Developmental Science in 2025: A Predictive Review

Richard M. Lemer, Jennifer P. Agans, Lisette M. DeSouza, and Rachel M. Hershberg

Yogi Berra, the New York Yankee Baseball Hall-of-Fame catcher, is purported to have observed that, “It’s hard to make predictions, especially about the future.” Nevertheless, based on our understanding of the contemporary features of developmental science, and our understanding of the scientific trajectories that have instantiated these current attributes of the field, we attempt to extrapolate these conceptual growth curves forward and discuss our prospections (predictions) about developmental science in 2025.

Developmental science seeks to describe, explain, and optimize intraindividual (within-person) change and interindividual (between-person) differences in intraindividual change across the life span (Bakes, Reese, & Nesselroade, 1977; Lerner, 2012; Lerner, Lerner, Bowers, & Geldhof, 2015). At this writing, contemporary developmental science is characterized, theoretically, by the centrality of models derived from the relational developmental systems (RDS) metamodel; methodologically, the field involves an embracing of quantitative, qualitative, and mixed-methods research as integral to understanding the meaning and course of human development (e.g., Yoshikawa, Weisner, Kalil, 8c Way, 2008). In turn, new foci of developmental science are emerging as central to the field. These foci are drawn from other disciplines. There is a new understanding of the role of biology in human development, one predicated on integrative understanding of evolution and of epigenetics, of methodological innovations in the study of development that are predicated on the idiographic nature of intraindividual change, and of the use of econometric methods to provide evidence about causal processes in community-based programs aimed at promoting positive human development. In addition, greater attention is being given to the use of RDS-based evidence to enact applications to optimize human development and to promote social justice. In this article we discuss the present status of developmental science (see also Lerner, 2012) and describe in some detail the nature and implications of these new foci.

The Relational Developmental Systems Metamodel

Due to the contributions ofWillis F. Overton (e.g., 2013, 2015; Overton & Mueller, 2013) and others (e.g., Gottlieb, 1997, 1998), the sun has set on split, reductionist accounts stressing nature or nurture. The metamodel described by Overton, termed “relational developmental systems” (RDS), has several implications for the future of the field. For instance, we discuss the links between the RDS metamodel and the emerging attention being paid to evolutionary biology and molecular genetics and, in turn, to applications aimed at promoting social justice.

From the late 1960s through the first half of the third decade of the 21st century, the study of human development evolved from a field dominated by split, reductionist (psychogenic or biogenic), approaches to a multidisciplinary (and, in regard to aspirations of many developmental scientists, an interdisciplinary) scholarly domain that seeks to integrate variables from biological through cultural and historical levels of organization across the life span into a synthetic, coactional system (e.g., Elder, 1998; Ford & Lerner, 1992; Gottlieb, 1997, 1998; Lerner, 2012). Prior reductionist accounts of development that adhered to a Cartesian dualism pulled apart (split) facets of the integrated relational developmental system (Overton, 2015). For instance, reductionist views typically elevated the importance of such split formulations as nature versus nurture, continuity versus discontinuity, stability versus instability, or basic versus applied science (Lerner, 2002, 2006).

These split approaches are rejected by proponents of theories derived from a RDS metamodel (e.g., Mistry & Dutta, 2015; Overton, 2013; Over- ton 8c Mueller, 2013) that, in turn, are derived from a process-relational paradigm (Overton, 2015). Overton explains that, compared to a Cartesian worldview, the process-relational paradigm focuses on process, becoming, holism, relational analysis, and the use of multiple perspectives and multiple explanatory forms. Within the process-relational paradigm, the organism is seen as inherently active, self-creating (autopoietic), self-organizing, selfregulating (agentic), nonlinear/complex, and adaptive (Overton, 2015; see also Sokol, Hammond, Kuebli, & Sweetman, 2015).

Within the RDS metamodel, the integration of different levels of organization frames understanding of life-span human development (Lerner, 2006; Overton, 2013, 2015). Accordingly, the conceptual emphasis in RDS-based theories is placed on mutually influential relations between individuals and contexts, represented as individualOcontext relations. These relations vary across place and across time (Elder, Shanahan, & Jennings, 2015): the “arrow of time,” or temporality, is history, which is the broadest level within the ecology' of human development. History imbues all other levels with change. Such change may be stochastic (e.g., nonnormative life or historical events; Bakes, Lindenberger, & Staudinger, 2006) or systematic, and the potential for systematic change constitutes a potential for (at least relative) plasticity across the life span.

Developmental scientists may focus on either the role of the individual and/or the context in seeking to understand particular instantiations of individualO context exchanges. Overton (2015) explained that, in the process-relational paradigm, this changing focus in developmental analysis involves different moments within a research program. One moment involves the idea of the identity of opposites, a second moment involves the opposites of identity, and a third (relationally integrative) moment involves the synthesis of wholes.

The first moment, the identity of opposites, recognizes that individual and context define, and are mutually constituted by each other. In this moment, or point, in programmatic developmental inquiry, the emphasis is on the fused person<=>context relationship as the primary unit of analysis for understanding development. The second, opposites of identity moment allows one, in effect, to hold the other parts of the integrated system in abeyance and focus on one part of the system. Overton (2013) explained that

Reestablishing a stable base—not an absolute fixity', nor an absolute relativity', but a relative relativity (Latour, 1993)—[enables] . . . the law of contradiction ... [to be] reasserted and categories [to] again exclude each other. As a consequence of this exclusion, parts exhibit unique identities that differentiate each from the other.

(p. 48)

Finally', the third moment, the synthesis of wholes, occurs when the first two moments are embedded in a multiperspective, process-relational paradigm and are recognized as mutually necessary in a systematic, integrative program of research, wherein one needs both of the first two moments.

Theories derived from an RDS metamodel, when framed by the identity' of opposites, focus on the “rules,” the processes, which govern, or regulate, exchanges between (the functioning of) individuals and their contexts. Brandtstadter (1998) termed these relations “developmental regulations” and noted that, when developmental regulations involve mutually beneficial individuals context relations, then these developmental regulations are adaptive. Developmental regulations are the fundamental feature of human life; indeed all life exists through bidirectional exchanges with the physical and/or social context (Darwin, 1859; Tobach & Schneirla, 1968). Among humans, these exchanges involve physiological systems and functions (e.g., respiration, circulation, digestion, reproduction) and behaviors (e.g., social affiliation and cooperation, as might be involved in protection, hunting, and scavenging; Johanson & Edey, 1981), and involve organismic self-regulation (e.g., hypothalamic functioning, circadian rhy'thms) and intentional self-regulation (e.g., goal selection, resource recruitment, and executive functioning; Gestsdottir & Lerner, 2008; McClelland, Geldhof, Cameron, & Wanless, 2015). The developmental course of self-regulation is in effect the developmental course of human agency (Sokol et al., 2015).

In sum, embedded within a process-relational paradigm, models derived from relational developmental systems metatheory emphasize that all levels of organization within the ecology' of human development are systemically integrated across life. As such, any variable from any level is embodied in, fused with, variables from all other levels; the structure and function of one variable is thus governed, or regulated, by the structure and function of other variables and, for the developing person, these developmental regulations mean that individuals context relations are the basic unit of analysis within human development. Moreover, history (temporality) imbues in individualscontext relations the potential for relative plasticity in human development.

In fact, plasticity is always a relative phenomenon within the RDS. Temporally ordered events in the life or lives of an individual or a group, respectively, may constrain change as well as provide affordances for it (Lerner, 1984). A system that promotes change can also function to diminish it, a point made as well by Maruyama (1963), in his discussion of deviation amplification and deviation countering processes within systems and, later, by Aldwin (2007; Aldwin & Gilmer, 2013) in regard to human development systems. In short, however, because of relative plasticity, developmental scientists may be optimistic that instances of individualscontext relations may be found or created to promote more positive human development among all people, and to promote social justice by providing opportunities for all individuals to optimize their chances of positive, healthy development (Lerner & Overton, 2008). Instantiation of such promotion and optimization efforts rests on the conduct of multidisciplinary research, the use of change-sensitive methodologies, and the translation of research into policies or programs.

Contemporary developmental science is marked by such scholarship within and across several substantive and methodological areas framing the field. In addition, we have noted that the field involves burgeoning attention to the interrelated areas of evolutionary biology and of epigenetics, to methodological innovations appropriate to understand the fundamental idio- graphic nature of human development, and the nature of systems change. There is, as well, a growth in attention to the use of econometric methods in testing causal statements about the bases of change. These methods, especially when added to a developmental science “methodological toolbox” that includes qualitative and mixed-methods approaches, have important implications for assessing causality. As we describe in the succeeding sections of this article, the coalescing of these substantive and methodological areas have important, indeed profound, implications for the application of developmental science.

Evolutionary Biology and Epigenetics

According to the concept of embodiment associated with the RDS metamodel, biological, psychological, and behavioral attributes of the person, in fusion with culture, have a temporal (historical) parameter (Overton, 2013, 2015). As such, embodiment—the fusion among all variables and all levels of organization within the relational developmental system—has implications across ontogeny and phylogeny (Ho, 2010; Jablonka & Lamb, 2005). One key implication iiwolves the idea that qualitative changes emerge across the life span through the integration of organism and contextual levels of organization (Lerner, 1984, 2002). A second key implication is the creation of relative plasticity' in phydogeny and ontogeny occurring because of embodied actions resulting in (autopoietic, or self-constructing) change in the developmental system (With- erington, 2014, 2015). Relative plasticity' characterizes the relations between organisms and contexts that, across time, create qualitative change in developmental processes within and across generations (Lerner, 1984). This qualitative discontinuity involves what developmental scientists have termed “epigenetic (emergent) change” (e.g., Gottlieb, 1997, 1998; Werner, 1957) in ontogeny. In turn, the action of genetic <=> context processes, that are instances of embodied change within the developmental system, are the focus of study in the field of epigenetics (e.g., Meaney, 2010; Misteli, 2013; Slavich & Cole, 2013).

It is important to distinguish the differences in denotation for these two uses of the term epigenesis. As we have noted, within the description of developmental change across the life span, the term epigenesis refers to the emergence of qualitatively discontinuous characteristics (e.g., developmental stages) across ontogeny' (see Gottlieb, 1997, 1998; Lerner, 1984; Lerner & Benson, 2013; Werner, 1957). In turn, Misteli (2013), noting that the term epi comes from the Greek and means “over” or “above,” indicated that epigenetic effects are effects that are ones “beyond” the effects of genes. Accordingly, in the literatures of evolutionary biology and of molecular biology', the term epigenetics refers to a process involving gene <=> context relations resulting in the modification of information transmitted by DNA (through messenger RNA, or mRNA) across long, even multigenerational time scales (e.g., Meaney, 2010; Misteli, 2013; Slavich & Cole, 2013).

The two concepts (of epigenetic/emergent change across ontogeny and changes in the information transmitted by DNA through epigenetics) may pertain of course to interrelated phenomena. Emergent change across the life span is explained, within theories associated with the RDS metamodel, by systems changes iiwolving mutually influential relations among levels of organizations, which would include the geneOcontext relations involved in epigenetics (e.g., Lerner & Benson, 2013; Witherington, 2014). As well, contemporary scholarship about the features of epigenetics and evolution reflects the concept of embodied change (of fusion, or integration, of changes at all levels of organization within the developmental system). As noted later, the embodiment of biological change within the RDS means that the impact of an individual’s biology on his or her developmental change can be altered (enhanced) through autopoietically occurring changes or through planned applications of developmental science in the service of promoting individual thriving or social justice.

Bateson and Gluckman (2011) observed that gene expression is fundamentally shaped by variables external to the cell nucleus (where deoxyribonucleic acid, DNA, is located). They stressed therefore that “[a] willingness to move between different levels of analysis has become essential for an understanding of development and evolution” (Bateson & Gluckman, 2011, p. 5). Similarly, Keller (2010) explained that it is erroneous either to conceptualize development as involving separate causal influences or to posit that attributes of the person develop as an outcome of the interaction of causal elements. Indeed, she noted that the concept of interaction is itself flawed, in that its use is predicated on the idea that there exists attributes that are at least conceptually separate. Keller explained that the concept of developmental dynamics precludes such separation. She emphasized that,

From its very beginning, development depends on the complex orchestration of multiple courses of action that involve interactions among many different kinds of elements—including not only pre-existing elements (e.g., molecules) but also new elements (e.g., coding sequences) that are formed out of such interactions, temporal sequences of events, dynamical interactions, etc.

(Keller, 2010, pp. 6-7)

Keller (2010), in discussing the elements of the epigenetic system, reflects the idea of the research moment of the opposites of identity, discussed by Overton (2015); but, as well, by pointing to the presence of dynamic interactions, her ideas reflect also the moment of the identity of opposites.

Moreover, Pigliucci and Muller (2010) noted that genes are not as much generators of evolutionary change as they are followers in the evolutionary process. They explained that “evolution progresses through the capture of emergent interactions into genetic-epigenetic circuits, which are passed to and elaborated on in subsequent generations” (Pigliucci & Muller, 2010, p. 14). Similarly, West-Eberhard (2003) connected evolution and the presence of relative plasticity across development. She explained that environmental variables are a major basis of adaptive evolutionary change. As also pointed out by Pigliucci and Muller (2010), West-Eberhard (2003) noted that genetic mutation does not provide either the origin or the evolution of novel adaptive characteristics because “genes are followers not leaders, in evolution” (p. 20). In addition, she explained that the relative plasticity of the phenotype can facilitate evolution by providing immediate changes in the organism (West-Eberhard, 2003). Similarly, Gissis and Jablonka (2011) noted that plasticity “is ... a large topic, but, just as Lamarck anticipated, an understanding of plasticity is now recognized as being fundamental to an understanding of evolution” (p. xiii).

Crystallizing the embodiment of variables from all levels of organization within the relational developmental system that create epigenetic change across generations, Jablonka and Lamb (2005) presented evidence demonstrating that human evolution involves four interrelated dimensions: genes, epigenetics, behavior, and culture. They explained that contemporary research in molecular biolog)' indicates clearly that current, neo-Darwinian assumptions about the role of genes in evolution are mistaken. This research demonstrates that cells can transmit information to daughter cells through non-DNA, epigenetic means. Therefore, genetic and epigenetic processes constitute two dimensions of evolution. In addition, animals can transmit information across generations though their behavior, which constitutes a third dimension of evolution. A fourth dimension of evolution is constituted by culture, in that humans “inherit” from their parents symbols and, in particular, language. As such, Jablonka and Lamb (2005) concluded that “It is therefore quite wrong to think about heredity and evolution solely in terms of the genetic system. Epigenetic, behavioral, and symbolic inheritance also provide variation on which natural selection can act” (p. 1).

These epigenetic effects referred to by Jablonka and Lamb (2005) occur because chemicals in the cell either allow or do not allow DNA to be transcribed into mRNA (Misteli, 2013). For example, acetyl groups, when linked with one of the four base chemicals comprising DNA, that is, to cytosine, allow DNA transcription; this process is termed acetylation. In turn, when methyl groups are linked to cytosine, then there is no transcription of DNA into mRNA. This process is termed “methylation.” In short, acetylation process allow DNA to be transcribed into mRNA (and to therefore play a role in producing proteins) and methylation processes silence DNA transcription.

If DNA is not transcribed into mRNA, then this DNA cannot play a role in the production of proteins for use by the cell. Because this silencing of gene transcription can persist (can remain stable) across generations (Meaney, 2010; Misteli, 2013; Roth, 2012; Slavich & Cole, 2013), epigenetic influences constitute heritable changes explained by processes other than DNA. Indeed, Gissis and Jablonka (2011), in a book discussing the transformations of Lamarckian theory that have arisen in relation to the increasingly more active focus on epigenetic processes in the study of evolution and development (Meaney, 2010), noted that a form of inheritance of acquired characteristics does exist in the form of epigenetic inheritance systems.

This system of epigenetic effects involves chemicals within the cell, within the internal milieu of the body, and within the external ecology within which the body is embedded (Misteli, 2013; Roth, 2012; Slavich & Cole,

2013) or embodied, in the terms used by Overton (2013a, 2013b, 2015). For instance, Roth (2012) noted that the genome of infants is modified by epigenetic changes involving experiential and environmental variables. She explained that parental stress, infant separation, or caregiver nurturance or maltreatment can alter methylation patterns that affect neurobiolog)' and behavior across the life span. Similarly, Slavich and Cole (2013) discussed evidence that changes in the expression of hundreds of genes occurs as a function of the physical and social environments inhabited by humans, and they noted that “external social conditions, especially our subjective perceptions of these conditions, can influence our most basic internal biological processes—namely, the expression of our genes” (p. 331)—a view that again highlights the implications of embodied biological changes as a focus of actions aimed at enhancing positive human development or social justice.

We return in the concluding section of this article to the implications of embodiment and epigenetics for promoting health and positive human development and, as well, social justice. Here we note, however, that the evidence concerning epigenetics, embodied action, and plasticity' that today is understood as accounting for the features of evolutionary and developmental change necessarily leads to deep skepticism about the “extreme nature” (Rose & Rose, 2000) of the claims of biological reductionists, for example, evolutionary psychology (Rose & Rose, 2000), sociobio log)' (Lerner, 2002; Lerner & von Eye, 1992), and behavior genetics (Molenaar,

2014) . Clearly the claims of such reductionists are inconsistent with the now quite voluminous evidence in support of the role of epigenetics in the multiple, integrated dimensions of human evolution, discussed earlier (Bateson, 2015; Coall, Callan, Dickins, & Chisholm, 2015; Gissis & Jablonka, 2011; Gunnar, Doom, & Esposito, 2015; Jablonka & Lamb, 2005; Lickli- ter & Honeycutt, 2015). Moreover, these claims run counter to research that has importantly begun focusing on the role of the organism’s active agency (McClelland et al., 2015), and of culture (Mistry & Dutta, 2015), in creating change within and across generations.

In contrast to the claims of biological reductionists, a process-relational metamodel and concepts associated with the RDS metamodel (Overton,

2015) suggest that transmission across generations is accounted for by the plastic embodied processes of the individual functioning in a reciprocal, that is, bidirectional (<=>), relation with his/her physical and cultural context. Thus, within the RDS perspective, and in the context of contemporary evolutionary scholarship (e.g., Gissis & Jablonka, 2011; Ho, 2010; Keller, 2010; Lickliter 8c Honeycutt, 2015; Meaney, 2010), the “Just So” stories (Gould, 1981) of evolutionary psychology are conceptually and empirically flawed. Furthermore, embodiment constitutes the basis for epigenesis within the persons life span (Gottlieb, 1997, 1998), including qualitative discontinuity across ontogeny in relations among biological, psychological, behavioral, and social-cultural variables. Evidence for the relative plasticity of human development within the integrated levels of the ecology of human development makes biologically reductionist accounts (or, equally, completely sociogeneic accounts) of parenting, offspring development, or sexuality implausible, at best, and entirely fanciful, at worst (Lerner, 1984, 2002, 2006).

In sum, the RDS metamodel provides an approach to the study of evolutionary and ontogenetic change that capitalizes on the dynamic, mutually influential relations between developing individuals and their complex and changing ecology'. These “strands” of theory merged in the 1970s, 1980s, and 1990s and created a focus on models emphasizing that time and place matter in regard to shaping the course of life (Bronfenbrenner, 2005; Elder, 1998; Elder & Shanahan, 2006; Elder et ah, 2015). A key emphasis in these ideas is that the scientific study of human development needed to involve the individual and the diversity of people to understand human development. The process-relational paradigm that framed conceptions of the bases of human development is associated with the generation of several RDS models ofhuman development (Overton, 2013; Overton & Mueller, 2013), conceptions that were used to guide the study of individuals, contexts, and their dynamic interrelations across the life span.

Person-Centered, Idiographic Methods, and Systems Science Methodology

Developmental science is the study of change, within the individual, within the individualOcontext relation, and within the autopoietic relational developmental system (Bakes et ah, 1977; Witherington, 2014, 2015). Indeed, because of temporality, change is a constant within the RDS. Developmental scientists do not ask, therefore, whether there is change but, rather, if and how one instance of a specific change matters for another specific instance of change (Bornstein, 2006). However, Molenaar (2014) explained that the standard approach to statistical analysis in the social and behavioral sciences is not focused on change but, instead, derived from mathematical assumptions regarding the constancy of phenomena across people and, critically, time. He noted that these assumptions, the ergodic theorems, lead to statistical analyses placing prime interest on the population level. Interindividual variation, and not intraindividual change, is the source of this population information (Molenaar, 2014).

However, within the process-relational paradigm (Overton, 2015) development is nonlinear and characterized by autopoietic (self-constructing) and hence idiographic intraindividual change, features of human functioning that violate the ideas of ergodicity. Accordingly, use of the RDS metamodel as a frame for research requires a rejection of use of data analytic tools predicated on the ergodic theorems that constitute the bases of traditional statistical procedures (Molenaar & Nesselroade, 2014; Nesselroade & Molenaar, 2010).

To explain, consider as a sample case Gaussian (normally distributed) processes. Molenaar (2014) noted that any ergodic Gaussian process has to obey the following two necessary conditions; (1) The Gaussian process has to be stationary (this condition indicates that the mean of the process has to be constant in time, the variance of the process has to be constant in time, and the sequential dependencies characterizing the process only depend upon the relative distance, or lag, between time points) and (2) The Gaussian process also has to be homogeneous across individuals (indicating that each participant in the population or group has to obey the same dynamic model).

Simply, the assumption used when framing statistical analysis through the use of the ergodic theorem is that the structure of interindividual variation of a developmental process at the population level is equivalent to the structure of intraindividual variation at the individual level (Molenaar, 2014; Molenaar & Nesselroade, 2015).

However, developmental processes have time-varying means, variances, and/or time-varying sequential dependencies, and therefore the structure of interindividual variation at the population level is not equivalent to the structure of intraindividual variation at the level of individual (Molenaar, 2014). Developmental processes are therefore nonergodic.

As a consequence, to obtain valid information about developmental processes it is necessary to study intraindividual variation within single individuals, and Molenaar and Nesselroade (2015; Nesselroade & Molenaar, 2010) have developed procedures such as the Idiographic Filter (IF), which involves use of the dynamic factor model at the level of the individual but then generates group-differential or nomothetic latent constructs to enable generalization across participants. Through use of procedures such as the IF, developmental scientists can capture the nonergodic nature of intraindividual change and, as well, produce generalities about groups that apply as well to the individuals within them.

How, then, may research proceed? Consistent with the Bornstein (2006) “specificity principle,” we suggest, therefore, that addressing a multipart “what” question is the key to conducting programmatic research about the function, structure, and content of development across the life span. To test RDS-based ideas about the ontogenetically changing structure of development across the life span—to test empirically the process-relational conception of intraindividual change (Overton, 2015; Sokol, Hammond, & Berkowitz, 2010; Sokol et al., 2015)—the task for developmental researchers is to undertake programs of research to ascertain answers to the following multipart “what” question:

  • 1. What structure—content relations emerge; that are linked to
  • 2. What antecedent and consequent adaptive developmental regulations (to what trajectory of individualOcontext relations); at
  • 3. What points in development; for
  • 4. What individuals; living in
  • 5. What contexts; across
  • 6. What historical periods?

The integration of the dynamic factor model (DFM) and the IF provides one promising way to ascertain what individuals have in common first and then build generalizations on that information. This approach stands in marked contrast to initially aggregating the individual-level information and extracting generality' from it in the form of average tendencies—the approach of traditional differential psy'chology. The integration of the DFM and the IF replaces static trait conceptions with an approach that embraces development and complexity.

The work of Molenaar and Nesselroade is an example of the application of systems science methods to developmental science framed by relational developmental systems theories (see also Molenaar, Lerner, & Newell, 2014). For instance, dynamic factor analysis is an example of a state space model, in that it integrates a model of the dynamic evolution of the state process and another model linking the state process at each time point to the observed process at that time.

Systems Science

methods are designed to address complexity, that is, change .... nonlinear relationships, bidirectional relationships (feedback loops), time-delayed effects, and emergent properties of the system—phenomena that are observed at the system level but cannot be linked to a specific individual component of the system.

(Mabry & Kaplan 2013, p. 9S)

Examples of systems science methods are computational/mathematical modeling and simulation, microsimulation, agent-based modeling, system dynamics modeling, network analysis, and discrete event simulation (Urban, Osgood, & Mabry, 2011).

The use of systems science methods in developmental science is a sample case of the opening of the field to innovations in methodology, perhaps especially those associated with other disciplines. Scholars have recognized that new methodological tools are required for understanding the change processes involved in an epigenetic, agentic, and autopoietic system. New tools are required to appraise the qualitative changes marking this system and to model/ test the revised understanding of causality within such a system. Accordingly, qualitative research and mixed-methods research are important and increasingly more prominent cases in point (Burton, Garrett-Peters, & Eaton, 2009; Tolan, Boker, & Deutsch, 2015; Yoshikawa et al., 2008). In addition, there is burgeoning use of data analytic methods derived from the work of econometricians.

Econometric Methods and the “Downfall of the Gold Standard”

The sine qua non of developmental analyses is the study of intraindividual change. As such, longitudinal designs continue to be the key approach to the study of such change (Molenaar & Nesselroade, 2015; von Eye, Bergman, & Hsieh, 2015). However, the problem of selection—ofwhat economists term “endogeneity” (e.g., Heckman, Ichimura, & Todd, 1997, 1998)—besets longitudinal studies, given that, even if representative samples are present at the beginning of a longitudinal study, selective attrition will increasingly bias the sample. People that stay in a study, perhaps especially a long-term one, have been found to have “something about them” (something endogenous to them) that differs from participants that drop out of a study (e.g., see Siegler & Botwinick, 1979). Are changes seen in the remaining participants due, therefore, to something about the nature of developmental process otto what may have been a preexisting endogenous factor (e.g., the tenacity' needed to stay at a task, obedience to authority, or trust in institutions)? The problem of endogeneity is particularly problematic when longitudinal studies are used to assess whether particular experiences of one group (e.g., participation in a community-based, youth development program) are associated with developmental changes that differ from those seen within members of a group not participating in the experience (program). Here, the researcher may not be able to infer that the program was causally associated with any differences between participating groups because it may be that there were preexisting, endogenous factors that led some individuals to participate in (self-select into) the experience.

As a consequence, because of the problem of endogeneity', randomized control trials (RCTs) have been regarded as the “gold standard” design to test for causality (McCall & Green, 2004). As such, many potential funders of developmental science research have eschewed longitudinal studies because of the inability to demonstrate causality due to selection effects. However, the landscape of research aimed at causal analysis has changed. Econometric methods are being used in developmental science research to address endogeneity' in longitudinal research. Among the important tools provided by econometricians are propensity' score analyses, instrumental variable (IV) analyses, and regression discontinuity designs.

A propensity score is the probability' of a unit (e.g., person, classroom, school) being assigned to a particular treatment given a set of observed covariates. Propensity scores are used to reduce selection bias by' equating groups based on these covariates (e.g., Heckman et al., 1997, 1998; Rosenbaum & Rubin, 1983). An instrument is a variable that does not itself belong in the explanatory equation but is correlated with the endogenous explanatory variables. In attempting to estimate the causal effect of some variable x on another y, an instrument is a third variable z which affects у only through its effect on x (Angrist, Imbens, & Rubin, 1996; Martens, Pestman, de Boer, Belitser, & Klungel, 2006; Staiger 8c Stock, 1997). In regression discontinuity designs, a pretest cutoff score is used to assign participants to either the program or comparison group. The assumption is that in the absence of the program the pre-post relationship would be equivalent for the two groups (Trochim, 2006).

The presence of these econometric tools indicates that randomized control trials are not the only means through which to assess causality in studies of the effects of programs on participants. When part of the methodological tools of developmental scientists conducting longitudinal research (and, of course, there are other methods in this toolbox, e.g., multiple imputation methods; Little, 1995), econometric methods provide researchers with a rich set of resources to use in the study of ontogenetic change. In addition, as we argue later, RCTs are not the “gold standard” for identifying causality. Indeed, as implemented from the latter decades of the 20th century through the first decades of the 21st century, many RCTS were “fools’ gold.” In the developmental science of the 1950s through the 1970s (e.g., Reese & Lipsitt, 1970), experimentation in the study of human development involved focus on internally valid designs. In such designs, researchers seek to eliminate threats to their being able to attribute the variance in the dependent variable to the manipulated variance in the independent variable. Such threats are ruled out through controls. However, many of the experiments actually conducted during the decades wherein experimental child psychology was a predominant approach taken by developmental researchers (i.e., from the 1940s into the early 1970s; see White, 1970) did not have adequate controls to rule out threats to internal validity. That is, the three control groups in the Solomon and Lessac (1968) formulation were rarely used in studies of human development.

Solomon and Lessac (1968) explained that the typical experiment included only one control group (pretest, no manipulation, posttest), a group included to account for the predicted differential variance associated with experiencing the manipulation as compared to only the pre- and the posttests. However, this design did not control for the variance that may be associated with the reactive effects of the pretest (and thus a second control group, involving no pretest but a manipulation and a posttest, was needed) or for the variance that may be associated with maturation/development (and thus a third control group, involving neither a pretest nor a manipulation, but only a posttest, was needed). Despite the absence of all three control groups, and thus the presence of threats to internal validity, a focus on experimental designs continued to be a method of choice of many researchers (Reese & Lipsett, 1970).

Much research continues to use the two-group design, with only the first of the three control groups noted by Solomon and Lessac (1968) used. Oddly, this approach is used in RCT research with samples across the life span and is mistakenly called the “gold standard” of experimental designs, despite the lack of appropriate controls. Nevertheless, even in the 1970s developmental scientists were moving away from a focus on issues of internal validity.

Hultsch and Hickey (1978) pointed out that issues of external validity were important if one took theoretical positions that were attentive to the contextual conditions associated with time and place (Elder, 1998; Elder et al., 2015). By external validity, Hultsch and Hickey meant features of experimental design that would allow generalization to other samples, to studies that employed similar constructs but different measures, or findings that might be generalized to different historical periods and places.

Although such research is important to conduct if one is attentive to many of the ideas derived from relational developmental systems metamodel, Freund and Isaacowitz (2013) noted that a third type of validity must be attended to in order to fully embrace the implications of the process-relational paradigm and the relational developmental systems metatheory. Inspired by the ideas of Brunswick (1955), and legitimated as a focus of such scholarship by the moment of the opposites of identity discussed by Overton (2013), Freund and Isaacowitz noted that an important tool of developmental scientists is ecologically valid experiments. They contend that such experiments should be used as a method when a researcher wants to elucidate contextual sources of variance in the individual—context relation that reflect the lived lives of people in particular places, developing within particular historical periods. Indeed, when such research is conducted, especially when it is conducted with sensitivity to the other types of validity, it can be an important asset in the methodological armamentarium of developmental scientists conducting research predicated on RDS-based theoretical models.

Applications of RDS-Based Research: The Promotion of Social Justice

Among the many split conceptions maintained by viewing the study of development through a Cartesian lens (Overton, 2015) was the split between basic and applied research. However, within models of human development derived from the ideas of the process-relational paradigm, this split joins other ones (e.g., nature—nurture or continuity—discontinuity) in being rejected. When one studies the embodied individual within the developmental system, then explanations of how changes in the individuals context relation (at Time 1) may eventuate in subsequent changes in this relation (at Time 2, Time 3, etc.) are tested by altering the Time 1 persons context relation. When such alterations are conducted in the ecologically valid setting of the individual, these assessments constitute tests of the basic, relational process of human development and, at the same time, applications— interventions—into the course of human development (Lerner, 2002). Indeed, depending on the level of analysis, aggregation, and time scale at which these interventions are implemented, such changes in the ecology of the individual^context relation may involve relationships between individuals (e.g., mentoring relationships), community-based programs, or social policies (e.g., Bronfenbrenner, 2005).

As we have explained, the rationale for applying developmental science to enhance the lives of individuals or groups is predicated on the presence of relative plasticity in human development, a concept that is derived from RDS-based ideas, such as directionally influential individual^context relations and embodiment. The relative plasticity of human development is a fundamental strength in, and the basis of optimism about, human development. Developmental scientists can be hopeful that there are combinations of person and context that can be identified or created (through programs or policies) to enhance the lives of all individuals and groups. In other words, developmental scientists may act to change the course of developmental regulations, of individualOcontext relations, in manners aimed at optimizing the opportunities for individual and group trajectories across life to reflect health and thriving.

As such, developmental scientists have in the repertoire of models and methods in their intellectual “toolbox" the means to work to promote a better life for all people, to give diverse individuals the requisite chances needed to maximize their aspirations and actions aimed at being active producers of their positive development, and to promote a more socially just world (Lerner, 2002, 2004; Lerner & Overton, 2008). In this regard, Lerner and Overton (2008) noted that theoretically predicated changes in the RDS need to be evaluated in regard to how more positive development may be promoted among individuals whose ecological characteristics (e.g., socioeconomic circumstances or educational opportunities) lower the probability of such development. To contribute significantly to creating a developmental science aimed at promoting social justice, scholars need to identify the means to change individualOcontext relations in manners that enhance the probability that all individuals, no matter their individual characteristics or contextual circumstances, have greater opportunity' to experience positive development (e.g., see Fisher, Busch, Brown, & Jopp, 2013).

Indeed, Fisher and Lerner (2013) noted that social justice focuses on the rights of all groups in a society' to have fair access to and a voice in policies governing the distribution of resources essential to their physical and psychological well-being. Social justice focuses also on social inequities, characterized as avoidable and unjust social structures and policies that limit access to resources based solely on group or individual characteristics such as race/ ethnicity, age, gender, sexual orientation, physical or developmental ability status, and/or immigration status, among others.

Developmental science framed by the process-relational paradigm has a clear agenda involving such scholarship. For instance, Fisher et al. (2013) provided a vision for social justice-relevant research in developmental science. Some of the research foci they discuss include, addressing the pervasive systemic disparities in opportunities for development; investigating the origins, structures, and consequences of social inequities in human development; identifying societal barriers to health and well-being; identifying barriers to fair allocation and access to resources essential to positive development; identifying how racist and other prejudicial ideologies and behaviors develop in majority groups; studying how racism, heterosexism, classism, and other forms of chronic and acute systemic inequities and political marginalization may have a “weathering” effect on physical and mental health across the life span; enacting evidence-based prevention and policy research aimed at demonstrating if systemic oppression can be diminished and psychological and political liberation can be promoted; taking a systems-level approach to reducing unjust institutional practices and to promoting individual and collective political empowerment within organizations, communities, and local and national governments; evaluating programs and policies that alleviate developmental harms caused by structural injustices; and, creating and evaluating empirically based interventions that promote a just society that nurtures lifelong healthy development in all of its members.

Conclusions: Beyond 2025

The epigenetic and embodied developmental changes that characterize individual^context relations within the autopoietic relational developmental system, and that provide a rationale for and optimism about applying developmental science in the service of promoting thriving and social justice for all people, requires “a theoretical framework more akin to current dynamic systems models than to traditional conceptions of either behavioral development or evolution” (Harper, 2005, p. 352). Overton (2013, 2015) provided this theoretical framework. Derived from a process-relational paradigm, the RDS metamodel that he has forwarded has explained why

the Cartesian-split-mechanistic scientific paradigm that until recently functioned as the standard conceptual framework for subfields of developmental science (including inheritance, evolution, and organismic— prenatal, cognitive, emotional, motivational, sociocultural—development) has been progressively failing as a scientific research program.

(Overton, 2013a, p. 22)

He noted:

An alternative scientific paradigm composed of nested metatheories with relationism at the broadest level and relational developmental systems as a midrange metatheory is offered as a more progressive conceptual framework for developmental science. Termed broadly the relational developmental systems paradigm, this framework accounts for the findings that are anomalies for the old paradigm; accounts for the emergence of new findings; and points the way to future scientific productivity.

(Overton, 2013a, p. 22)

And so where do we go from here? What is the future trajectory of developmental sciences after 2025?

The theoretical orientations and interests of contemporary cohorts of developmental scientists, the aspiration to produce scholarship that matters in the real world, and the needs for evidence-based means to address the challenges of the 21st century have coalesced to make Kurt Lewin’s (1952, p. 169) quote, that “There is nothing so practical as a good theory,” an oft-proven empirical reality. The scientific and societal value on which the developmental science of the post-2025 era will be judged is whether its theoretical and methodological tools accurately reflect the diversity and dynamism of human development and are centered on promoting thriving across the life span. Therefore, promoting social justice is, and will be, the most significant lens through which the contributions of developmental science will be viewed.

Acknowledgment

This article is based on a keynote address delivered by Richard M. Lerner at the 8th biennial meeting of the Society for the Study of Human Development, Fort Lauderdale, Florida, November 3, 2013.

Funding

The preparation of this article was supported in part by grants from the John Templeton Foundation.

References

Aldwin, С. M. (2007). Stress, coping, and development: An integrative approach (2nd ed.). New York, NY: Guilford.

Aldwin, С. M., & Gilmer, D. F. (2013). Health, illness, and optimal aging: Biological and psychosocial perspectives (2nd ed.). New York, NY: Springer.

Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444-455.

Bakes, P. B., Lindenberger, U., & Staudinger, U. M. (2006). Life span theory in developmental psychology. In R. M. Lerner (Ed.), Theoretical models of human development. Vol. 1. Handbook of child psychology (6th ed., pp. 569-664). Hoboken, NJ: Wiley.

Bakes, P. B., Reese, H. W., & Nesselroade, J. R. (1977). Life-span developmental psychology: Introduction to research methods. Monterey, CA: Brooks/Cole.

Bateson, p. (2015). Ethology and human development. In W. F. Overton & P. C. Mole- naar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 208-243). Hoboken, NJ: Wiley.

Bateson, P., & Gluckman, p. (2011). Plasticity, development and evolution. Cambridge, England: Cambridge University' Press.

Bornstein, M. H. (2006). Parenting science and practice. In K. A. Renninger & I. E. Sigel (Vol. Eds.), Handbook of child psychology, Vol. 4: Child psychology in practice (6th ed., pp. 893-949). Hoboken, NJ: Wiley.

Brandtstadter, ). (1998). Action perspectives on human development. In R. M. Lerner (Ed.), Theoretical models of human development. Vol. I. The handbook of child psychology (5th ed.. pp. 807-863). New York. NY: Wiley.

Bronfenbrenner, U. (2005). Making human beings human: Bioecologicalperspectives on human development. Thousand Oaks, CA: Sage.

Brunswick, E. (1955). Representative design and probabilistic theory in a functional psychology. Psychological Review, 62, 193-121.

Burton, L. M., Garrett-Peters, R., & Eaton, S. C. (2009). “More than good quotations”: How ethnography informs knowledge on adolescent development and context. In R. M. Lerner & L. Steinberg (Eds.), Handbook of adolescent psychology, Vol. I. Individual bases of adolescent development (3rd ed., pp. 55—91). Hoboken, NJ: Wiley.

Coall, D. A.. Callan, A., Dickins, T. E., & Chisholm, J. S. (2015). Evolution and prenatal development. In M. E. Lamb (Ed.), Social, emotional, and personality development, Vol. 3 Handbook of child psychology and developmental science (7th ed., pp. 57-105). Hoboken, NJ: Wiley.'

Darwin, C. (1859). The origin of species by means of natural selection or the preservation of favoured races in the struggle for life. London, England: J. Murray.

Elder, G. H., Jr. (1998). The life course and human development. In R. M. Lerner (Vol. Ed.) & W. Damon (Ed.), Handbook of child psychology: Vol. 1. Theoretical models of human development (5th ed., pp. 939-991). New York, NY: John Wiley.

Elder, G. H., Jr., & Shanahan, M. J. (2006). The life course and human development. In R. M. Lerner (Ed.), Theoretical models of human development. Vol. 1. The handbook of child psychology (6th ed., pp. 665-715). Hoboken, NJ: Wiley.

Elder, G. H..Jr., Shanahan, M. J., & Jennings, |, 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 ed., pp. 6-54). Hoboken, NJ: Wiley.

Fisher, С. B., Busch, N. A., Brown, ). L., & Jopp, D. S. (2013). Applied developmental science: Contributions and challenges for the 21st century. In R. M. Lerner, M. A. Easterbrooks, & J. Mistry (Eds.), Handbook of psychology. Vol. 6. Developmental psychology (2nd ed., pp. 516-546). Hoboken, NJ: John Wiley & Sons.

Fisher, С. B., tk Lerner, R. M. (2013). Promoting positive development through social justice: An introduction to a new ongoing section of Applied Developmental Science. Applied Developmental Science, 17(2), 57-59.

Ford, D. H., Sc Lerner, R. M. (1992). Developmental systems theory: An integrative approach. Newbury Park, CA: Sage.

Freund, A. M., Sc Isaacowitz, D. M. (2013). Beyond age comparisons: A plea for the use of a modified Brunswikian approach to experimental designs in the study of adult development and aging. Human Development, 56, 351-371. doi:l 0.1159/000357177

Gestsdottir, G., & Lerner, R. M. (2008). Positive development in adolescence: The development and role of intentional self regulation. Human Development, 51, 202-224.

Gissis, S. B., & Jablonka, E. (Eds.). (2011). Transformations of Lamarckism: From subtle fluids to molecular biology. Cambridge, MA: MIT Press.

Gottlieb, G. (1997). Synthesizing nature-nurture: Prenatal roots of instinctive behavior. Mah- wah, NJ: Lawrence Erlbaunt Associates.

Gottlieb, G. (1998). Normally occurring environmental and behavioral influences on gene activity: From central dogma to probabilistic epigenesis. Psychological Review, 105, 792-802.

Gould, S. J. (1981). The mismeasure of man. New York, NY: Norton.

Gunnar, M. R., Doom, J. R., Sc Esposito, E. A. (2015). Psychoneuroendocrinology of stress. In M. E. Lamb (Ed.), Social, emotional, and personality development, Vol. 3 Handbook of child psychology and developmental science (7th ed., pp. 106-151). Hoboken, NJ: Wiley.

Harper, L. V. (2005). Epigenetic inheritance and the intergenerational transfer of experience. Psychological Bulletin, 131, 340-360.

Heckman, J. J., Ichimura, H., Sc Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.

Heckman, J. J., Ichimura, H., Sc Todd, P. E. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261-294.

Ho, M. W. (2010). Development and evolution revisited. In К. E. Hood, С. T. Halpern, G. Greenberg, & R. M. Lerner (Eds.), Handbook of developmental systems, behavior and genetics (pp. 61-109). Malden. MA: Wiley Blackwell.

Hultsch, D. F., tk Hickey, T. (1978). External validity in the study of human development: Theoretical and methodological issues. Human Development, 21, 76-91.

Jablonka, E., & Lamb, M. J. (2005). Evolution in four dimensions: Genetic, epigenetic, behavioral, and symbolic variation in the history of life. Cambridge, MA: MIT Press.

Johanson, D. C., tk Edey, M. A. (1981). Dicy: The beginnings of humankind. New York, NY: Simon & Schuster.

Keller, E. F. (2010). The mirage of a space between nature and nurture. Durham, NC: Duke University Press.

Latour, B. (1993). We have never been modern. Cambridge, MA: Harvard University Press.

Lerner, R. M. (1984). On the nature of human plasticity. New York, NY: Cambridge University Press.

Lerner, R. M. (2002). Concepts and theories of human development (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

Lerner, R. M. (2004). Liberty: Thriving and civic engagement among America’s youth. Thousand Oaks, CA: Sage.

Lerner, R. M. (2006). Developmental science, developmental systems, and contemporary theories of human development. In R. M. Lerner (Ed.), Theoretical models of human development. Vol. 1. The handbook of child psychology (6th ed., pp. 1-17). Hoboken, NJ: Wiley.

Lerner, R. M. (2012). Essay review: Developmental science: Past, present, and future. International Journal of Developmental Science, 6(1/2), 29-36.

Lerner, R. M., & Benson, J. B. (2013). Introduction. Embodiment and epigenesis. A view of the issues. In R. M. Lerner & J. B. Benson (Eds.), Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system. Vol. 1. Philosophical, theoretical, and biological dimensions. Advances in Child Development and Behavior, 44, 1-19.

Lerner, R. M., Lerner, J. V, Bowers, E. R, & Geldhof, G. J. (2015). Positive youth development and relational developmental systems. In W. F. Overton tk P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed.. pp. 607-651). Hoboken, NJ: Wiley.

Lerner, R. M., tk Overton, W. F. (2008). Exemplifying the integrations of the relational developmental system: Synthesizing theory, research, and application to promote positive development and social justice. Journal of Adolescent Research, 23(3), 245-255.

Lerner, R. M., tk von Eye, A. (1992). Sociobiolog)' and human development: Arguments and evidence. Human Development, 35, 12-33.

Lewin, K. (1952). Field theory in social science: Selected theoretical papers. London, England: Tavistock.

Lickliter, R., & Honeycutt, H. (2015). Biolog)' development, and human systems. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 162-207). Hoboken, NJ: Wiley.

Little, R. ). A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association, 90, 1112-1121.

Mabry, P. L., tk Kaplan, R. M. (2013). Systems science: A good investment for the public’s health. Health Education & Behavior, 40(1, Suppl.), 9S-12S.

Martens, E. E, Pestman, W. R., de Boer, A., Belitser, S. V, & Klungel, О. H. (2006). Instrumental variables: Application and limitations. Epidemiology, 17(3), 260—267.

Maruyama, M. (1963). The second cybernetics: Deviation-amplifying mutual causal processes. American Scientist, 5(2), 164—179.

McCall, R. B., & Green, B. L. (2004). Beyond the methodological gold standard of behavioral research: Considerations for practice and policy. SRCD Social Policy Reports, 18, 1-19.

McClelland, M. M„ Geldhof, G. J„ Cameron, C. E„ & Wanless, S. B. (2015). Development and self-regulation. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. I. Handbook of child psychology and developmental science (7th ed., pp. 523— 565). Hoboken, NJ: Wiley.

Meaney, M. (2010). Epigenetics and the biological definition of gene x environment interactions. Child Development, 81(1), 41—79.

Misteli, T. (2013). The cell biology of genomes: Bringing the double helix to life. Cell, 152, 1209-1212.

Mistry, J.. & Dutta, R. (2015). Human development and culture. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 369-406). Hoboken, NJ: Wiley.

Molenaar, P. C. (2014). Dynamic models of biological pattern formation have surprising implications for understanding the epigenetics of development. Research in Human Development, 11(1), 50-62.

Molenaar, P. С. M., Lerner, R. M., & Newell, K. (Eds.). (2014). Handbook of developmental systems theory and methodology. New York, NY: Guilford.

Molenaar, P. С. M., & Nesselroade, J. R. (2014). New trends in the inductive use of relational developmental systems theory: Ergodicity, nonstationarity, and heterogeneity. In P. С. M. Molenaar, R. M. Lerner, & K. Newell (Eds.), Handbook of developmental systems theory and methodology (pp. 442-464). New York, NY: Guilford.

Molenaar, P. С. M., & Nesselroade, J. R. (2015). Systems methods for developmental research. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 652-682). Hoboken, NJ: Wiley.

Nesselroade, ). R., & Molenaar, P. С. M. (2010). Emphasizing intraindividual variability in the study of development over the life span. In W. F. Overton (Ed.), The handbook of life-span development. Vol. 1. Cognition, biology, methods (pp. 30-54). Hoboken, NJ: Wiley.

Overton, W. F. (2013a). Relationism and relational developmental systems; A paradigm for developmental science in the post-Cartesian era. In R. M. Lerner & J. B. Benson (Eds.), Advances in child development and behavior, Vol. 44. Embodiment and epigenesis: Theoretical and methodological issues in understanding the role of biology within the relational developmental system, Part A: Philosophical, theoretical, and biological dimensions (pp. 21-64). Waltham, MA: Elsevier.

Overton, W. F. (2013b). A new paradigm for developmental science: Relationism and relational-developmental systems. Applied Developmental Science, 17(2), 94-107.

Overton, W. F. (2015). Process and relational developmental systems. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 9-62). Hoboken, NJ: Wiley.

Overton. W. E, & Mueller, U. (2013). Metatheories, theories, and concepts in the study of development. In R. M. Lerner, M A. Easterbrooks, & J. Mistry (Eds.), Handbook of psychology. Vol. 6. Developmental psychology (2nd ed., pp. 19-58). Hoboken, NJ: Wiley.

Pigliucci, M., & Muller, G. B. (2010). Elements of an extended evolutionary synthesis. In M. Pigliucci & G. B. Mueller (Eds.), Evolution—The extended synthesis (pp. 3-17). Cambridge, MA: MIT Press.

Reese, H. W., & Lipsitt, L. P. (Eds.). (1970). Experimental child psychology. New York, NY: Academic Press.

Rose, H., & Rose, S. (2000). Introduction. In. H. Rose & S. Rose (Eds.), Alas poor Darwin: Arguments against evolutionary psychology (pp. 1-13). London, England: Vintage.

Rosenbaum, R, & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Roth, T. L. (2012). Epigenetics of neurobiology and behavior during development and adulthood. Developmental Psychobiology, 54, 590—597.

Siegler, I. C., & Botwinick, J. (1979). A long-term longitudinal study of intellectual ability of older adults: The matter of selective subject attrition. Journal of Gerontology, 54(2), 242-245.

Slavich, G. M., Sc Cole, S. W. (2013). The emerging field of human social genomics. Clinical Psychological Science, 1, 331-348.

Sokol, B. W., Hammond, S. I., & Berkowitz, M. W. (2010). The developmental contours of character. In T. Lovat, R. Toomey, & N. Clement (Eds.), International research handbook on values education and student wellbeing (pp. 579-603). New York, NY: Springer.

Sokol, B. W., Hammond, S., Kuebli, J., & Sweetman, L. (2015). The development of agency. In W. F. Overton & P. C. Molenaar (Eds.), Theory and method. Vol. I. Handbook of child psychology and developmental science (7th ed., pp. 284—322). Hoboken, N|: Wiley.

Solomon, R. L., Sc Lessac, M. S. (1968). A control group design for experimental studies of developmental processes. Psychological Bulletin, 70, 145-150.

Staiger D., Sc Stock J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65, 557-586.

Tobach, E., & Schneirla, T. C. (1968). The biopsycholog)' of social behavior of animals. In R. E. Cooke Sc S. Levin (Eds.), Biologic basis of pediatric practice (pp. 68-82). New York, NY: McGraw-Hill.

Tolan, P. H., Boker, S. M., Sc Deutsch, N. L. (2015). Mixed methods in developmental science. In W. E Overton Sc R C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 715-757). Hoboken, NJ: Wiley.

Trochim, W. M. K. (2006). The regression-discontinuity design. Retrieved from http:// www.socialresearchmethods.net/kb/quasird.php.

Urban, J. B., Osgood, N., Sc Mabry, p. (2011). Developmental systems science: Exploring the application of non-linear methods to developmental science questions. Research in Human Development, 8, 1-25. doi:10.1080/15427609.2011.549686

von Eye, A., Bergman, L. R., Sc Hsieh, C. (2015). Person-oriented methodological approaches. In W. F. Overton Sc P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 789-841). Hoboken, NJ: Wiley.'

Werner, H. (1957). The concept of development from a comparative and organismic point of view. In D. B. Harris (Ed.), The concept of development (pp. 125-148). Minneapolis, MN: University of Minnesota Press.

West-Eberhard, M. ). (2003). Developmental plasticity and evolution. New York, NY: Oxford University Press.

White, S. H. (1970). The learning theory tradition and child psychology. In P. H. Mus- sen (Ed.), Carmichael’s manual of child psychology (3rd ed., pp. 657-702). New York, NY: Wiley.

Witherington, D. C. (2014). Self-organization and explanatory pluralism: Avoiding the snares of reductionism in developmental science. Research in Human Development, 11(1), 22-36.

Witherington, D. C. (2015). Dynamic systems in developmental science. In W. F. Over- ton & P. C. Molenaar (Eds.), Theory and method. Vol. 1. Handbook of child psychology and developmental science (7th ed., pp. 63-112). Hoboken, NJ: Wiley.

Yoshikawa, H.. Weisner, T. M., Kalil, A., & Way, N. (2008). Mixing qualitative and quantitative research in developmental science: Uses and methodological choices. Developmental Psychology, 44(2), 344-354.

 
Source
< Prev   CONTENTS   Source   Next >