Open-Source Pipeline of Cursor-Tracking for Likert-Scale Data Collected in Qualtrics
References
- Siemers N, Gao X, Jamieson Z, Knäuper B. A feasibility study of a novel cursor tracking software to improve readiness assessments. Journal of Technology in Behavioral Science; 2026. DOI: 10.1007/s41347-025-00576-1
- Mathur MB, Reichling DB. Open-source software for mouse-tracking in Qualtrics to measure category competition. Behavior Research Methods. 2019;51(5):1987–1997. DOI: 10.3758/s13428-019-01258-6
- Joshi A, Kale S, Chandel S, Pal DK. Likert scale: Explored and explained. British Journal of Applied Science & Technology. 2015;7(4):396–403. DOI: 10.9734/BJAST/2015/14975
- Krosnick JA, Fabrigar LR.
Designing rating scales for effective measurement in surveys . In: Lyberg L, Biemer P, Collins M, De Leeuw E, Dippo C, Sharz N, Trewin D, editors. Survey Measurement and Process Quality. Wiley; 1997. pp. 141–164. DOI: 10.1002/9781118490013.ch6 - Cepeda C, Rodrigues J, Dias MC, Oliveira D, Rindlisbacher D, Cheetham M, Gamboa H.
Mouse tracking measures and movement patterns with application for online surveys . In: Holzingr A, Kieseberg P, Tjoa A, Weippl E, editors. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing; 2018. pp. 28–42. DOI: 10.1007/978-3-319-99740-7_3 - Cheetham M, Cepeda C, Gamboa H, Hoelscher C, Valizadeh S. Automated detection of decision-making style, based on users’ online mouse pointer activity. In: Chouvarda I, Fred A, Gamboa H, editors. Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023). Vol 4: BIOSIGNALS.
SciTePress ; 2023. pp. 281–286. DOI: 10.5220/0011716700003414 - Dias MC, Cepeda C, Rindlisbacher D, Battegay E, Cheetham M, Gamboa H. Predicting response uncertainty in online surveys: A proof of concept. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019). Vol 4.
SciTePress ; 2019. pp. 155–162. DOI: 10.5220/0007381801550162 - Weisgarber P, Valacich J, Jenkins J, Kim D, Kumar M. Detecting social desirability bias with human-computer interaction: A mouse-tracking study. In: Proceedings of the 57th Annual Hawaii International Conference on System Sciences. HICSS.
IEEE Computer Society ; 2024. pp. 4673–4682.https://aisel.aisnet.org/hicss-57/in/hci/4 - Horwitz R, Brockhaus S, Henninger F, Kieslich PJ, Schierholz M, Keusch F, Kreuter F.
Learning from mouse movements: Improving questionnaires and respondents’ user experience through passive data collection . In: Beatty P, Collins D, Kaye L, Padilla JL, Willis G, Wilmot A, editors. Advances in Questionnaire Design, Development, Evaluation and Testing. Wiley; 2020. pp. 403–425. DOI: 10.1002/9781119263685.ch16 - Schneider IK, van Harreveld F, Rotteveel M, Topolinski S, van der Pligt J, Schwarz N, Koole SL. The path of ambivalence: Tracing the pull of opposing evaluations using mouse trajectories. Frontiers in Psychology. 2015;6:996. DOI: 10.3389/fpsyg.2015.00996
- Cepeda C, Tonet R, Osorio DN, Silva HP, Battegay E, Cheetham M, Gamboa H. Latent: A flexible data collection tool to research human behavior in the context of web navigation. IEEE Access. 2019;7:77659–77673. DOI: 10.1109/ACCESS.2019.2916996
- Cepeda C, Dias MC, Rindlisbacher D, Gamboa H, Cheetham M. Knowledge extraction from pointer movements and its application to detect uncertainty. Heliyon. 2021;7(1):
e05873 . DOI: 10.1016/j.heliyon.2020.e05873 - Meyer T, Kim AD, Spivey M, Yoshimi J. Mouse tracking performance: A new approach to analyzing continuous mouse tracking data. Behavior Research Methods. 2024;56(5):4682–4694. DOI: 10.3758/s13428-023-02210-5
- Stillman PE, Shen X, Ferguson MJ. How mouse-tracking can advance social cognitive theory. Trends in Cognitive Sciences. 2018;22(6):531–543. DOI: 10.1016/j.tics.2018.03.012
- Niño-Adan I, Manjarres D, Landa-Torres I, Portillo E. Feature weighting methods: A review. Expert Systems with Applications. 2021;184:115424. DOI: 10.1016/j.eswa.2021.115424
- Oskouei AG, Samadi N, Khezri S, Moghaddam AN, Babaei H, Hamini K, Nojavan SF, Bouyer A, Arasteh B. Feature-weighted fuzzy clustering methods: An experimental review. Neurocomputing. 2025;619:129176. DOI: 10.1016/j.neucom.2024.129176
- Xiao J, Ye H, He X, Zhang H, Wu F, Chua TS. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv [preprint]; 2017. DOI: 10.24963/ijcai.2017/435
- Carter B, Del Ponte A. Integrating web applications into popular survey platforms for online experiments. Behavior Research Methods. 2022;54(6):3093–3099. DOI: 10.3758/s13428-022-01792-w
- Loescher LJ, Hibler E, Hiscox H, Hla H, Harris RB. Challenges of using the internet for behavioral research. CIN: Computers, Informatics, Nursing. 2011;29(8):445–448. DOI: 10.1097/NCN.0b013e3181fc4153
- Rodd JM. Moving experimental psychology online: How to obtain high quality data when we can’t see our participants. Journal of Memory and Language. 2024;134:104472. DOI: 10.1016/j.jml.2023.104472
DOI: https://doi.org/10.5334/jors.690 | Journal eISSN: 2049-9647
Language: English
Page range: 46 - 46
Submitted on: Feb 1, 2026
Accepted on: May 1, 2026
Published on: Jun 8, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2026 Nellie Siemers, Zachary Jamieson, Xinran Gao, Mira Saad, Bärbel Knäuper, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.