Differential human factors in user data

Stachl, Clemens (2017): Differential human factors in user data. Dissertation, LMU München: Faculty of Psychology and Educational Sciences

Abstract

This thesis investigates how differential human factors, such as demography and personality, are related to actual individual behavior. Within this broad context, this work addresses the prevailing lack of real behavior in the scientific field of psychology and differential-/social psychology in particular. Furthermore, this work provides an introduction to the practice of data-logging as a promising alternative to self-reports for the collection of behavioral data. Additionally, we introduce new data-analytical concepts from the field of machine learning in order to appropriately handle large and noisy datasets, such as technical logs. To illustrate these concepts we provide three empirical studies, using behavioral logging procedures. In the first study we report on data obtained in a virtual automotive driving simulation. Using these data, we demonstrate how individual driving patterns can be used to predict driver gender with high accuracy from basic automotive driving logs. Additionally, we provide information about the most important variables associated with male and female driving styles. Two additional studies utilize a specially designed Android application, to automatically collect behavioral user data in a privacy protecting manner from participants private smartphones. The second study describes how most stable mobile application usage on smartphones can be predicted from individual personality and demography scores and highlights implications for personality sensitive recommender systems. The third study demonstrates how individual personality can potentially be predicted, using a wide range of user interactions, with a machine learning approach. Finally, we discuss the reported results within the context of previous research and highlight possible implications of technological advancements for psychological science.

• Item Type: Theses (Dissertation, LMU Munich)
• Keywords: Data Logging, Machine Learning, Personality, Behavior, Smartphone Usage
• Subjects: 100 Philosophy and Psychology; 100 Philosophy and Psychology > 150 Psychology; 300 Social sciences; 300 Social sciences > 310 General statistics
• Faculties: Faculty of Psychology and Educational Sciences
• Language: English
• Date of oral examination: 7. February 2017
• 1. Referee: Bühner, Markus
• Signature of the printed copy: 0001/UMC 24527