The Predictiveness of Personality Traits and Behavioral Sensing Data for Life Outcomes
Creators: name: Stachl, Clemens
Publisher: name: Leibniz Institut für Psychologie (ZPID)
Year: 2020
Subjects: subject: 150

description: Short summary: In this project we will investigate and compare the predictiveness of personality traits and objective sensing data from smartphones for life outcomes. Predicting life outcomes from Mobile Sensing Data Personality traits are generally described as relatively stable patterns of thought, feelings, and behaviors that are relevant in many parts of life. Further, personality traits are important because they predict important life outcomes (Ozer &amp; Benet-Martínez, 2006, Roberts, Kuncel, Shiner, Caspi &amp; Goldberg, 2007 Soto, 2019). Hence, knowing someone's personality is extremely useful to assess whether a person would be open to discuss ideas over a coffee, reliable to work with, good to have as a friend, or good to party with. While these findings are impressive, they are somewhat limited because they entirely rely on self-reported data from questionnaires and experience sampling and on in-sample correlations or regression modeling. The fundamental problems of self-reports have been known for a long time, especially with regard to behavioral data (Ellis, Davidson, Shaw &amp; Geyer, 2019, Furr, 2009, Gosling, John, Craik &amp; Robins, 1998, Paulhus &amp; Vazire, 2007). Until recently, it was extremely difficult to collect large amounts of objective data on how people actually behave. However, with the advent of new computing technologies in the last two decades, researchers now have the tools to collect and analyze data from objective quantifications of individual differences to determine the role psychological traits and states play for life outcomes (Boyd, Pasca &amp; Lanning, 2020). Especially promising are behavioral and situational data that can be gathered from smartphones (Harari, 2015, 2016, Miller, 2012). Further, it has been shown that these data are both associated with and predictive of personality traits at the factor and facet level (Harari, et al., 2019, Stachl et al., 2017, 2019). The richness, unobtrusiveness, objectivity and fine granularity of these data increasingly raises the question of whether self-reports should still be considered the gold standard or ground truth for the quantification of personality traits and individual differences (Boyd et al., 2020, Boyd &amp; Pennebaker, 2017). In particular this question is relevant if prediction rather than explanation is the primary goal. The predictiveness of personality traits (for behavior and life outcomes alike) has been frequently praised in the literature (Ozer &amp; Benet-Martínez, 2006, Roberts, Kuncel, Shiner, Caspi &amp; Goldberg, 2007 Soto, 2019). However, predictive modeling (i.e., out-of-sample model evaluation) has rarely been used in past research to evaluate this claim. While, similar in-sample associations might be observable across replicating studies, it will be more interesting to see if models for the prediction of life outcomes can be created based on self-reports of personality and in-vivo behaviors and how well the models generalize beyond individual samples (Soto, in press). This motivates us to compare how well state of the art self-report measures of psychological traits (Big Five personality traits), and sensing data from smartphones can be associated with, and predict life outcomes.