Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes

Weib, Maximilian; Angerbauer, Katrin; Voit, Alexandra; Schwarzl, Magdalena; Sedlmair, Michael; Mayer, Sven

doi:10.1109/tvcg.2020.3030400

Cited by 13 publications

(14 citation statements)

References 63 publications

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“…Voit et al's comparison of five empirical research methods [76] (i.e., online, VR, augmented reality, lab, in situ) suggested that VR and in situ provide similar insights when evaluating standardised questionnaires such as SUS [14] or AttrakDiff [35]. Weiß et al [81] showed that alternative empirical research methods (e.g., VR) might be used to infer insights about in situ studies and that the evaluation of situated visualisations is not necessarily dependent on the empirical research method.…”

Section: Vr Studies For Human-centred Researchmentioning

confidence: 99%

“…This is particularly interesting for the human-centred security domain where private and sensitive contexts are often challenging to study [19,77]. We also noticed that VR has already been successfully applied in several other research domains (e.g., Human-computer Interaction [44,47,52,76], Information Visualisation [81,85]).…”

Section: Lessons Learned From Prior Workmentioning

confidence: 99%

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Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

Mathis

O’Hagan

Khamis

et al. 2022

2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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Figure 1: We explore the use of virtual reality (VR) for shoulder surfing research in the authentication research domain. We compare the impact of non-immersive/immersive VR observations on participants' observation performance and behaviour while shoulder surfing authentications. We demonstrate the strengths of VR-based shoulder surfing research by exploring three different authentication scenarios: (➊) automated teller machine (ATM), (➋) smartphone PIN, and (➌) smartphone pattern authentication.

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Section: Vr Studies For Human-centred Researchmentioning

confidence: 99%

Section: Lessons Learned From Prior Workmentioning

confidence: 99%

Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

Mathis

O’Hagan

Khamis

et al. 2022

2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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“…To the best of our knowledge, there exists no targeted scale yet for measuring the aesthetic pleasure of visual data representations. Until now, visualization researchers can only use scales that are designed for interactive products in general; for example, the AttrakDiff questionnaire has been used in several visualization studies (e. g., [17,76]).…”

Section: Measuring Aesthetic Pleasure Outside Of Visualizationmentioning

confidence: 99%

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

He,

Isenberg,

Dachselt

et al. 2022

Preprint

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We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies. Given the lack of an aesthetic pleasure scale dedicated to visualizations, researchers have mostly chosen their own terms to study or compare the aesthetic pleasure of visualizations. Yet, many terms are possible and currently no clear guidance on their effectiveness regarding the judgment of aesthetic pleasure exists. To solve this problem, we engaged in a multi-step research process to develop the first validated rating scale specifically for judging the aesthetic pleasure of a visualization (osf.io/fxs76). Our final BeauVis scale consists of five items, "enjoyable," "likable," "pleasing," "nice," and "appealing." Beyond this scale itself, we contribute (a) a systematic review of the terms used in past research to capture aesthetics, (b) an investigation with visualization experts who suggested terms to use for judging the aesthetic pleasure of a visualization, and (c) a confirmatory survey in which we used our terms to study the aesthetic pleasure of a set of 3 visualizations.

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“…3D generalized bar charts were extremely rare and only sometimes appeared either in the early years we coded or more recently to visualize data on 3D surfaces such as a globe. Canonical examples of generalized bar charts taken from [92], [95], [48]:…”

Section: Generalized Bar Representationsmentioning

confidence: 99%

Not As Easy As You Think -- Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology

Chen

Isenberg²,

Laramee

et al. 2022

Preprint

Self Cite

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Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes

Cited by 13 publications

References 63 publications

Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

Not As Easy As You Think -- Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology

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