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Longitudinal research aspires to: 1) understand developmental processes, 2) discover the determinants of developmental success, and 3) identify changeable factors as targets for interventions aimed at increasing such success. Linear causal models are too simplistic when dealing with complex interactions between individuals and contexts. Although our models for understanding relationships have evolved from linear to interactional with multiple feedback loops, we often follow the rule of Occam’s Razor and fall back on simple linear models when conceptualizing human development across time. However, there is clearly a complex interplay between the individual, his/her context, and the experiences elicited by the individual. In addition, larger cultural and time-period forces that vary from generation to generation organize an individual’s (as well as a researcher’s) experience over time.

Longitudinal researchers themselves have undergone a historical evolution from: psychologists assessing trait-like constructs (e.g., IQ) over time, to developmental psychologists emphasizing the changes over time in such areas as cognition (Piaget) or attachment (Bowlby and Ainsworth), to developmental scientists requiring multidisciplinary understanding of social institutions, cultural variability, and biology, to systems theorists interested in dynamic and hierarchical systems, and most recently, again to psychologists to understand the meanings and representations that individuals impose on their behavior as well as their social surround.

What then is the simplest model for understanding longitudinal research? We need a developmental model that takes into account individual changes over time; an ecological model that takes into consideration parents, family, school, peers, and community factors; a regulation model that includes the dynamic interplay of individual and context over time; and a more complex psychological model that includes individual representations of themselves and reality. Currently, the primary aims of longitudinal research, i.e., the identification of forces that promote continuity and discontinuity between earlier and later positive and negative adaptations, are often undermined by theoretical limitations (the inherent indeterminacy of dynamic systems), statistical limitations (the problem of endogeneity: unknown third variables and feedback from the outcome to the predictors), and logistic limitations (the cost of including all relevant personal and ecological variables in the study). Given these constraints, the best strategy for longitudinal researchers is to make active decisions to deal with these limitations, rather than pretend they don’t exist.