Foreword: Understanding and Enhancing Human Development Among Global Youth – On the Unique Value of Developmentally Oriented Longitudinal Research

The goal of developmental science is to describe, explain and optimize within-person change and between-people differences in within-person change across the life span (ontogeny) (Lerner, 2018). As such, repeated assessments of individuals across x-axis (temporal) divisions is the sine qua non of the essential features of developmental science research. In other words, to fulfil the goal of developmental science, longitudinal designs are required to obtain evidence contributing to understanding or to enhancing ontogenetic change (Collins, 2006). However, measuring one or more individuals at two or more successive ontogenetic points is a necessary but insufficient basis for developmental longitudinal research.

As is documented by the chapters across this volume, repeated measures must be taken at points of ontogeny for which there is a theoretical basis for expecting these measures (1) to assess changing features of (that is, variational or, ideally, transformational tipping points in) the process of development and/or (2) to constitute optimal ontogenetic points in which to enact interventions to enhance the course of developmental change. As is now understood by increasing numbers of developmental scientists, the development process involves relations between attributes of the individual and features of their tiered social, institutional, physical and cultural ecology, an ecology that changes integratively across history (for example, Bronfenbrenner and Morris, 2006; Elder et al, 2015; Overton, 2015).

Moreover, the developmental relations between an individual and the context are not independent. Contemporary theories of human development which emphasize that individual–context relations are mutually influential, represented most often in the literature as individual↔context relations, are at the cutting-edge of developmental science (Dick and Müller, 2017; Lerner, 2015, 2018). The individual–context relations depicted in these models involve dynamic and systematic relations across time and place (Elder et al, 2015). Dynamic models of human development processes require developmentally oriented longitudinal research to be tested.

The chapters in this volume both reflect and expand the theoretical, methodological and empirical foundations of this relational, dynamic developmental systems approach to using longitudinal methods to describe, explain and optimize the ontogenetic development of diverse global youth. Indeed, the chapters portray the ways in which developmentally oriented longitudinal research is uniquely suited to illuminate the features of both individuals and contexts that must be integratively studied to actualize the health and sustainable development of young people.

Banati (2020, p 1), noting that ‘At its heart, sustainable development is about families and communities living in peace and prosperity, their children growing up safe and healthy, and transitioning to productive adulthood’, presents a compelling vision of the way in which the levels of the ecology of human development must be aligned across a child’s lifespan to enable the child to thrive. Banati’s vision is richly illuminated by the chapters in this book: together, they demonstrate that when a child is embedded in families, communities and the broader ecology of human development that are marked by personal and contextual safety, when resources needed for physical and mental health and economic well-being are equitably distributed and accessible to the diverse youth and their families within a nation, then each child – across all instances of their specific individuality – will thrive across the life course; each child will have a pathway to an adulthood involving positive contributions to self, family, community and civil society.

The chapters in this book illuminate the manner in which the use of longitudinal methods enables integrative understanding of the specific dynamics of individual–context relations across time and place. The chapters document the singular contributions of developmentally predicated longitudinal methodology in depicting mutually influential relations between youth and context, point to the challenges involved in conducting high-quality developmentally oriented longitudinal research, and present important ideas for productively addressing these challenges.

Among the key assets of developmentally oriented longitudinal research documented across the chapters are the ways in which this approach to studying sustainable human development can identify not only variational (quantitative) change but also transformational (qualitative) change. In addition, the findings presented throughout the book illustrate the ways in which changing individual–context relations across the life course moderate sustainable human development. Moreover, the chapters also illustrate the fundamental importance of how time, and the coaction of its different instantiations (Elder, 1998) – ontogenetic, family and generational (historical) time – moderate the developmental process.

At the same time, this book illustrates the challenges of conducting theoretically predicated and methodologically rigorous, developmentally oriented longitudinal research. The study of developmental processes obviously involves the assessment of systematic and successive change and, within dynamic conceptions of this process, such assessment must involve the study of the individual, the context and the coaction of individual and context – that is, individual–context relations, including individual–individual relationships (see Overton, 2015, for a discussion of the three moments of developmental analysis involved in programmatic research framed by dynamic, relational developmental systems-based models). Accordingly, the approach to longitudinal methodology illustrated by the research in this book points to the need to integrate change-sensitive measures, research designs and data analysis procedures.

For example, measures must not only possess reliability and validity; they must also have at least strong measurement invariance across (obviously) ontogenetic (for example, age) time points, and across family and historical time points. In addition, measures must be invariant across other instances of individual specificity, for instance, gender, race, area of residence (such as rural or urban), socioeconomic status and culture. Clearly, then, using measures designed to index traits, which are attributes claimed to be invariant across time and place (for example, Costa and McCrea, 1980; McCrae et al, 2000), are of no use in a developmentally appropriate approach to measurement.

In addition, the designs of research must include repeated use of these measures at x-axis points selected on the basis of, ideally, theoretical understanding of key ontogenetic transition points in the developmental process under investigation or, at least, on the basis of empirical evidence that the selected x-axis points are potentially instances of such ontogenetic variation (Lerner, 2018). Moreover, the data-analytic procedures used to assess whether there is variation in the scores derived from change-sensitive measures employed to index individual, context and individual–context relations must be able to depict systematic within-individual change across successive x-axis points if, in fact, such systematic change occurs.

Here, however, lies a major challenge for extant developmentally oriented longitudinal research. Most approaches to data analysis in past longitudinal research have been variable-centred or, more recently, person-centred. Clearly, in a field defined as having its fundamental focus on within-person change, assessing how variables co-vary across ontogenetic time, or even how groups of individuals differ in how variables co-vary across ontogenetic time, respectively, are insufficient for depicting how variables co-vary within a specific individual across time and place (Rose, 2016; Molenaar and Nesselroade, 2015). Variable-centred and person-centred data analysis methods are associated with mathematical ideas found in the ergodic theorems, which involve assumptions of homogeneity across individuals and stationarity of the dynamic model of each member of a sample (Molenaar and Nesselroade, 2015). However, Bornstein (2017, 2019) has presented theoretical ideas (associated with dynamic models) and empirical evidence, in particular about the development of global youth, documenting the presence of a specificity principle for developmental analysis.

This principle underscores that, in addition to any nomothetic or group-differential attributes of a person, each individual has specific, idiographic attributes. Idiographic features of development exist because each child possesses specific physical/physiological attributes (including genetic and epigenetic characteristics; Molenaar, 2014; Slavich and Cole, 2013) and, as well, specific psychological, behavioural and social relationship attributes. Idiographic attributes of a person co-act dynamically with specific contextual attributes at specific times and places across specific periods of life, and this coaction increases the depth and breadth of a person’s specificity and, as well, constitute across time and place their specific pathway of ontogenetic development. Such specificity means that human development is not ergodic, that is, no two individuals (including monozygotic [MZ] twins; Richardson, 2017; Slavich and Cole, 2013) have the same characteristics of individuality within or across ontogenetic time points (Rose, 2016).

Of course, developmentally oriented longitudinal research about global youth should not abandon either variable or person-specific data-analytic foci. Such analytic foci should be used when they are predicated on the specific questions addressed in a specific investigation. Nevertheless, the importance of the specificity principle, coupled with the non-ergodic nature of human development, means that questions about person-specific change across ontogenetic points should be included in a complete programme of developmental science research and, as such, methods of idiographic data analysis, for instance, such as dynamic factor analysis (Molenaar and Nesselroade, 2015; Ram and Grimm, 2015), should be included in such scholarship.

Although such analyses provide information about the pathways of a specific person, data from a specific individual can be linked to idiographic data from one or more other individuals through integrative latent-variable data analysis procedures such as the idiographic filter (Molenaar and Nesselroade, 2012, 2015). The use of statistical procedures that enable latent-variable integrative data analysis (IDA) across multiple individuals is only one instance of such methods (Curran and Hussong, 2009). Indeed, IDA methods have also been used to integrate different variable-centred longitudinal data sets (for example, Callina et al, 2017) and, as such, they represent an instance of the future innovations in longitudinal methodology pointed to by Banati and by the authors of the chapters in this book for future advances in developmentally oriented longitudinal research about sustainable human development. Banati (2020), summarizing the significance of IDA methods, points to the value of linking information across longitudinal data sets. She notes that such links can elucidate both the generality and the specificity of development across variables, times and places. Contributing to such cross-study linkages thus increases the contribution of any single longitudinal investigation.

Of course, using IDA across studies will require more access by different investigators to the data sets collected by other researchers. Such collaboration may set the stage for another instance of contribution by different teams of developmentally oriented longitudinal researchers. As envisioned by Banati (2020), and as illustrated by different longitudinal projects she has assembled to contribute to this book, a more precise understanding of the course of sustainable human development can be realized if developmental scientists create cross-national, comparative – and what she terms harmonized – developmentally oriented longitudinal studies. There is already evidence that such harmonization is illuminating what is general and what is specific about individual–context relational pathways of sustainable life-course change among youth developing in different nations (for example, Lerner et al, 2018; Tirrell et al, 2019).

In sum, the set of longitudinal studies within this book portrays the value of developmentally oriented longitudinal research for providing theoretically and methodologically invaluable insight about the nature of the individual and context coactions involved in sustainable development across the life course. In addition, this book documents the unrivalled potential of such developmental science for evidence-based applications that will enable future developmental researchers to contribute increasing more useful information about how to equitably promote individual health and well-being and positive engagement with civil society. Promoting such development through enhancing the positive outcome of individual–context coactions will contribute to a more socially just life for all global youth.

More than any other existing volume, this book illuminates our present developmental–longitudinal knowledge base about sustainable youth development. In addition, this book is a beacon for future progress. It stands as an invaluable marker of where developmental science is and where it needs to go to create thriving for present cohorts of global youth and for cohorts to be born across the rest of this century.

Acknowledgements

The preparation of this foreword was supported in part by grants from the Chan Zuckerberg Initiative, Compassion International, the Templeton Religion Trust and the Templeton World Charity Foundation.

References

  • Banati, P. (2020) Introduction: measuring sustainable human development across the life course. In P. Banati (Ed) Sustainable Development Across the Life Course: Evidence from Longitudinal Research (pp 1–30). Bristol: Policy Press/Bristol University Press.

  • Bornstein, M.H. (2017) The specificity principle in acculturation science. Perspectives in Psychological Science 12(1): 3–45.

  • Bornstein, M.H. (2019) Fostering optimal development and averting detrimental development: prescriptions, proscriptions, and specificity. Applied Developmental Science 23(4): 340–45.

  • Bronfenbrenner, U. and Morris, P.A. (2006). The bioecological model of human development. In W. Damon and R.M. Lerner (Eds) Handbook of Child Psychology: Vol 1: Theoretical Models of Human Development (6th edn, pp 793–828). Hoboken, NJ: Wiley.

  • Callina, K.S., Johnson, S.K., Tirrell, J.M., Batanova, M., Weiner, M. and Lerner, R.M. (2017) Modeling pathways of character development across the first three decades of life: an application of integrative data analysis techniques. Journal of Youth and Adolescence 46(6): 1216–37.

  • Collins, L.M. (2006) Analysis of longitudinal data: the integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology 57: 505–28.

  • Costa Jr, P.T. and McCrae, R.R. (1980) Still stable after all these years: personality as a key to some issues in adulthood and old age. In P.B. Baltes and O.G. Brim Jr (Eds) Life Span Development and Behavior: Vol 3 (pp 65–102). New York: Academic Press.

  • Curran, P.J. and Hussong, A.M. (2009) Integrative data analysis: the simultaneous analysis of multiple data sets. Psychological Methods 14: 81–100.

  • Dick, A.S. and Müller, U. (Eds) (2017) Advancing Developmental Science: Philosophy, Theory, and Method. New York: Routledge.

  • Elder Jr, G.H. (1998) The life course and human development. In W. Damon and R.M. Lerner (Eds) Handbook of Child Psychology: Vol 1: Theoretical Models of Human Development (5th edn, pp 939–91). New York: Wiley.

  • Elder, G.H., Shanahan, M.J. and Jennings, J.A. (2015) Human development in time and place. In M.H. Bornstein and T. Leventhal (Eds) Handbook of Child Psychology and Developmental Science: Vol 4: Ecological Settings and Processes in Developmental Systems (7th edn, pp 6–54). Hoboken, NJ: Wiley.

  • Lerner, R.M. (2015) Preface. In R.M. Lerner (Editor-in-Chief) Handbook of Child Psychology and Developmental Science (7th edn, pp xv–xxi). Hoboken, NJ: Wiley.

  • Lerner, R.M. (2018) Concepts and Theories of Human Development (4th edn). New York: Routledge.

  • Lerner, R.M., Tirrell, J.M., Dowling, E.M., Geldhof, J., Gestsdóttir, S., Lerner, J.V. et al (2018) The end of the beginning: evidence and absences studying PYD in a global context. Adolescent Research Review 4(1): 1–14. doi.org/10.1007/s40894-018-0093-4

  • McCrae, R.R., Costa Jr, P.T., Ostendorf, F., Angleitner, A., Hrebícková, M., Avia, M.D. et al (2000) Nature over nurture: temperament, personality, and life span development. Journal of Personality and Social Psychology 78: 173–86.

  • Molenaar, P.C.M. (2014) Dynamic models of biological pattern formation have surprising implications for understanding the epigenetics of development. Research in Human Development 11: 50–62.

  • Molenaar, P.C.M. and Nesselroade, J.R. (2012) Merging the idiographic filter with dynamic factor analysis to model process. Applied Developmental Science 16: 210–19.

  • Molenaar, P.C.M. and Nesselroade, J.R. (2015) Systems methods for developmental research. In W.F. Overton and P.C.M. Molenaar (Eds) Handbook of Child Psychology and Developmental Science: Vol 1: Theory and Method (7th edn, pp 652–82). Hoboken, NJ: Wiley.

  • Overton, W.F. (2015) Process and relational developmental systems. In W.F. Overton and P.C.M. Molenaar (Eds) Handbook of Child Psychology and Developmental Science: Vol 1: Theory and Method (7th edn, pp 9–62). Hoboken, NJ: Wiley.

  • Ram, N. and Grimm, K.J. (2015) Growth curve modeling and longitudinal factor analysis. In W.F. Overton and P.C.M. Molenaar (Eds) Handbook of Child Psychology and Developmental Science: Vol 1: Theory and Method (7th edn, pp 758–88). Hoboken, NJ: Wiley.

  • Richardson, K. (2017) Genes, Brains, and Human Potential: The Science and Ideology of Human Intelligence. New York: Columbia University Press.

  • Rose, T. (2016) The End of Average: How We Succeed in a World That Values Sameness. New York: HarperCollins.

  • Slavich, G.M. and Cole, S.W. (2013) The emerging field of human social genomics. Clinical Psychological Science 1: 331–48.

  • Tirrell, J.M., Dowling, E.M., Gansert, P., Buckingham, M., Wong, C.A., Suzuki, S. et al (2019) Toward a measure for assessing features of effective youth development programs: contextual safety and the “Big Three” components of positive youth development programs in Rwanda. Child & Youth Care Forum 49(2): 201–22. https://doi.org/10.1007/s10566-019-09524-6

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