Using Machine Learning to Predict Inappropriate Respondents
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- OZAKI Koken
- Graduate School of Business Sciences, University of Tsukuba
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- SUZUKI Takashi
- Graduate School of Business Sciences, University of Tsukuba
Bibliographic Information
- Other Title
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- 機械学習による不適切回答者の予測
- キカイ ガクシュウ ニ ヨル フテキセツ カイトウシャ ノ ヨソク
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Abstract
<p>Results of an analysis of survey data which includes data from inappropriate respondents (respondents who do not devote an appropriate amount of attentional resources when answering questions or whose answers for two questions are contradictory) are untrustworthy. To address this problem, an instructional manipulation check or directed questions scale can be used to identify such respondents. However, survey companies are not willing to use such tools for ethical reasons. In the present study, using eleven machine learning models and six exploratory variables, a prediction model which can judge whether a respondent is inappropriate is developed. The model shows that two explanatory variables, the maximum number of consecutive items on a scale to which a respondent answered with the same response option and response time, are effective for the prediction. The model can reduce the percentage of inappropriate respondents in the analyzed data, which leads to an improvement in the trustworthiness of the analysis results.</p>
Journal
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- Kodo Keiryogaku (The Japanese Journal of Behaviormetrics)
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Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 46 (2), 39-52, 2019
The Behaviormetric Society
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Keywords
Details 詳細情報について
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- CRID
- 1390565134843610496
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- NII Article ID
- 130007822899
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- NII Book ID
- AN0008437X
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- ISSN
- 18804705
- 03855481
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- NDL BIB ID
- 030069955
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed