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Using Primary Dynamic Factor Analysis on repeated cross-sectional surveys with binary responses / Primär Dynamisk Faktoranalys för upprepade tvärsnittsundersökningar med binära svar

With the growing popularity of business analytics, companies experience an increasing need of reliable data. Although the availability of behavioural data showing what the consumers do has increased, the access to data showing consumer mentality, what the con- sumers actually think, remain heavily dependent on tracking surveys. This thesis inves- tigates the performance of a Dynamic Factor Model using respondent-level data gathered through repeated cross-sectional surveys. Through Monte Carlo simulations, the model was shown to improve the accuracy of brand tracking estimates by double digit percent- ages, or equivalently reducing the required amount of data by more than a factor 2, while maintaining the same level of accuracy. Furthermore, the study showed clear indications that even greater performance benefits are possible.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-165580
Date January 2020
CreatorsEdenheim, Arvid
PublisherLinköpings universitet, Artificiell intelligens och integrerade datorsystem
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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