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Over the last decade, a large subset of cyberpsychology research has investigated the relationship between technology (e.g. smartphone, internet, and social media) use in young adults, and individual differences in a range of psychological constructs such as satisfaction with life (SWL), locus of control, impulsivity, narcissism, loneliness, and depression, anxiety, and stress (DAS). However, this body of work has almost exclusively relied on the use of self-reports when measuring technology use. Self-reports of technology use have been previously shown to have a weak relationship with the objective behaviors that these measures aim to capture. As a result, some have feared that the extant findings in this field may be invalid or unreliable in terms of alignment with objectively measured behaviors. Furthermore, the absence of behavioral sensing methods in this field also suggests that little is known about the precise patterns of technology use that may characterize individual differences in these constructs.My dissertation is centered around a large behavioral sensing study of 271 young adults, wherein participants had their smartphone and personal computer activity logged continuously for a month long period, and completed a wide set of different psychological surveys, technology use surveys, as well as weekly and daily surveys on their mental well-being. From this data, my dissertation aimed to broadly: (1) evaluate the validity of past self-report based findings in this field using objective behavioral data, (2) further investigate the strength of associations and discrepancies between self-reports and objective measures of technology use, (3) discover novel objective behaviors related to each of these psychological constructs, and evaluate how well these behavioral features can predict individual differences in these constructs in a machine learning context.Across 30 hypotheses tested, I show that just 27.2% of hypotheses derived from studies employing self-report assessments, and 75% of hypotheses derived from studies employing behavioral sensing methods, were supported to some degree. Furthermore, I show that commonly problematic technology use scales had relatively weak associations with all objective device usage measures I generated. I also provide further evidence that self-report measuresof specific technology use (e.g. time on smartphones) appear to have at most a moderate association with logged measurements, and suffer from other patterns of systematic biases. From my exploratory analysis, I discovered novel behaviors associated with individual differences in SWL and loneliness that were significant after correcting for false discoveries, and present evidence that SWL, loneliness, and DAS, appear to have a stronger relationship with observable differences in technology use compared to locus of control, impulsivity or narcissism.Throughout my dissertation, I discuss how integration of behavioral sensing methods into this field provides the opportunity to improve the robustness, precision, reproducibility, and novelty of future findings regarding the relationship between technology use and individual differences in psychological phenomena.

Personal visualizations present a separate class of visualizations where users interact with their own data to draw inferences about themselves. In this paper, we study how a realistic understanding of personal visualizations can be gained from analyzing user interactions. We designed an interface presenting visualizations of the personal data gathered in a prior study and logged interactions from 369 participants as they each explored their own data. We found that the participants spent different amounts of time in exploring their data and used a variety of physical devices which could have affected their engagement with the visualizations. Our findings also suggest that the participants made more comparisons between their data instances than with the provided baselines and certain interface design choices, such as the ordering of options, influenced their exploratory behaviors., EuroVis 2020 - Short Papers, Mix: Color, Design, etc., 79, 83, Poorna Talkad Sukumar, Gonzalo J. Martinez, Ted Grover, Gloria Mark, Sidney K. D'Mello, Nitesh V. Chawla, Stephen M. Mattingly, and Aaron D. Striegel