As a developmental and family psychologist, I conduct research on the interplay between family systems processes and well-being across adolescence and young adulthood, situated in the larger socio-ecological context. In addition, with my data science background, I am particularly interested in applying innovative methods and data to family and developmental research. Research topics include:
- Families and youth in the digital context
- Machine learning for research on family experiences and youth achievements
- Family dynamics and adolescents’ and young adults’ development towards achievement
- Longitudinal dyadic data analysis of family processes
(1) Families and youth in the digital context.
Digital technology is ubiquitous and pervasive in individuals’ and families’ life. Thus, the digital context is an increasingly important context to consider in the social ecology of families and youth. How do family interactions unfold in the digital world? What are the implications of family digital interactions for family functioning and youth well-being?
To answer these questions, currently I am leading the Family Screenome Project, based on the Screenomics approach, to collect temporally dense, imaged-based data streams from smartphones of adolescents and parents, along with bi-weekly survey data on their relationship quality and well-being. This project is passively and unobtrusively collecting screenshots from participants’ smartphones every 5 seconds whenever the smartphone screen is activated, across up to 6 months. With rich, objective data (~360k screenshots per participant on average) collected from this innovative approach on parents’ and adolescents’ digital behaviors, many questions with regard to family dynamics, digital communication, media effects, and adolescent well-being can be answered. See below a demonstration of how parent-young adult smartphone interactions based on screenome observations can be analyzed across multiple time scales.
Further, on this topic, another part of my work is theoretical, trying to synthesize past research and push for building a theoretical framework for families and technology.
Sun, X., Ram, N., Reeves, B., Cho, M. J., Fitzgerald, A., Yang, X., & Robinson, T. N. Dynamic Characteristics of Young Adults’ Smartphone Interactions with Parents: Observing Screenomes in Multiple Timescales Manuscript under review for an invited special issue submission.
Sun, X. & Wang, Y. (2021, November). Typology of Research on Families and Technology: A Systematic Review. Workshop to be chaired at the TCRM conference at the National Council on Family Relations, virtual conference.
Sun, X., Reeves, B., Ram, N., Cho, M. J., Fitzgerald, A., & Robinson, T. N. (2020, November). Pulling Closer or Pushing Away? Ambivalence in the Dynamics Between Young Adults and Parents in the Digital Context. Paper presented at the National Council on Family Relations, virtual conference.
Sun, X., Robinson, T. N., Ram, N., Reeves, B., Cho, M. J., Chiatti, A., Lee, J., Roehrick, K., Yang, X., & Fitzgerald, A. (2020, July). Parent-Young Adult Communication in the Digital World: A Screenomics Paradigm. Paper presented at 6th International Conference on Computational Social Science (IC2S2), virtual conference.
Sun, X. & McMillan, C. (2018). The interplay between families and technology: Directions for future investigation. In J. Van Hook, S. McHale, & V. King (Eds). Families and technology, New York: Springer. (Authors have equal contributions)
(2) Machine learning for research on family experiences and youth achievements.
With booming big data and wide access to computational power, the utility of machine learning methods is gaining attention in social sciences. My research involves building machine learning (ML) models on large-scale datasets that use adolescent experiences as features in predicting their future achievement outcomes. For example, using the Add Health data, a nationally representative, large-scale dataset, I used ML to develop a paradigm for a data-based cross-study synthesis, examining the predictive value of high-dimensional data on adolescent family experiences for young adult educational achievement. Questions that I am trying to answer include, but not limited to: How important are family experiences in adolescence in predicting young adult achievement outcomes? Which family experience factors are the best predictors of achievements? What complex patterns among family experience in the predictions emerge (see example plots below)? Answering these questions using ML can not only facilitate theoretical understandings about families and youth development towards achievement, but can also inform precision intervention on promoting future achievement outcomes by targeting at important factors. As a fast-developing area, the application of ML paradigm in family and developmental research, I believe, will lead to a proliferation of new discoveries and practical implications, research papers and grants.
Love, B. & Sun, X. (2021, July). Big Data & Data Mining Approaches for Psychology Research. Invited talk for the UCL (University College London)-PKU (Peking University) Summer School in Experimental Design in Psychology. (Love and Sun had equal contributions)
Sun, X., Ram, N., & McHale, S. M. (2020). Adolescent family experiences predict young adult educational attainment: A data-based cross-study synthesis with machine learning. Journal of Child and Family Studies, 29, 2770-2785. https://doi.org/10.1007/s10826-020-01775-5
Sun, X., (2020, November). Introduction to Machine Learning for Family Research: Basic Concepts, Common Algorithms, and Application Examples. Workshop chaired at the National Council on Family Relations, virtual conference.
Sun, X. (2019, March). Leveraging Machine Learning Methods for Research on Child Development in the Big Data Era. Symposium chaired at the Biennial Meeting of Society for Research on Child Development, Baltimore, MD.
(3) Family dynamics and adolescents’ and young adults’ development towards achievement.
My work contributes to the understanding of long-term implications of early family experiences, especially those with parents and siblings across middle childhood and adolescence, for achievement outcomes in young adulthood. By incorporating the experiences of multiple family members, my research highlights within-family differences including between mothers and fathers and between siblings, as well as the role of gender in developmental outcomes. Further, my work examines contexts for family dynamics, especially the cultural context and family members’ work experiences. I conduct this line of work in collaboration with Susan McHale (Penn State), Kimberly Updegraff (Arizona State University), and Adriana Umaña-Taylor (Harvard University).
Sun, X., McHale, S. M., Updegraff, K. A., & Umaña-Taylor, A. J. (2021). Implications of Mexican-origin youth’s work experiences for relationships with fathers. Cultural Diversity and Ethnic Minority Psychology. [online first] https://doi.org/10.1037/cdp0000468
Sun, X., McHale, S. M., & Updegraff, K. A. (2020). Career adaptivity mediates longitudinal links between parent-adolescent relationships and young adult occupational attainment. Developmental Psychology, 56, 2309-2321. https://doi.org/10.1037/dev0001120
Sun, X., McHale, S. M., & Updegraff, K. A. (2019). Sibling experiences in middle childhood predict sibling differences in college graduation. Child Development, 90, 25-34. https://doi.org/10.1111/cdev.13047
(Media mentions: NICHD, ScienceDaily, Penn State News, The University Network, MedicalResearch, 知识分子)
Sun, X., McHale, S. M., & Updegraff, K. A. (2019). Sibling dynamics in adolescence predict young adult orientations to couple relationships: A dyadic approach. Manuscript in press by Journal of Adolescence, 77, 129-138. https://doi.org/10.1016/j.adolescence.2019.10.014
Sun, X., McHale, S. M., & Updegraff, K. A. (2017). Maternal and paternal resources across childhood and adolescence as predictors of young adult achievement. Journal of Vocational Behavior, 100, 111-123.
Free full text from NIH
(4) Longitudinal dyadic data analysis of family processes.
Family systems are complex, and I have been interested in applying methods to capture their complexity. One step is applying dyadic data analytics to examine whether and how partners’ experiences and individual characteristics are linked to their own and to the other’s relationship experiences. In this research line, I have focused on actor-partner interdependence modeling, especially the adaptation of this method to analysis of longitudinal data collected from family dyads, including couple, parent-youth, and sibling dyads. Findings highlight the interdependence of family members’ experiences over time, and gender asymmetry in influence processes.
Sun, X., McHale, S. M., & Crouter, A. C.(2020). Perceived underemployment and couple relationships among African American parents: A dyadic approach. Cultural Diversity and Ethnic Minority Psychology, 26, 82-92. https://doi.org/10.1037/cdp0000285
(Media mentions: APA Spotlight, Penn State News)
Skinner, O. D., Sun, X., & McHale S. M. (2020). Longitudinal linkages between expressivity and parent- youth relationships in African American families: A dyadic approach. Journal of Child and Family Studies, 29, 442-454. https://doi.org/10.1007/s10826-019-01578-3
Lawson, K. M., Sun, X., & McHale, S. M. (2019). Family-friendly for her, longer hours for him: Actor-partner model linking work-family environment to work-family interference. Journal of Family Psychology, 33, 444-452. https://doi.org/10.1037/fam0000506
Sun, X., McHale, S. M., Crouter, A. C., & Jones, D. E. (2017). Longitudinal links between work experiences and marital satisfaction in African American dual-earner couples. Journal of Family Psychology, 31, 1029-1039. http://dx.doi.org/10.1037/fam0000381