Statistical modeling using mouse movements to model measurement error and improve data quality in web surveys

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The overarching goal of this project is the improvement of web survey data collection by detecting issues in the instrument and on part of individual participants. In the first funding period, we have demonstrated the usefulness of mouse-tracking paradata for this purpose based on a case study, and developed sophisticated techniques for their analysis. In this continuation, we aim to further develop and extend these methods, and evaluate their robustness in the field. We will take a two-pronged interdisciplinary approach, building on our prior work and experience. The first strand of work packages concerns the statistical methodology for analyzing the multidimensional time-series that make up interaction paradata. Here, we will extend our analysis methods beyond mouse movements alone to handle all observable user interactions (including, but not limited to, keyboard input, touchscreen data, and further device information), apply deep learning to uncover yet-undiscovered features that go beyond our manually defined indicators, and investigate heterogeneity between user groups. As a second, independent but closely linked, survey research strand, we will apply and evaluate our old and newly developed methods in the field, investigate how they generalize to larger and more diverse samples, and guide the methods development to meet the requirements of survey practitioners. In addition, we will assess our methods’ potential to detect not only hard questions, but inattentive and fraudulent respondents, addressing two pertinent issues facing survey providers. Lastly, we will supply survey practitioners with best practices for collecting this class of data, through development and testing of consent procedures. In parallel, we will create data collection and processing infrastructure to underpin our efforts, and that we will make available to researchers as open-source software with added documentation, tutorials and learning materials. Throughout both strands, we will work closely with project partners who maintain survey panels, and have established collaborations with survey providers to ensure that our plans are feasible. Together, we will create a new dataset that will aid our and others’ future investigations of paradata.