Differences in Crowdsourced vs. Lab-based Mobile and Desktop Input Performance Data

被引:23
|
作者
Findlater, Leah [1 ]
Zhang, Joan [2 ]
Froehlich, Jon E. [2 ]
Moffatt, Karyn [3 ]
机构
[1] Univ Maryland, Human Comp Interact Lab, Coll Informat Studies, College Pk, MD 20742 USA
[2] Univ Maryland, Human Comp Interact Lab, Dept Comp Sci, College Pk, MD 20742 USA
[3] McGill Univ, Sch Informat Studies, ACT Res Grp, Montreal, PQ, Canada
来源
PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17) | 2017年
关键词
Human performance; crowdsourcing; input devices; mobile; MECHANICAL TURK; QUALITY;
D O I
10.1145/3025453.3025820
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Research on the viability of using crowdsourcing for HCI performance experiments has concluded that online results are similar to those achieved in the lab-at least for desktop interactions. However, mobile devices, the most popular form of online access today, may be more problematic due to variability in the user's posture and in movement of the device. To assess this possibility, we conducted two experiments with 30 lab-based and 303 crowdsourced participants using basic mouse and touchscreen tasks. Our findings show that: (1) separately analyzing the crowd and lab data yields different study conclusions-touchscreen input was significantly less error prone than mouse input in the lab but more error prone online; (2) age-matched crowdsourced participants were significantly faster and less accurate than their lab-based counterparts, contrasting past work; (3) variability in mobile device movement and orientation increased as experimenter control decreased-a potential factor affecting the touchscreen error differences. This study cautions against assuming that crowdsourced data for performance experiments will directly reflect lab-based data, particularly for mobile devices.
引用
收藏
页码:6813 / 6824
页数:12
相关论文
共 8 条
  • [1] A Comparison of Performance and Preference on Mobile Devices vs. Desktop Computers
    Adepu, Sushma
    Adler, Rachel F.
    2016 IEEE 7TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS MOBILE COMMUNICATION CONFERENCE (UEMCON), 2016,
  • [2] The hidden conversion funnel of mobile vs. desktop consumers
    Goldstein, Anat
    Hajaj, Chen
    ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2022, 53
  • [3] Perceptual Ratings of Voice Likability Collected through In-Lab Listening Tests vs. Mobile-Based Crowdsourcing
    Gallardo, Laura Fernandez
    Jimenez, Rafael Zequeira
    Moeller, Sebastian
    18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION, 2017, : 2233 - 2237
  • [4] Co-present mobile phone use as an expectancy violation: revisiting 'phubbing' in two lab-based experiments
    Vanden Abeele, Mariek M. P.
    Frackowiak, Michal
    Antheunis, Marjolijn L.
    SOCIAL INFLUENCE, 2024, 19 (01)
  • [5] Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data
    Rodriguez, Jorge
    Semanjski, Ivana
    Gautama, Sidharta
    Van de Weghe, Nico
    Ochoa, Daniel
    SENSORS, 2018, 18 (09)
  • [6] Differences in the city branding of European capitals based on online vs. offline sources of information
    Molina, Arturo
    Fernandez, Alejandra C.
    Gomez, Mar
    Aranda, Evangelina
    TOURISM MANAGEMENT, 2017, 58 : 28 - 39
  • [7] The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report
    Brito, Henrique
    Lopes, Henrique
    de Carvalho, Margarida Vaz
    Carrilho, Daniel
    Carvalho, Adriano
    Araujo, Duarte
    PSYCHOLOGY OF SPORT AND EXERCISE, 2024, 73
  • [8] The effectiveness of traditional vs. velocity-based strength training on explosive and maximal strength performance: A network meta-analysis
    Held, Steffen
    Speer, Kevin
    Rappelt, Ludwig
    Wicker, Pamela
    Donath, Lars
    FRONTIERS IN PHYSIOLOGY, 2022, 13