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Identifikation av icke-representativa svar i frågeundersökningar genom detektion av multivariata avvikare
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2014 (Swedish)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the character of the Väljarbarometern dataset, three existing algorithms are adapted to detect these outliers. Also, a number of randomly generated observations are added to the data, by all algorithms correctly labelled as outliers. The resulting anomaly scores generated by each algorithm are compared, concluding the Otey algorithm as the most effective for the purpose, above all since it takes into account correlation between variables. A plausible cut-off value for outliers and separation between non-representative and representative outliers are discussed. The resulting recommendation is to handle observations labelled as outliers through respondent follow-up or if not possible, through downweighting, inversely proportional to the anomaly scores. 

Place, publisher, year, edition, pages
2014. , 36 p.
Series
UPTEC STS, ISSN 1650-8319 ; 14003
Keyword [en]
outliers, multivariate detection, satisficing, surveys, anomaly score, representativity
Keyword [sv]
avvikare, multivariat detektion, satisficing, frågeundersökning, anomalipoäng, representativitet
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-219546OAI: oai:DiVA.org:uu-219546DiVA: diva2:700184
External cooperation
United Minds
Subject / course
Statistics
Educational program
Systems in Technology and Society Programme
Presentation
2014-01-24, Å64119, Ångströmslaboratoriet, Uppsala, 18:31 (Swedish)
Supervisors
Examiners
Available from: 2014-04-08 Created: 2014-03-03 Last updated: 2014-04-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
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  • Other locale
More languages
Output format
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