Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing

Social interactions are crucial to affective well-being. Still, people vary interindividually and intraindividually in their social needs. Social need regulation theories state that mismatches between momentary social desire and actual social contact result in lowered affect, yet empirical knowledge about this dynamic regulation is limited. In a gender- and age-heterogenous sample, German-speaking participants (N = 306, 51% women, Mage = 39.41, range 18-80 years) answered up to 20 momentary questionnaires about social interactions and affect while mobile sensing tracked their conversations, calls, and app usage over 2 days. Combining preregistered and exploratory analyses, we investigated how momentary affect relates to social dynamics, focusing on two states of mismatch between social desire and social contact: social deprivation (i.e., being alone but desiring contact) and social oversatiation (i.e., being in contact but desiring to be alone). We used specification curve analyses to scrutinize the operationalization of these constructs. Social oversatiation was associated with decreased positive affect and increased negative affect. Social deprivation, however, was unrelated to affect. Exploratory multilevel models showed that a higher desire to be alone was consistently associated with decreased affective well-being, whereas a higher desire for social contact was related to increased affective well-being. Mobile sensing data revealed differential association patterns between affect and face-to-face versus digital communication. We discuss implications for social need regulation, related studies on voluntary solitude, and advantages of combining experience sampling and mobile sensing assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychological well-being in Europe after the outbreak of war in Ukraine

The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual's personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.

Snapshots of daily life: Situations investigated through the lens of smartphone sensing

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people's thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations' Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies

Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data.

Personality Research and Assessment in the Era of Machine Learning

The increasing availability of high–dimensional, fine–grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone–sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.

Personality Sensing for Theory Development and Assessment in the Digital Age

People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine–grained records of everyday personality expression. In this paper, we present a conceptual framework and empirical illustration for personality sensing research, which leverages sensing technologies for personality theory development and assessment. To further empirical knowledge about the degree to which personality–relevant information is revealed via such data, we outline an agenda for three research domains that focus on the description, explanation, and prediction of personality. To illustrate the value of the personality sensing research agenda, we present findings from a large smartphone–based sensing study ( N = 633) characterizing individual differences in sensed behavioural patterns (physical activity, social behaviour, and smartphone use) and mapping sensed behaviours to the Big Five dimensions. For example, the findings show associations between behavioural tendencies and personality traits and daily behaviours and personality states. We conclude with a discussion of best practices and provide our outlook on how personality sensing will transform our understanding of personality and the way we conduct assessment in the years to come. © 2020 European Association of Personality Psychology

Digital Footprints of Sensation Seeking

Abstract. The increasing usage of new technologies implies changes for personality research. First, human behavior becomes measurable by digital data, and second, digital manifestations to some extent replace conventional behavior in the analog world. This offers the opportunity to investigate personality traits by means of digital footprints. In this context, the investigation of the personality trait sensation seeking attracted our attention as objective behavioral correlates have been missing so far. By collecting behavioral markers (e.g., communication or app usage) via Android smartphones, we examined whether self-reported sensation seeking scores can be reliably predicted. Overall, 260 subjects participated in our 30-day real-life data logging study. Using a machine learning approach, we evaluated cross-validated model fit based on how accurate sensation seeking scores can be predicted in unseen samples. Our findings highlight the potential of mobile sensing techniques in personality research and show exemplarily how prediction approaches can help to foster an increased understanding of human behavior.