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  • 1
    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
    URL: Volltext  (kostenfrei)
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  • 2
    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
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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