What Does a Personality Look Like?

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1 What Does a Personality Look Like? Arman Daniel Catterson University of California, Berkeley 4141 Tolman Hall, Berkeley, CA ABSTRACT In this paper, I describe a visualization system designed to provide comprehensive, comprehensible, and compelling personality feedback to users. In contrast to previous visualization platforms, the visualization described in this paper integrates multiple personality trait variables over time, and displays features of the situation and social interaction. The results of a user experience study suggest that participants viewing this feedback were more accurate in the interpretation of descriptive statistics and were more engaged than participants who viewed a visualization of traditional descriptive statistics. Together, these results suggest that this dynamic display of personality represent a significant improvement over previous visualizations. Author Keywords Personality; user feedback; experience sampling methods; time-series data; data-visualization INTRODUCTION The rise of big data presents both new opportunities and challenges for the collection, synthesis, and visualization of information about people. Personality psychologists test theories about individual differences by collecting information about people from a wide variety of different sources [6]. For example, one important dimension of personality is the extent to which a person is introverted or extraverted. This dimension can be measured in terms of the person's self-report (e.g., I think that I am outgoing, I'm not very shy), in terms of the person's reputation (e.g., other people think that I'm outgoing and not shy), and specific behaviors (e.g., I go to a lot of parties, I spend a lot of time with other people). As mobile computing power becomes more affordable and accessible, new technologies allow personality researchers to assess a person's behavior, emotion, and self-perceptions with greater precision and complexity than before. For example, whereas personality researchers historically measured individual differences through aggregate scales, administering multiple personality scales via mobile assessment allows researchers to examine not only betweenperson differences (i.e., how the person differs from others) but also within-person differences (i.e., how the person differs from his or her own average) [1]. This trend toward a high level of precision in measuring people's behavior extends far beyond the narrow field of personality psychology. The rising popularity of consumer products such as wearables that utilize behavioral sensors to collect information about a person's movement over the course of the day and interpret that information in terms of her or his activity level over time demonstrates that people are increasingly interested in collecting and visualizing information information about the self [5]. As such 'Quantified Self' technology continues to grow in popularity and complexity, researchers, developers, and consumers face a challenge in comprehensively and comprehensibly integrating these different sources of data into an integrated narrative. The effective visualization of complex information about the self is a challenge that existing personality feedback visualization tools are not equipped to solve. In this paper, I describe ways in which existing visualization tools can be integrated to present a more comprehensive and integrative view of a person's personality, and the results of a user experience study to gauge the effectiveness of this method. RELATED WORK The current research was informed in part by past attempts to visualize information about the self. The overwhelming majority of past personality visualizations are focused on describing where a person's 'average' trait tendency falls on some distribution. For example, Gosling & Potter's designed

2 one of the most popular scientific personality survey websites, and provide personality feedback to incentive participation [4]. On this site (outofservice.com), they display feedback as the user's average level of a given personality dimension on a spectrum. Such visualizations are based on a view of personality that is characterized by describing meanlevel differences, and are not able to capture how the participant's personality changes over time and place. Unfortunately, to the author's knowledge almost all other personality feedback websites provide users with average reports. As experience sampling methods have grown in popularity, researchers have begun to visualize withinperson variation. For example, Fleeson [1] compared within-person variance to between-person variation by plotting the standard deviations as bar graphs. While this visualization effectively demonstrated his point that people differ as much from others as they differ from their own average self, it was not designed with participant feedback in mind. For example, the bivariate relationships between personality variation and time (e.g., how the person's extraversion changes over time and situational contexts?), or personality covariation (e.g., does a person change in extraversion in the same way that s/he changes in status?) were not plotted. More recently, Killingsworth [3] developed a popular mobile application (TrackYourHappiness) that measures people's happiness over time. Unlike other visualizations, this personality visualization displays bivariate correlational relationships to participants, and allows participants to examine how their personality changes over time. However the application does not display every bivariate relationship, but instead focuses on relationships between happiness and other variables; users are thus limited by the researcher to what patterns and trends they can examine. In all existing personality visualizations, users are not given the ability to make their own decisions about what relationships they examine, or what variables are plotted. Users thus do not make inferences on their own, but instead are explicitly informed by researchers which relationships are important. MOTIVATION FOR THE CURRENT RESEARCH This research was motivated by three broad goals. First, I sought to design a personality visualization that was comprehensive in its integration of multiple personality variables manifest across multiple time points and multiple situations. Whereas past personality visualizations display at most bivariate relationships, I sought to integrate multiple behavioral, self-rated, and other-rated variables in one visualization. Second, I sought to make a personality visualization that was comprehensible to viewers, even without the explicit display of descriptive statistics. Would users be able to correctly infer the means, standard deviations, and correlational relationships across multiple personality variables when those descriptive statistics are not explicitly displayed? Third, I sought to create a visualization that was compelling for users; something that users would find more interesting than traditional personality feedback visualizations. Experience sampling method surveys often require participants to complete multiple item surveys multiple times a day for multiple days. Attrition rates for ESM studies can be quite high. Might a more compelling and interactive visualization interest users more than more traditional personality visualizations? VISUALIZATION METHODS Description of Data I based my visualization on pilot data collected in Fall 2013 for my dissertation. In this study, participants completed six surveys a day for six days. To narrow the scope of my visualization, I randomly selected one participant from this dataset who completed the majority (97%) of assessments. I also sought to select a variety of self-reported traits, situational contexts, and social interactions from the larger dataset to visualize. Specifically, I selected three personality variables extraversion, social status, and authenticity. These numeric personality variables were collected via self-reports on a scale from 1-5.

3 Participants in this study also described what they were doing during each time point (a string variable) through open-ended response format, and described the number of other people they were interacting with through a drop-down menu with five answer choices: alone, with one other person, in a small group of 3-5 people, in a large group of 6-10 people, and in a crowd 11+ people). Together, these variables represent numeric, string, and categorical variables. Description of Development Process I began my visualization process by identifying the different kinds of data I wanted to map in my visualization. I used d3 to build my visualization in order to ensure that users could access personality feedback across a variety of on web-based browsers and mobile devices, take advantage of interactive features, and generate visuals based on changing data. With little programming experience of my own, I based my visualization on Mike Bostock's line chart (# ). Because my data were collected over multiple time points, it made sense to create a timeseries plot and map time to the x-axis of the graph. Because users would likely want to examine how different personality variables change over time and personality variables were continuous - I mapped the person's self-rated personality variable to the y-axis. To avoid overplotting, I decided to only display one personality variable at a time. However, in order to allow users to examine multiple personality variables, I adapted Mike Bostock's chained transition (# ) to switch between different personality variables, disabling the rescaling of the y-axis to ensure that the full spectrum of the 1-5 scale was used for all visualizations. Because the question about 'what are you doing right now' was an open-ended response variable, users potentially could provide up to 36 different answers (one for each time point). In order to effectively map these 36 different responses to data, I adapted Bostock's x-value mouseover (# ) so a user could see what he or she reported doing at each time point by hovering a mouse over the time point. With only five levels, I was able to map the number of people a person was with to the radius of the circle. This was one feature I was able to do with my own knowledge of syntax. DESCRIPTION OF VISUALIZATION An interactive version of the visual can be viewed here: A static version of this visual is presented below in Figure 1. This personality visualization successfully integrates time-series data, self-reported personality data, features of the situation, and features of the social interaction into one comprehensive and interactive image. Furthermore, this visualization is flexible; users can potentially switch between an infinite number of, and examine personality changes across a variety of different times. Figure 1. Screen-shot of the Personality Visualization There are several limitations to this visualization that I was not able to address in the design stage. First, assessment number is not the most descriptive way to illustrate time. In this study, time points are linked to real days and hours information that would be good to represent through more accurate labeling of the x- axis. Second, I was not able to successfully allow participants to plot multiple personality variables at the same time. Nevertheless, I believe my visualization successfully improves on previous personality feedback systems by integrating four different kinds of personality variables in one visualization. USER EXPERIENCE STUDY While this visualization is objectively more comprehensive than previous visualizations, it is unclear whether my other goals were met. Would users of this dynamic visualization be able to correctly interpret descriptive statistics that are not explicitly reported (such as the mean, standard deviation, and correlational relationship between different variables)

4 as accurately as users of a static visualization, or users who were given more traditional forms of personality feedback? Would users find this visualization more compelling than other visualizations? To evaluate whether this visualization was comprehensible and compelling for users, I designed and conducted an online user experience study. Participants Participants were 200 workers recruited through Amazon's Mechanical Turk. Participants were required to be based in the US, took on average 8 minutes to complete the study, and were compensated an effective hourly wage of $8. Procedures After signing up for the study, participants were randomly assigned to one of three conditions. In each condition, participants were provided a link to a visualization that opened a pop-up window in a separate screen. In one condition, participants received a link to the dynamic visualization described above. In a second condition, participants received a link to a static version of this dynamic visualization. In this static condition, all the interactive elements of the visualization were removed each point was labeled with the description of the situation that the person was in, and the three different personality visualizations were organized on top of each other. In the control condition, participants received a link to a collection of descriptive statistics that displayed the mean, standard deviation, and correlational Subjective interest. To measure interest and engagement in the visualization, we asked participants whether they would be interested in receiving feedback like this, whether other people would be interested in receiving feedback like this, and whether they thought that personality feedback is interesting on a scale from 1 (Strongly Disagree) to 5 (Strongly Agree). These measures were highly correlated (α =. 80) and were thus averaged into a single index. Objective interest. We also provided participants the opportunity to sign up for a personality study that would provide them feedback. Participants indicated whether they would like to sign up ( Yes or No ) and provided their e- mail address. relationship between the different variables. Critically, all conditions contained the exact same amount of information the only difference was the way that this information was displayed. Measures Subjective comprehension To measure participants' subjective sense of comprehension, participants were asked whether the feedback was easy to interpret and whether they found this personality feedback confusing. These items were only moderately correlated (r(198) =.24, p <.05), and were thus analyzed separately. Objective comprehension To assess participants' objective ability to comprehend the descriptive statistics, participants were asked questions about the average, standard deviation, and pairwise correlational relationships for each of the three different personality variables. Questions about means and standard deviations were rated on a scale from 1-5 (the full range of the measures). Questions about correlational relationships were rated on a scale from 0-1, with an instruction that A perfect relationship means that when one variable increases (or decreases), the other variable increases (or decreases) by the exact same amount. No relationship means that change in one variable means nothing about change in the other. Results I conducted a series of linear regressions, predicting measures of subjective and objective comprehension and interest from condition a dummy coded categorical variable with the dynamic visualization set as the reference group (i.e., the intercept), and static and control visualizations set as linear contrasts. In all analyses reported below, the unstandardized regression effects describe differences between my dynamic visualization and the static and traditional visualizations. Did participants find the dynamic visualization less subjectively comprehensible than other visualizations? There were no significant differences in the ease of interpretation, participants found all three visualizations relatively easy to interpret. Participants

5 Figure 2. Differences in Objective Comprehension

6 rated the dynamic visualization as significantly more confusing than the control condition (b = -.44, p <. 05). However, this mean confusion rating for the dynamic visualization (M = 2.85) was below the midpoint of the scale. Open-ended comments suggest that users found the animation that occurred in transition between personality variables particularly confusing; this is discussed more in the discussion section. Did participants find the dynamic visualization less objectively comprehensible than other visualizations? The effects of condition on objective comprehension ratings are displayed in Figure 2. Each graph represents participants' ratings of a specific descriptive statistic for a specific personality variable. Whereas the bar in the graph represents the participants' rating of that descriptive statistic, the solid line that crosses the bars represents the actual descriptive statistic for that variable. The first row contains ratings for the average, the second row contains ratings for the standard deviation, and the third row contains ratings of the correlational relationships. The dynamic visualization allowed participants to successfully interpret the descriptive statistics for all three personality variables, and for the three pairwise correlation coefficients. There were no significant differences in how participants rated the mean of each of the three personality variables. Participants were very accurate at describing the mean, even when that mean was not explicitly provided. Although participants tended to overestimate the standard deviation of the personality variables, this tendency was seen across conditions. Furthermore, participants were significantly more likely to overestimate the standard deviation of authenticity in the control condition (b =.74, p <.01). Participants also tended to overestimate the correlational relationships among the different personality variables. However, participants were significantly more likely to overestimate the relationship between authenticity and status (b =.22, p <.01) and authenticity and extraversion (b =.15, p <. 01) in the control condition. Did participants find the dynamic visualization more subjectively interesting than other visualizations? Participants did not rate the dynamic visualization as significantly more interesting than either the static visualization (b =.03, p =.82) or the control condition (b = -.07, p =.64). Did participants find the dynamic visualization more objectively interesting than other visualizations? To test whether participants were more likely to sign up for a personality study in the condition where they viewed the dynamic feedback, I tested a generalized linear model predicting sign up (yes vs. no) from condition. Participants were, on average, around 20% more likely to sign up for a future study when the participant viewed the dynamic feedback than when viewing the other conditions (odds ratio = 2.30, p <. 01). DISCUSSION This visualization successfully integrated multiple personality variables into an integrated narrative of a person s week. Whereas past visualizations typically display bivariate relationships, my visualization integrates four personality variables in a single image. Furthermore, the results of a user experience study suggest that people were able to accurately interpret descriptive statistics including mean, standard deviation, and correlational relationships from this visual, and were significantly more likely to sign up for a study that would provide them with their own dynamic personality feedback. Together, these findings demonstrate that an integrative solution to the display of multivariate personality data has the potential to be a more engaging and accurate feedback delivery system. FUTURE WORK The results from this user experience study suggest directions for future development of this visualization, as well as ways this visualization might be incorporated in research. Future Development First, this visualization does not allow users to plot bivariate relationships between multiple personality variables. Users might be more accurate about correlational relationships between personality

7 variables if they can see two lines simultaneously on the same figure.. Furthermore, the user experience research suggests that users were ; this suggests that adding an 'average' line (as I had originally planned to do) is perhaps less important than adding different kinds of comparison lines to the graph. Second, users commented that the animation. A future version of this visualization might take advantage of mapping time to animation, such that when users select to display a new personality variable, the timeseries plot builds from the left-hand side of the plot (when time = 0) to the right-hand side of the plot (when time = n), allowing users to see how a personality variable unfolds in accelerated time. Third, future development will also focus on incorporating other features to allow additional variables to be mapped to data. Other features, such as brushing and dodging, might allow users to filter the time-series data to highlight interactions with specific people or specific situations. Fourth, one feature missing from many personality visualizations including this visualization - is the ability to compare one person's personality to another. Future research might consider how comparisons between people would enhance participation rates (i.e., users could share a link with others that would allow friends and family members to make comparisons to each other). Future Research Directions Beyond additional features, more work needs to be done to integrate this feedback as part of a comprehensive system that allows researchers and users to define their own questions that assess personality constructs, features of the situation, and features of the social interaction. Indeed, one major limitation of the evaluation study is that participants were not the subject of the personality visualization; participants evaluated the personality of a random other person. Ratings of interest, comprehension, and engagement may be more pronounced when participants are evaluating their own personality. A system that integrates surveys with feedback will also allow researchers to examine the effect of realtime feedback on behavior change. Might participants be more likely to gain insight about maladaptive emotional states, such as the situational and social sources of stress and anxiety when they are provided comprehensive feedback about their patterns of everyday life? Initial results from this study are encouraging of this possibility; in addition to the measures described in this paper, participants were also asked to provide explanations for what the person was like, things that they found interesting, and why s/he varied in extraversion across the situation. A more rigorous qualitative analysis of these data goes beyond the scope of this final project, however an initial look reveals that people generated more complex interpretations of personality that emphasize causal relationships between situational factors and individual differences when given the dynamic visualization. In future research, I might work with cognitive-behavioral therapists to incorporate instructions to help guide users through the self-data exploration process and help facilitate real behavior change. ACKNOWLEDGMENTS The author wishes to thank Drs. Maneesh Agrawala and Jessica Hullman for their instruction and advice throughout the semester (and for reading this far). classmates in CS for their thoughtful comments on an early version of this project, and the many developers of d3 for making it so easy. REFERENCES 1. Fleeson, W. (2001). Toward a structure-and process-integrated view of personality: Traits as density distributions of states. Journal of personality and social Psychology, 80(6), Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330(6006), Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or

8 persistent change? Journal of Personality and Social Psychology, 84, Swan, M. (2012). Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3), Vazire, S. (2010). Who knows what about a person? The self other knowledge asymmetry (SOKA) model. Journal of personality and social psychology, 98(2), 281. The columns on the last page should be of approximately equal length. Remove these two lines from your final version.

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