No abstract

Experience sampling (ESM), diary, ecological momentary assessment (EMA), ambulatory monitoring, and related methods are part of a research tradition aimed at capturing the ongoing stream of individuals’ behavior in real-world situations. By design, these approaches prioritize ecological validity. In this paper, we examine how the purported ecological validity these study designs provide may be compromised during data analysis. After briefly outlining the benefits of EMA-type designs, we highlight some of the design issues that threaten ecological validity, illustrate how the typical multilevel analysis of EMA-type data can compromise generalizability to “real-life”, and consider how unobtrusive monitoring and person-specific analysis may provide for more precise descriptions of individuals’ actual human ecology.

This study describes when and how adolescents engage with their fast-moving and dynamic digital environment as they go about their daily lives. We illustrate a new approach— screenomics—for capturing, visualizing, and analyzing screenomes, the record of individuals’ day-to-day digital experiences. Sample includes over 500,000 smartphone screenshots provided by four Latino/Hispanic youth, age 14 to 15 years, from low-income, racial/ethnic minority neighborhoods. Screenomes collected from smartphones for 1 to 3 months, as sequences of smartphone screenshots obtained every 5 seconds that the device is activated, are analyzed using computational machinery for processing images and text, machine learning algorithms, human labeling, and qualitative inquiry. Adolescents’ digital lives differ substantially across persons, days, hours, and minutes. Screenomes highlight the extent of switching among multiple applications, and how each adolescent is exposed to different content at different times for different durations—with apps, food-related content, and sentiment as illustrative examples. We propose that the screenome provides the fine granularity of data needed to study individuals’ digital lives, for testing existing theories about media use, and for generation of new theory about the interplay between digital media and development.

Family systems theorists have forwarded a set of theoretical principles meant to guide family scientists and practitioners in their conceptualization of patterns of family interaction – intra-family dynamics – that, over time, give rise to family and individual dysfunction and/or adaptation. In this paper, we present an analytic approach that merges state space grid methods adapted from the dynamic systems literature with sequence analysis methods adapted from molecular biology into a “grid-sequence” method for studying inter-family differences in intra-family dynamics. Using dyadic data from 86 parent-adolescent dyads who provided up to 21 daily reports about connectedness, we illustrate how grid-sequence analysis can be used to identify a typology of intra-family dynamics and to inform theory about how specific types of intra-family dynamics contribute to adolescent behavior problems and family members’ mental health. Methodologically, grid-sequence analysis extends the toolbox of techniques for analysis of family experience sampling and daily diary data. Substantively, we identify patterns of family-level micro-dynamics that may serve as new markers of risk/protective factors and potential points for intervention in families.

Methodologically, grid-sequence analysis extends the toolbox of techniques for analysis of dyadic experience sampling data. Substantively, we identify patterns of dyad-level microdynamics that may serve as new markers of risk/protective factors and potential points for intervention in older adults' proximal context.