Gizem Hülür and Elisa Weber
Lifespan development is embedded in multiple social systems and social relationships. Lifespan developmental and relationship researchers study individual codevelopment in various dyadic social relationships, such as dyads of parents and children or romantic partners. Dyadic data refers to types of data for which observations from both members of a dyad are available. The analysis of dyadic data requires the use of appropriate data-analytic methods that account for such interdependencies. The standard actor-partner interdependence model, the dyadic growth curve model, and the dyadic dual change score model can be used to analyze data from dyads. These models allow examination of questions related to dyadic associations such as whether individual differences in an outcome can be predicted by one’s own (actor effects) and the other dyad member’s (partner effects) level in another variable, correlated change between dyad members, and cross-lagged dyadic associations, that is, whether one dyad member’s change can be predicted by the previous levels of the other dyad member. The choice of a specific model should be guided by theoretical and conceptual considerations as well as by features of the data, such as the type of dyad, the number and spacing of observations, or distributional properties of variables.
Lizbeth Benson and Nilam Ram
In ecological sciences, biodiversity is the dispersion of organisms across species and is used to describe the complexity of systems where species interact with each other and the environment. Some argue that biodiversity is important to cultivate and maintain because higher levels are indicative of health and resilience of the ecosystem. Because each species performs functional roles, more diverse ecosystems have greater capability to respond, maintain function, resist damage, and recover quickly from perturbations or disruptions. In the behavioral sciences, diversity-type constructs and metrics are being defined and operationalized across a variety of functional domains (socioemotional, self, cognitive, activities and environment, stress, and biological). Emodiversity, for instance, is the dispersion of an individual’s emotion experiences across emotion types (e.g., happy, anger, sad). Although not always explicitly labeled as such, many core propositions in lifespan developmental theory—such as differentiation, dedifferentiation, and integration—imply intraindividual change in diversity and/or interindividual differences in diversity. For example, socioemotional theories of aging suggest that as individuals get older, they increasingly self-select into more positive valence and low arousal emotion inducing experiences, which might suggest that diversity in positive and low arousal emotion experiences increases with age. When conceptualizing and studying diversity, important considerations include that diversity (a) provides a holistic representation of human systems, (b) differs in direction, interpretation, and linkages to other constructs such as health (c) exists at multiple scales, (d) is context-specific, and (e) is flexible to many study designs and data types. Additionally, there are also a variety of methodological considerations in study of diversity-type constructs including nuances pertaining theory-driven or data-driven approaches to choosing a metric. The relevance of diversity to a broad range of phenomena and the utility of biodiversity metrics for quantifying dispersion across categories in multivariate and/or repeated measures data suggests further use of biodiversity conceptualizations and methods in studies of lifespan development.
Thomas M. Hess, Erica L. O'Brien, and Claire M. Growney
Blood pressure is a frequently used measure in studies of adult development and aging, serving as a biomarker for health, physiological reactivity, and task engagement. Importantly, it has helped elucidate the influence of cardiovascular health on behavioral aspects of the aging process, with research demonstrating the negative effect of chronic high blood pressure on various aspects of cognitive functioning in later life. An important implication of such research is that much of what is considered part and parcel of getting older may actually be reflective of changes in health as opposed to normative aging processes. Research has also demonstrated that situational spikes in blood pressure to emotional stressors (i.e., reactivity) also have implications for health in later life. Although research is still somewhat limited, individual differences in personal traits and living circumstances have been found to moderate the strength of reactive responses, providing promise for the identification of factors that might ameliorate the effects of age-related changes in physiology that lead to normative increases in reactivity. Finally, blood pressure has also been successfully used to assess engagement levels. In this context, recent work on aging has focused on the utility of blood pressure as a reliable indicator of both (a) the costs associated with cognitive engagement and (b) the extent to which variation in these costs might predict both between-individual and age-related normative variation in participation in cognitively demanding—but potentially beneficial—activities. This chapter elaborates on these three approaches and summarizes major research findings along with methodological and interpretational issues.
Shevaun D. Neupert and Jennifer A. Bellingtier
Daily diary designs allow researchers to examine processes that change together on a daily basis, often in a naturalistic setting. By studying within-person covariation between daily processes, one can more precisely establish the short-term effects and temporal ordering of concrete daily experiences. Additionally, the daily diary design reduces retrospective recall bias because participants are asked to recall events that occurred over the previous 24-hour period as opposed to a week or even a year. Therefore, a more accurate picture of individuals’ daily lives can be captured with this design. When conclusions are drawn between people about the relationship between the predictors and outcomes, the covariation that occurs within people through time is lost. In a within-person design, conclusions can be made about the simultaneous effects of within-person covariation as well as between-person differences. This is especially important when many interindividual differences (e.g., traits) may exist in within-person relationships (e.g., states).
Daily diary research can take many forms. Diary research can be conducted with printed paper questionnaires, divided into daily booklets where participants mail back each daily booklet at the end of the day or entire study period. Previous studies have called participants on the telephone to respond to interview questions each day for a series of consecutive days, allowing for quantitative as well as qualitative data collection. Online surveys that can be completed on a computer or mobile device allow the researcher to know the specific day and time that the survey was completed while minimizing direct involvement with the collection of each daily survey. There are many opportunities for lifespan developmental researchers to adopt daily diary designs across a variety of implementation platforms to address questions of important daily processes. The benefits and drawbacks of each method along with suggestions for future work are discussed, noting issues of particular importance for aging and lifespan development.
Jeremy B. Yorgason, Melanie S. Hill, and Mallory Millett
The study of development across the lifespan has traditionally focused on the individual. However, dyadic designs within lifespan developmental methodology allow researchers to better understand individuals in a larger context that includes various familial relationships (husbands and wives, parents and children, and caregivers and patients). Dyadic designs involve data that are not independent, and thus outcome measures from dyad members need to be modeled as correlated. Typically, non-independent outcomes are appropriately modeled using multilevel or structural equation modeling approaches. Many dyadic researchers use the actor-partner interdependence model as a basic analysis framework, while new and exciting approaches are coming forth in the literature. Dyadic designs can be extended and applied in various ways, including with intensive longitudinal data (e.g., daily diaries), grid sequence analysis, repeated measures actor/partner interdependence models, and vector field diagrams. As researchers continue to use and expand upon dyadic designs, new methods for addressing dyadic research questions will be developed.
Michaela Riediger and Antje Rauers
Experience-sampling methodology (ESM) captures everyday events and experiences during, or shortly after, their natural occurrence in people’s daily lives. It is typically implemented with mobile devices that participants carry with them as they pursue their everyday routines, and that signal participants multiple times a day throughout several days or weeks to report on their momentary experiences and situation. ESM provides insights into short-term within-person variations and daily-life contexts of experiences, which are essential aspects of human functioning and development. ESM also can ameliorate some of the challenges in lifespan-developmental methodology, in particular those imposed by age-comparative designs. Compared to retrospective or global self-reports, for example, ESM can reduce potential non-equivalence of measures caused by age differences in the susceptibility to retrospective memory biases. Furthermore, ESM maximizes ecological validity compared to studies conducted in artificial laboratory contexts, which is a key concern when different age groups may differentially respond to unfamiliar situations. Despite these strengths, ESM also bears significant challenges related to potential sample selectivity and selective sample attrition, participants’ compliance and diligence, measurement reactivity, and missing responses. In age-comparative research, these challenges may be aggravated if their prevalence varies depending on participants’ age. Applications of ESM in lifespan methodology therefore require carefully addressing each of these challenges when planning, conducting, and analyzing a study, and this article provides practical guidelines for doing so. When adequately applied, experience sampling is a powerful tool in lifespan-developmental methodology, particularly when implemented in long-term longitudinal and cross-sequential designs.
Noemi Pizarroso Lopez
Historical psychology claims that the mind has a history, that is, that our ways of thinking, reasoning, perceiving, feeling, and acting are not necessarily universal or invariable, but are instead subject to modifications over time and space. The theoretical and methodological foundations of this movement were laid in France by psychologist Ignace Meyerson in his book Les fonctions psychologiques et les œuvres, published in 1948. His program stressed the active, experimental, constructive nature of human behavior, spanning behavioral registers as diverse as the linguistic, the religious, the juridical, the scientific/technical, and the artistic. All these behaviors involve aspects of different mental functions that we can infer through a proper analysis of “works,” considered as consolidated testimonies of human activity. As humanity’s successive achievements, constructed over the length of all the paths of the human experience, they are the materials with which psychology has to deal.
Meyerson refused to propose an inventory of functions to study. As unstable and imperfect products of a complex and uncertain undertaking, they can be analyzed only by avoiding the counterproductive prejudice of metaphysical fixism. Meyerson spoke in these terms of both deep transformations of feelings, of the person, or of the will, and of the so-called “basic functions,” such as perception and the imaginative function, including memory, time, space, and object.
Before Meyerson the term “historical psychology” had already been used by historians like Henri Berr and Lucien Febvre, a founding member of the Annales school, who firmly envisioned a sort of collective psychology of times past. Meyerson and his disciples eventually vied with their fellow historians of the Annales school for the label of “historical psychology” and criticized their notions of mentality and outillage mental. The Annales historians gradually abandoned the label, although they continued to cultivate the idea that mental operations and emotions have a history through the new labels of a “history of mentalities” and, more recently at the turn of the century, a “history of emotions.” While Meyerson and a few other psychologists kept using the “historical psychology” label, however, mainstream psychology remained quite oblivious to this historical focus. The greatest efforts made today among psychologists to think of our mental architecture in terms of transformation over time and space are probably to be found in the work of Kurt Danziger and Roger Smith.
Stephanie J. Wilson, Alex Woody, and Janice K. Kiecolt-Glaser
Inflammatory markers provide invaluable tools for studying health and disease across the lifespan. Inflammation is central to the immune system’s response to infection and wounding; it also can increase in response to psychosocial stress. In addition, depression and physical symptoms such as pain and poor sleep can promote inflammation and, because these factors fuel each other, all contribute synergistically to rising inflammation. With increasing age, persistent exposure to pathogens and stress can induce a chronic proinflammatory state, a process known as inflamm-aging.
Inflammation’s relevance spans the life course, from childhood to adulthood to death. Infection-related inflammation and stress in childhood, and even maternal stress during pregnancy, may presage heightened inflammation and poor health in adulthood. In turn, chronically heightened inflammation in adulthood can foreshadow frailty, functional decline, and the onset of inflammatory diseases in older age.
The most commonly measured inflammatory markers include C-reactive protein (CRP) and proinflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). These biomarkers are typically measured in serum or plasma through blood draw, which capture current circulating levels of inflammation. Dried blood spots offer a newer, sometimes less expensive collection method but can capture only a limited subset of markers. Due to its notable confounds, salivary sampling cannot be recommended.
Inflammatory markers can be added to a wide range of lifespan developmental designs. Incorporating even a single inflammatory assessment to an existing longitudinal study can allow researchers to examine how developmental profiles and inflammatory status are linked, but repeated assessments must be used to draw conclusions about the associations’ temporal order and developmental changes. Although the various inflammatory indices can fluctuate from day to day, ecological momentary assessment and longitudinal burst studies have not yet incorporated daily inflammation measurement; this represents a promising avenue for future research.
In conclusion, mounting evidence suggests that inflammation affects health and disease across the lifespan and can help to capture how stress “gets under the skin.” Incorporating inflammatory biomarkers into developmental studies stands to enhance our understanding of both inflammation and lifespan development.
Eric S. Cerino and Karen Hooker
Intraindividual variability (IIV) refers to short-term fluctuations that may be more rapid, and are often conceptualized as more reversible, than developmental change that unfolds over a longer period of time, such as years. As a feature of longitudinal data collected on micro timescales (i.e., seconds, minutes, days, or weeks), IIV can describe people, contexts, or general processes characterizing human development. In contrast to approaches that pool information across individuals and assess interindividual variability in a population (i.e., between-person variability), IIV is the focus of person-centered studies addressing how and when individuals change over time (i.e., within-person variability). Developmental psychologists interested in change and how and when it occurs, have devised research methods designed to examine intraindividual change (IIC) and interindividual differences in IIC. Dispersion, variability, inconsistency, time-structured IIV, and net IIV are distinct operationalizations of IIV that, depending on the number of measures, occasions, and time of measurement, reflect unique information about IIV in lifespan developmental domains of interest. Microlongitudinal and measurement-burst designs are two methodological approaches with intensive repeated measurement that provide a means by which various operationalizations of IIV can be accurately observed over an appropriate temporal frame to garner clearer understanding of the dynamic phenomenon under investigation. When methodological approaches are theoretically informed and the temporal frame and number of assessments align with the dynamic lifespan developmental phenomenon of interest, researchers gain greater precision in their observations of within-person variability and the extent to which these meaningful short-term fluctuations influence important domains of health and well-being. With technological advancements fueling enhanced methodologies and analytic approaches, IIV research will continue to be at the vanguard of pioneering designs for elucidating developmental change at the individual level and scaling it up to generalize to populations of interest.
Philip Parker and Robert Brockman
Longitudinal structural equation modeling (LSEM) is used to answer lifespan relevant questions such as (a) what is the effect of one variable on change in and other, (b) what is the average trajectory or growth rate of some psychological variable, and (c) what variability is there in average trajectories and what predicts this variability. The first of these questions is often answered by a LSEM called an autoregressive cross-lagged (ACL) model. The other two questions are most typically answered by an LSEM called a latent growth curve (LGC). These models can be applied to a few time waves (measured over several years) or to many time waves (such as present in diary studies) and can be altered, expanded, or even integrated. However, decisions on what model to use must be driven by the research question. The right tool for the job is not always the most complex. And, more importantly, the right tool must be matched to the best possible research design. Sometimes in lifespan research the right tool is LSEM. However, researchers should prioritize research design as well as careful specification of the processes and mechanisms they are interested in rather than simply choosing the most complicated LSEM they can find.
Gawon Cho, Giancarlo Pasquini, and Stacey B. Scott
The study of human development across the lifespan is inherently about patterns across time. Although many developmental questions have been tested with cross-sectional comparisons of younger and older persons, understanding of development as it occurs requires a longitudinal design, repeatedly observing the same individual across time. Development, however, unfolds across multiple time scales (i.e., moments, days, years) and encompasses both enduring changes and transient fluctuations within an individual. Measurement burst designs can detect such variations across different timescales, and disentangle patterns of variations associated with distinct dimensions of time periods. Measurement burst designs are a special type of longitudinal design in which multiple “bursts” of intensive (e.g., hourly, daily) measurements are embedded in a larger longitudinal (e.g., monthly, yearly) study. The hybrid nature of these designs allow researchers to address questions not only of cross-sectional comparisons of individual differences (e.g., do older adults typically report lower levels of negative mood than younger adults?) and longitudinal examinations of intraindividual change (e.g., as individuals get older, do they report lower levels of negative mood?) but also of intraindividual variability (e.g., is negative mood worse on days when individuals have experienced an argument compared to days when an argument did not occur?). Researchers can leverage measurement burst designs to examine how patterns of intraindividual variability unfolding over short timescales may exhibit intraindividual change across long timescales in order to understand lifespan development. The use of measurement burst designs provides an opportunity to collect more valid and reliable measurements of development across multiple time scales throughout adulthood.
Joseph E. Gaugler, Colleen M. Peterson, Lauren L. Mitchell, Jessica Finlay, and Eric Jutkowitz
Mixed methods research consists of collecting and analyzing qualitative and quantitative data within a singular study. The “methods” of mixed methods research vary, but the ultimate goal is to provide greater understanding and explanation via the integration of qualitative and quantitative data. Mixed methods studies have the potential to advance our understanding of complex phenomena over time in adult development and aging (e.g., depression following the death of a spouse), but the utility of this approach depends on its application. The authors systematically searched the literature (CINHAL, Embase, Ovid/Medline, PubMed, PsychInfo, and ProQuest) to identify longitudinal mixed methods studies focused on aging. They identified 6,351 articles published between 1994 and 2017, of which 174 met the inclusion criteria. The majority of mixed methods studies reported on the evaluation of interventions or educational programs. Non-interventional studies tended to report on experiences related to the progression of various health conditions, the needs and experiences of caregivers, and the lived experiences of older adults. About half (n = 81) of the mixed methods studies followed a sequential explanatory design where a qualitative component followed quantitative evaluation, and most of these studies achieved “integration” by comparing qualitative and quantitative data in Results sections. There was considerable heterogeneity across studies in terms of overall design (randomized trials, program evaluations, cohort studies, and case studies). As a whole, the literature suffered from key limitations, including a lack of reporting on sample selection methodology and mixed methods design characteristics. To maximize the value of mixed methods in adult development in aging research, investigators should conform to recommended guidelines (e.g., depict participant study flow and use recommended notation) and consider more sophisticated mixed methods applications to advance the state of the art.
Alexandre J.S. Morin and David Litalien
As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.
Johnson Ching Hong Li and Virginia Man Chung Tze
In behavioral, social, and developmental research, researchers often begin with a fundamental question that examines whether there is a significant relationship between an independent variable (IV; e.g., video games) and a dependent variable (DV; e.g., aggression). However, examining this simple IV-DV relationship is not sufficient in most research scenarios given that this relationship may differ across the levels of a third variable, which is known as a moderator. For example, researchers may examine the degree to which the relationship between an independent variable and a dependent variable differs across the levels of a moderator or moderators (e.g., gender, ethnicity, socioeconomic status, intervention) to provide a more complete picture of the IV-DV effect and how this effect is or is not applicable to certain groups of participants.
In lifespan developmental research, a key component lies in the study of change, growth, or trajectory of one’s life over time. Undoubtedly, not all individuals may follow the same developmental change or growth over time and examining moderators (e.g., gender, intervention, etc.) that may explain these individual changes is crucial for researchers to better understand the effects on their research investigation and for practical implications. The existing literature shows that conceptual and methodological strategies for moderation analysis have been developed and evolved in lifespan developmental psychology. In particular, researchers in lifespan developmental psychology have used various types of moderation analyses, including assessing whether moderators can explain the pretest and posttest difference based on the conventional analysis of variance (ANOVA) framework and evaluating whether moderators may explain how different individuals follow or deviate from the general growth and trajectory based on advanced latent growth curve modeling (LGCM). Researchers who study lifespan development have realized the importance of moderation effects in their work. In light of the complexity of current biological, psychological, and social factors embedded in lifespan developmental research, the trend of utilizing more sophisticated LGCM than ANOVA to understand the growth trajectories will receive more attention in the future.
Kimberly L. Fine and Kevin J. Grimm
Multilevel modeling is a data analytic framework that is appropriate when analyzing data that are dependent due to the clustering of observations in higher-level units. Clustered data appear in a variety of disciplines, which makes multilevel modeling a necessary data analytic tool for many researchers. Longitudinal data are a special kind of clustered data as the repeated observations are clustered within individuals. Multilevel models can be applied to longitudinal data to examine how individuals change over time and how individuals differ in their change processes over time. For longitudinal data, linear multilevel models, where the fixed- and random-effects parameters enter the model in a linear fashion, and nonlinear multilevel models, where at least one fixed-and/or random-effect parameter enters the model in a nonlinear fashion are commonly estimated to examine different forms of the individual change process. Multilevel structural equation modeling is an extension of multilevel modeling that allows for multivariate outcomes, and this framework is very useful for modeling multivariate longitudinal data (e.g., multivariate growth models and second-order growth models).
Sven Hroar Klempe
The term “psychology” was applied for the first time in the 16th century. Yet the most interesting examples appeared in three different contexts. The Croatian poet and humanist Marko Marulić (ca. 1520), the German philosopher and Calvinist Johann Thomas Freig (1575), and the German Lutheran philosopher Rudolph Goclenius (1590). Marulić’s manuscript is likely lost, and neither of the other two defined the term. Even the interests of the three went apparently in different directions. Marulić focused on poetry and history, Freig on physica, and Goclenius on theological issues. Nevertheless, they had something in common, and this may represent the gate through which the ways they conceived the term can be understood. They all dealt with the soul, but also that it was a highly disputable concept and not uniformly understood. Another commonality was the avoidance or reinterpretation of Aristotle’s philosophy. The Florentines’ cultivation of Plato had influenced Marulić. Freig was a Ramist, thus, also a humanist who approached philosophical questions rhetorically. Goclenius belonged partly to the same movement. Consequently, they all shared a common interest in texts and language. This is just one, yet quite important aspect of the origin of psychology as a science. Thus, these text- and humanity-oriented aspects of psychology are traceable from the very beginning. This reaches a peak point when Alexander Baumgarten publishes his two volumes on aesthetics, as they were based on Christian Wolff’s Psychologia empirica (1732). They are also traceable in Kant’s critical phase, and even more in Wundt’s folkpsychology. Thus there is a more or less continuous line from the very first uses of the term psychology and some tendencies in social and cultural psychology. In other words, psychology is pursued along an historical line that ends up in the German, and not the British enlightenment.
Oscar Gonzalez and David P. MacKinnon
Lifespan developmental research studies how individuals change throughout their lifetime and how intraindividual or interindividual change leads to future outcomes. Lifespan researchers are interested in how developmental processes unfold and how specific developmental pathways lead to an outcome. Developmental processes have been previously studied using developmental cascade models, concepts of equifinality and multifinality, and developmental interventions. Statistical mediation analysis also provides a framework for studying developmental processes and developmental pathways by identifying intermediate variables, known as mediators, that transmit the effect between early exposures and future outcomes. The role of statistical mediation in lifespan developmental research is either to explain how the developmental process unfolds, or to identify mediators that researchers can target in interventions so that individuals change developmental pathways. The statistical mediation model is inherently causal, so the relations between the exposures, mediators, and outcomes have to be correctly specified, and ruling out alternative explanations for the relations is of upmost importance.
The statistical mediation model can be extended to deal with longitudinal data. For example, the autoregressive mediation model can represent change through time by examining lagged relations in multiwave datasets. On the other hand, the multilevel mediation model can deal with the clustering of repeated measures within individuals to study intraindividual and interindividual change. Finally, the latent growth curve mediation model can represent the variability of linear and nonlinear trajectories for individuals in the variables in the mediation model through time. As a result, developmental researchers have access to a range of models that could describe the theory of change they want to study. Researchers are encouraged to consider mechanisms of change and to formulate mediation hypotheses about lifespan development.
In the literature of mainstream scientific psychology, German scholar William Stern has been known primarily (if at all) as the inventor of the intelligence quotient (IQ). In fact, however, Stern’s contributions to psychology were much greater and more consequential than this. In this all-inclusive article, I have sought to provide readers with a fuller appreciation for the breadth and depth of Stern’s work, and, in particular, for that comprehensive system of thought that he elaborated under the name “critical personalism.” Drawing frequently on translated quotations from Stern’s published works, and on his personal correspondence with the Freiburg philosopher Jonas Cohn, I have endeavored to show how Stern was much more than “the IQ guy.” During the first 20 years of his academic career, spent at the University of Breslau in what is now the Polish city of Wroclaw, Stern founded that sub-discipline of psychology that would be concentrated on the study of individual differences in various aspects of human psychological functioning. He also made major contributions to that sub-discipline referred to at the time as “child” psychology, and laid the foundations for a comprehensive system of thought that he would name “critical personalism.” After relocating to Hamburg in 1916, Stern continued his scholarly efforts in these domains, taught courses both in psychology and in philosophy at the university that opened its doors there in 1919, and played major administrative roles there in the institutional homes of both disciplines until forced to flee Nazi Germany in 1934. The present chapter highlights ways in which, over the course of his scholarly career, Stern boldly opposed certain trends within mainstream thinking that were ascendant during his time.