M.F.O. (Max Felix Orson) Welz

Max Felix Orson Welz
Erasmus School of Economics (ESE)
Erasmus University Rotterdam
ERIM PhD Candidate
Field: Marketing
Affiliated since 2020

PhD Track Robust methods for analyzing (big) rating-scale data

Rating-scale data are omnipresent in modern data collection, yet statistical methods typically do not take into account the peculiarities of such data. In addition, while the literature on robust (outlier-resistant) methods for continuous data is growing rapidly, such techniques are not suitable for rating-scale data due to their discrete nature and limited range. This project aims to fill those gaps in the statistical literature by developing robust methods for the analysis of rating-scale data.

Empirical research in the social sciences relies heavily on the statistical analysis of data measured on rating scales. Results of such research have a tremendous impact on society: for example, policy makers monitor opinions in society via rating-scale data collected in surveys, psychologists collect rating-scale data in experiments to gain new insights into human behavior, and companies let test groups rate various aspects of a new product before launching it.

Due to modern technology and the internet, data collection is easier than ever. As a result, data sets are growing ever larger but at the same time reliability of the data is decreasing with online data collection. Even though rating scales by definition have a limited range and thus do not exhibit extreme values, observations can still be outliers if they go against the correlation structure of the majority of the data. Such correlation outliers are likely to be present, especially in big data. Yet currently the literature on outliers in rating-scale data is very scarce.

The aim of this project is to develop outlier-resistant methods for the statistical analysis of rating-scale data, as well as to extract the relevant information from big (incomplete) rating-scale data.

Keywords
Auto-associative neural networks, outliers, rating scales, response styles, robust statistics, singular value decomposition
Time frame
2020 -

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