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Voting advice applications (VAAs) are online tools providing voting advice to their users. This voting advice is based on the match between the answers of the user and the answers of several political parties to a common questionnaire on political attitudes. To visualize this match, VAAs use a wide array of visualisations, most popular of which are the two-dimensional political maps. These maps show the position of both the political parties and the user in the political landscape, allowing the user to understand both their own position and their relation to the political parties. To construct these maps, VAAs require scales that represent the main underlying dimensions of the political space. This makes the correct construction of these scales important if the VAA aims to provide accurate and helpful voting advice. This paper presents three criteria that assess if a VAA achieves this aim. To illustrate their usefulness, these three criteria—unidimensionality, reliability and quality—are used to assess the scales in the cross-national EUVox VAA, a VAA designed for the European Parliament elections of 2014. Using techniques from Mokken scaling analysis and categorical principal component analysis to capture the metrics, I find that most scales show low unidimensionality and reliability. Moreover, even while designers can—and sometimes do—use certain techniques to improve their scales, these improvements are rarely enough to overcome all of the problems regarding unidimensionality, reliability and quality. This leaves certain problems for the designers of VAAs and designers of similar type online surveys.
The marketing materials of remote eye-trackers suggest that data quality is invariant to the position and orientation of the participant as long as the eyes of the participant are within the eye-tracker’s headbox, the area where tracking is possible. As such, remote eye-trackers are marketed as allowing the reliable recording of gaze from participant groups that cannot be restrained, such as infants, schoolchildren and patients with muscular or brain disorders. Practical experience and previous research, however, tells us that eye-tracking data quality, e.g. the accuracy of the recorded gaze position and the amount of data loss, deteriorates (compared to well-trained participants in chinrests) when the participant is unrestrained and assumes a non-optimal pose in front of the eye-tracker. How then can researchers working with unrestrained participants choose an eye-tracker? Here we investigated the performance of five popular remote eye-trackers from EyeTribe, SMI, SR Research, and Tobii in a series of tasks where participants took on non-optimal poses. We report that the tested systems varied in the amount of data loss and systematic offsets observed during our tasks. The EyeLink and EyeTribe in particular had large problems. Furthermore, the Tobii eye-trackers reported data for two eyes when only one eye was visible to the eye-tracker. This study provides practical insight into how popular remote eye-trackers perform when recording from unrestrained participants. It furthermore provides a testing method for evaluating whether a tracker is suitable for studying a certain target population, and that manufacturers can use during the development of new eye-trackers.