研究業績

@article{9387296,

title = {Detection of Careless Responses in Online Surveys Using Answering Behavior on Smartphone},

author = {Masaki Gogami and Yuki Matsuda and Yutaka Arakawa and Keiichi Yasumoto},

doi = {10.1109/ACCESS.2021.3069049},

issn = {2169-3536},

year  = {2021},

date = {2021-03-26},

journal = {IEEE Access},

volume = {9},

pages = {53205 - 53218},

abstract = {Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses.We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Close

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses.We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses.

Close

Mohamed A. Abdelwahab, Shizuo Kaji, Maiya Hori, Shigeru Takano, Yutaka Arakawa, Rin-ichiro Taniguchi

Measuring “Nigiwai” from Pedestrian Movement Journal Article

In: IEEE Access, 9 , pp. 24859 - 24871, 2021, ISSN: 2169-3536.

Abstract | Links | BibTeX

Daichi Koike, Shigemi Ishida, Yutaka Arakawa

Called Function Identification of IoT Devices by Network Traffic Analysis Inproceedings

In: The 36th ACM/SIGAPP Symposium On Applied Computing (SAC2021), pp. 737-743, 2021.

BibTeX

Kenta Hayashi, Shingo Kumazoe, Shigemi Ishida, Yutaka Arakawa

Distinguishing Working State by Palm Orientation Inproceedings

In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021), 2021.

Links | BibTeX

Sirui Jian, Shigemi Ishida, Yutaka Arakawa

Initial Attempt on Wi-Fi CSI Based Vibration Sensing for Factory Equipment Fault Detection Inproceedings

In: International Workshop on Maintenance-Free Context Sensing (MFSens), in conjunction with International Conference on Distributed Computing and Networking (ICDCN), Nara, Japan, 2021.

BibTeX

田中宏和, 本松大夢, 中村優吾, 荒川豊

連れ立ち行動促進システムの提案 Conference