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  • 1
    Online Resource
    Online Resource
    Privacy Attitudes toward Mouse-Tracking Paradata Collection (2023)
    Berlin : Humboldt-Universität zu Berlin
    In:  87,1, Seiten 602-618
    Details
    Language: English
    Pages: 1 Online-Ressource (17 Seiten)
    Publ. der Quelle: : Oxford University Press, 2023
    Angaben zur Quelle: 87,1, Seiten 602-618
    DDC: 050
    Keywords: Allgemeine fortlaufende Sammelwerke ; Sozialwissenschaften ; Englische und altenglische (Angelsächsische) Literaturen
    Abstract: Survey participants’ mouse movements provide a rich, unobtrusive source of paradata, offering insight into the response process beyond the observed answers. However, the use of mouse tracking may require participants’ explicit consent for their movements to be recorded and analyzed. Thus, the question arises of how its presence affects the willingness of participants to take part in a survey at all—if prospective respondents are reluctant to complete a survey if additional measures are recorded, collecting paradata may do more harm than good. Previous research has found that other paradata collection modes reduce the willingness to participate, and that this decrease may be influenced by the specific motivation provided to participants for collecting the data. However, the effects of mouse movement collection on survey consent and participation have not been addressed so far. In a vignette experiment, we show that reported willingness to participate in a survey decreased when mouse tracking was part of the overall consent. However, a larger proportion of the sample indicated willingness to both take part and provide mouse-tracking data when these decisions were combined, compared to an independent opt-in to paradata collection, separated from the decision to complete the study. This suggests that survey practitioners may face a trade-off between maximizing their overall participation rate and maximizing the number of participants who also provide mouse-tracking data. Explaining motivations for paradata collection did not have a positive effect and, in some cases, even reduced participants’ reported willingness to take part in the survey.
    Abstract: Peer Reviewed
    DOI: 10.18452/33707
    DOI: 10.1093/poq/nfad034
    URN: urn:nbn:de:kobv:11-110-18452/34337-3
    URL: Volltext  (kostenfrei)
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    Online Resource
    HU-Berlin Edoc
  • 2
    Online Resource
    Online Resource
    Detecting Respondent Burden in Online Surveys: How Different Sources of Question Difficulty Influence Cursor Movements (2025)
    Berlin : Humboldt-Universität zu Berlin
    In:  43,1, Seiten 191-213
    Details
    ISSN: 0894-4393 , 0894-4393
    Language: English
    Pages: 1 Online-Ressource (23 Seiten)
    Publ. der Quelle: : Sage, 2024
    Angaben zur Quelle: 43,1, Seiten 191-213
    DDC: 004
    Keywords: mouse-tracking ; measurement error ; online surveys ; response difficulty ; response time ; paradata ; Informatik ; Sozialwissenschaften
    Abstract: Online surveys are a widely used mode of data collection. However, as no interviewer is present, respondents face any difficulties they encounter alone, which may lead to measurement error and biased or (at worst) invalid conclusions. Detecting response difficulty is therefore vital. Previous research has predominantly focused on response times to detect general response difficulty. However, response difficulty may stem from different sources, such as overly complex wording or similarity between response options. So far, the question of whether indicators can discriminate between these sources has not been addressed. The goal of the present study, therefore, was to evaluate whether specific characteristics of participants’ cursor movements are related to specific properties of survey questions that increase response difficulty. In a preregistered online experiment, we manipulated the length of the question text, the complexity of the question wording, and the difficulty of the response options orthogonally between questions. We hypothesized that these changes would lead to increased response times, hovers (movement pauses), and y-flips (changes in vertical movement direction), respectively. As expected, each manipulation led to an increase in the corresponding measure, although the other dependent variables were affected as well. However, the strengths of the effects did differ as expected between the mouse-tracking indices: Hovers were more sensitive to complex wording than to question difficulty, while the opposite was true for y-flips. These results indicate that differentiating sources of response difficulty might indeed be feasible using mouse-tracking.
    Abstract: Peer Reviewed
    DOI: 10.18452/32745
    DOI: 10.1177/08944393241247425
    URN: urn:nbn:de:kobv:11-110-18452/33363-1
    URL: Volltext  (kostenfrei)
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    Online Resource
    HU-Berlin Edoc
  • 3
    Online Resource
    Online Resource
    Predicting Question Difficulty in Web Surveys: A Machine Learning Approach Based on Mouse Movement Features (2023)
    Berlin : Humboldt-Universität zu Berlin
    In:  41,1, Seiten 141-162
    Details
    ISSN: 0894-4393 , 0894-4393
    Language: English
    Pages: 1 Online-Ressource (22 Seiten)
    Publ. der Quelle: Thousand Oaks, Calif. [u.a.] : Sage
    Angaben zur Quelle: 41,1, Seiten 141-162
    DDC: 004
    Keywords: mouse movements ; paradata ; web surveys ; difficulty ; personalization ; supervised learning models ; classification ; Informatik ; Sozialwissenschaften
    Abstract: Survey research aims to collect robust and reliable data from respondents. However, despite researchers’ efforts in designing questionnaires, survey instruments may be imperfect, and question structure not as clear as could be, thus creating a burden for respondents. If it were possible to detect such problems, this knowledge could be used to predict problems in a questionnaire during pretesting, inform real-time interventions through responsive questionnaire design, or to indicate and correct measurement error after the fact. Previous research has used paradata, specifically response times, to detect difficulties and help improve user experience and data quality. Today, richer data sources are available, for example, movements respondents make with their mouse, as an additional detailed indicator for the respondent–survey interaction. This article uses machine learning techniques to explore the predictive value of mouse-tracking data regarding a question’s difficulty. We use data from a survey on respondents’ employment history and demographic information, in which we experimentally manipulate the difficulty of several questions. Using measures derived from mouse movements, we predict whether respondents have answered the easy or difficult version of a question, using and comparing several state-of-the-art supervised learning methods. We have also developed a personalization method that adjusts for respondents’ baseline mouse behavior and evaluate its performance. For all three manipulated survey questions, we find that including the full set of mouse movement measures and accounting for individual differences in these measures improve prediction performance over response-time-only models.
    Abstract: Peer Reviewed
    DOI: 10.18452/25911
    DOI: 10.1177/08944393211032950
    URN: urn:nbn:de:kobv:11-110-18452/26591-2
    URL: Volltext  (kostenfrei)
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    Online Resource
    HU-Berlin Edoc
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