Images might provide richer and more objective information than text answers to open-ended survey questions. Little is known, nonetheless, about the consequences for data quality of asking participants to answer open-ended questions with images. Therefore, this paper addresses three research questions: (1) What is the effect of answering web survey questions with images instead of text on breakoff, noncompliance with the task, completion time and question evaluation? (2) What is the effect of including a motivational message on these four aspects? (3) Does the impact of asking to answer with images instead of text vary across device types? To answer these questions, we implemented a 2 × 3 between-subject web survey experiment (N = 3043) in Germany. Half of the sample was required to answer using PCs and the other half with smartphones. Within each device group, respondents were randomly assigned to (1) a control group answering open-ended questions with text; (2) a treatment group answering open-ended questions with images; and (3) another treatment group answering open-ended questions with images but prompted with a motivational message. Results show that asking participants to answer with images significantly increases participants' likelihood of noncompliance as well as their completion times, while worsening their overall survey experience. Including motivational messages, moreover, moderately reduces the likelihood of noncompliance. Finally, the likelihood of noncompliance is similar across devices.
ABSTRACT
Metered data, also called web-tracking data, are generally collected from a sample of participants who willingly install or configure, onto their devices, technologies that track digital traces left when people go online (e.g., URLs visited). Since metered data allow for the observation of online behaviours unobtrusively, it has been proposed as a useful tool to understand what people do online and what impacts this might have on online and offline phenomena. It is crucial, nevertheless, to understand its limitations. Although some research have explored the potential errors of metered data, a systematic categorisation and conceptualisation of these errors are missing. Inspired by the Total Survey Error, we present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework (1) describes the data generation and the analysis process for metered data and (2) documents the sources of bias and variance that may arise in each step of this process. Using a case study we also show how the TEM can be applied in real life to identify, quantify and reduce metered data errors. Results suggest that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. This framework can help improve the quality of both stand-alone metered data research projects, as well as foster the understanding of how and when survey and metered data can be combined.
