Detecting Bad Answers in Survey Data Through Unsupervised Machine Learning

Author:

Jebreel Najeeb Moharram,Haffar Rami,Singh Ashneet Khandpur,Sánchez David,Domingo-Ferrer Josep,Blanco-Justicia Alberto

Publisher

Springer International Publishing

Reference26 articles.

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2. Winker, P.: Assuring the quality of survey data: incentives, detection and documentation of deviant behavior. Stat. J. IAOS 32(3), 295–303 (2016)

3. Rogers, F., Richarme, M.: The honesty of online survey respondents: lessons learned and prescriptive remedies. Decision Analyst, Inc. White Papers (2009). https://www.decisionanalyst.com/whitepapers/onlinerespondents/

4. Bredl, S., Storfinger, N., Menold, N.: A literature review of methods to detect fabricated survey data, Discussion Paper, Technical report (2011). https://www.econstor.eu/handle/10419/74449

5. Schraepler, J.-P., Wagner, G.G.: Identification, characteristics and impact of faked interviews in surveys: an analysis by means of genuine fakes in the raw data of SOEP, IZA Discussion Paper no. 969 (2003). http://hdl.handle.net/10419/20205

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1. Detection of Fabricated Survey Data using Clustering Analysis;2023 International Conference on Information Technology (ICIT);2023-08-09

2. Automation of cleaning and ensembles for outliers detection in questionnaire data;Expert Systems with Applications;2022-11

3. Automation of Cleaning and Ensembles for Outliers Detection in Questionnaire Data;SSRN Electronic Journal;2022

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