Dynamic Process Methodology in the Social and Developmental Sciences

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Again, once obtained and modeled, the specific aspects of the dynamic processes supporting individual functioning that are indicated by model parameters can be examined with respect to aging. Consistent with the typical examination of age differences and age-related changes, we can hypothesize about and track how the dynamic processes producing individual behavior over the short term change over the long term. Questions might take the form: Does the speed of the adaptation process change with age? Are age-related differences or changes in health related to qualitative differences in the effectiveness of a stability-maintenance process?

For example, Brauer, Woollacott and Shumway-Cook found differential patterns of moment-to-moment task performance among younger and older adults attempting to maintain postural control while completing another attention-demanding cognitive task. Specifically, the ordering of compensatory movements and responses differed, indicating differential progression of stability-maintenance processes with age and impairment. Somewhat parallel notions emerge in life-span theories of developmental regulation and coping, which suggest that the dynamics of adaptation processes change qualitatively with age.

In particular, while stability-maintenance processes may play a primary role in adaptation at younger ages, transformational change processes that involve a lowering of equilibrium set-points become increasingly prominent at older ages e. In the final section of this article, we will discuss how measurement burst study designs may facilitate such multiple-time scale inquiries.

Before doing so, though, we turn to the analytical methods. In the preceding section, we drew a conceptual distinction between constructs used to describe individuals' inherent capacity for change — dynamic characteristics e. In this section, we tether this conceptual distinction to a corresponding methodological distinction between measures of net intraindividual variability and models of time-structured intraindividual variability.

We follow most closely in the footsteps Fiske and Rice laid down in their classic review of intraindividual response variability, and attempt to push forward in a way that accommodates and meshes their framework with many of the conceptual and methodological innovations that have occurred in the decades since.

Fiske and Rice defined pure variability as variability across repeated assessments where a the individual was exposed at each occasion to the same stimulus or to objectively indistinguishable stimuli, and b the total situation in which the responses are made is the same on all occasions. Said differently, pure intraindividual variability is variability that manifests in a static, unchanging, stable context.

Specifically, Type I intraindividual variability requires conformity to the additional assumptions that c the order of responses is immaterial, meaning that the data show no systematic trend over time e. Data that violate this latter assumption and that contain lagged effects or cycles patterns other than a monotonic function of time were considered to be manifestations of Type II reactive intraindividual variability. Coming from a developmental perspective where one of the fundamental principles is that both the individual and his or her environment are always changing e. Net intraindividual variability is constituted of short-term within-person changes that are treated as being unstructured in relation to time e.

Key to the distinction is that with net intraindividual variability, the ordering of the occasions is treated as immaterial, whereas with time-structured intraindividual variability the serial ordering of the repeated measurements is of material interest.

To be clear, the ordering of repeated assessments in a given study is inherently time-structured e. However, it is at the discretion of the researcher to select an analytic approach that treats the data as time-structured or not. Such decisions about if and how to decompose total variability into time-structured and net portions and their relative size are typically based on a number of different considerations, including conceptual arguments and study design — a point we will discuss in more detail below.

Also, our distinctions are designed for application to within-person variability. The structure of between-person differences is not addressed specifically, nor do we presume that the distinction has a straightforward between-person variability analogue. At most, we suggest that summary descriptions of an individuals' time-structured and net intraindividual variability measured or modeled individual by individual can be used in a secondary analysis of between-person differences.

Our focus is on how the methodological landscape can be viewed in relation to the dynamic characteristics and processes that manifest at the level of the individual. The main objective in describing them is to identify, extract, and measure the systematic serial ordering within a stream of observed behavior within-person. Methodologically, the repeated measurements obtained from a single individual are examined for systematic time-related structures.

The data are modeled in relation to time. The parameters obtained from the time-structured model are used to describe specific aspects of the dynamic process of interest. For example, the rate of linear change obtained from a regression model wherein repeated measures of cognitive performance are regressed on an index of time e. By definition, time-structured variability focuses on the temporal relations in the data. The time-locked, serial ordering of the data is used to describe and understand dynamic processes. For example, Carstensen, Gottman, and Levenson , in describing regulatory and control processes occurring while members of older married couples interacted with one another, modeled theoretically interesting sequences of global affect, some indicating stability-maintenance processes e.

Note that the time metric is open. Time can be indexed in a number of different ways, including calendar time, time from some universal event e. Further, the progression or serial ordering of time can also be conceptualized and tracked in different ways. That is, the relevant ordering of observations may be in relation to the progression of clock time, in relation to progression of psychological or social time, or in relation to a series of individually defined events e. The only basic requirement is that the observations have a specific ordering, but the metric is of additional use especially with respect to causality if it retains the special quality that time always moves forward.

The methods used to measure and model time-structured intraindividual variability all make heavy use of the time-ordered nature of the data. The objective is to construct a statistical model that adequately describes systematic time-dependent structures in the data and that allows for the prediction of future behaviors or outcomes. Estimates of specific parameters from the selected model of time-structured intraindividual variability might then be used as an indicator of substantively important characteristics of specific within-person processes.

These models include auto-regressive models, moving average models, and spectral analysis — or more generally, time-series analysis in the time domain e. Extensions to multivariate time-structured intraindividual variability are also available. Although these multivariate models have not yet been applied widely in the psychology of aging, there are some exemplars. Chow, Nesselroade, Shifren, and McArdle ; see also Shifren, Hooker, Wood and Nesselroade, used dynamic factor models to extract systematic patterns of change in the emotional states experienced by a sample of Parkinson's patients across 70 days.

These researchers used the time-ordered nature of the repeated measures data to identify the extent to which individuals' current mood states systematically persisted from day-to-day. In particular, they obtained a description of the stability-maintenance processes engaged by a subset of individuals who maintained positive mood in the face of a debilitating disease characterized by symptoms that fluctuate unpredictably from day-to-day.

There is little doubt that the technological advances in intensive, real-time data collection and highly speeded data manipulation and calculation, and the long base of theorizing about dynamic processes in relation to aging will lead to much more applications of such methods Stone et al.

For example, vector autoregressive methods and multivariate spectral analysis are being used in the modeling of functional connectivity among brain regions e. Combining such models with intensive longitudinal data collected across many minutes, hours, days, or weeks allows for capture of the dynamic processes occurring at other micro-time scales.

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For example, a dancers' flexibility is indicated by the diversity of positions into which they can contort their bodies. The main objective in describing an individuals' flexibility, or other dynamic characteristic, is to identify, extract, and measure the diversity of behaviors exhibited over time. Methodologically, the repeated measurements obtained from a single individual are collected into a distribution or ensemble of scores.

Then, assuming exchangeability of observations, standard measures of central tendency and dispersion are used to describe that individual's total ensemble of behaviors. Of specific interest with respect to quantifying dynamic characteristics describing an individual's capacity for change are indices of dispersion that capture information about where the ends of the distribution are.

For example, an intraindividual standard deviation iSD , calculated on the distribution of scores obtained across repeated measurements of a single individual, describes the extent to which his or her scores tend to vary around the mean score. A large iSD would indicate that the individual had a wide range of behaviors e. Calculated separately for the distributions obtained from multiple individuals, the iSD or other measures of dispersion can be treated as a measure of interindividual differences in capacity for change e.

Measures and models of net intraindividual variability assume exchangeability of observations or more precisely, locally independent and identically distributed observations i. Consider that the concept of flexibility does not have to do with the process by which the dancer moves from one contorted position to another. Rather, it is simply about the range of possible positions the level to which one leg can be raised while standing on the other, the extent to which the back bends, etc.

In this sense, when measuring flexibility, the serial or time-structured ordering of the contortions does not matter, only their overall diversity. Whether a flexible person can engage in the process of dance is a separate question. To be clear, a key assumption in the calculation of indices of net intrainidividual variability e. For example, the mean, variance, skewness, and kurtosis of the distribution 1 st , 2 nd , 3 rd , and 4 th moments provide a relatively comprehensive description of how that individual's behaviors are dispersed across the range of possible scores.

The potential costs of the needed assumptions include incongruence between the dynamic characteristics of human behavior we seek to observe and examine and the statistical viability of measuring and modeling specific aspects of intraindividual variability. Given the conceptual priority developmentalists place on how individuals change with respect to time, it may be difficult to imagine that repeated observations of the same person are ever truly independent.

Although a few indices of dispersion make use of the time-ordered nature of repeated measurements e. In principle, before calculating statistics that require the independence assumption all time-related patterns should be removed or covaried. Time dependencies in the data should be accounted for and set aside. Thus, our use of the term net intraindividual variability similar usage as in net revenue or net profit.

Assuming independent and identically distributed observations, a plethora of summary statistics can be used to quantify how the repeated observations obtained from a single individual are dispersed or distributed across scores or categories. From these, one should choose the index that most appropriately coincides with the theoretical construct it is intended to measure.

Of the many indices and models available, the intraindividual standard deviation iSD is by far the most popular index of intraindividual variability in psychological aging research and has been used as a measure of a wide range of dynamic characteristics e. Although the iSD has proven particularly useful, there are also other quantifications of dispersion and models available. Correspondent indices for count data include the index of dispersion IDV; also called coefficient of dispersion or variance to mean ratio , and for categorical data, indices of qualitative variation see Wilcox, and entropy Shannon, Net intraindividual variability measures can also be used to describe multivariate data.

The amount or extent of association among multiple variables assessed in tandem repeatedly from a single individual or entity multivariate time series is often called intraindividual covariation or coupled within-person variation e. In the bivariate case, the objective is the same as above — to quantify the amount of observed covariation between the two variables. Analogues to the univariate measures listed previously would include the various within-person correlation coefficients e.

Again, the measures of intraindividual covariation or more generally, association are used as measures of individuals' dynamic characteristics. For example, within-person correlations between positive and negative affect have been used as a measure of poignancy, individuals' capacity to experience mixed emotions e. Similarly, within-person associations between repeated measures of stress and negative affect have been used to measure individuals' affective reactivity e. Multilevel modeling has provided a useful and increasingly popular framework for examining between-person differences in bivariate within-person associations see Bolger et al.

Note, however that within the multilevel regression framework, precedence is implicitly given to one or the other of two variables. One variable is treated as an outcome, the other as a predictor. Care should be taken when interpreting results with respect to the intended univariate or bivariate nature of one's theoretical construct. For example, Moskowitz and Zuroff used the bivariate or circular measures of dispersion to measure individual's behavioral flexibility with respect to behavioral extremity pulse and interpersonal style spin.

Similar applications to repeated measures of individuals' core affect, as defined by dimensions of valence pleasure-displeasure and arousal activation-deactivation make use of pulse and spin to measure individuals' emotional variability Kuppens et al. In short, P-technique methods model the intraindividual variation of and covariation among many measures using data reduction methods e. The obtained summary measures e. For example, Ong and Bergeman used P-technique analyses intraindividual principal components to quantify the intraindividual structure of day-to-day emotional experiences as an index of individuals' capacity to distinguish between pleasant and unpleasant feeling states, emotional complexity considered an indicator of adaptational effectiveness; for other applications, see Carstensen et al.

In summarizing this section on the methods being used to examine time-structured and net intraindividual variability, we highlight the need for care in the alignment of theoretical conceptions and the methods meant to render them operational e. The distinction between net and time-structured intraindividual variability is a methodological one, wherein one set of measures and models requires or assumes that data conform to iid assumptions time only being used as a categorical identifier and the other set explicitly models data with time-related dependencies. We have tethered this distinction to two sets of constructs, dynamic characteristics and dynamic processes.

Formulaic calculation of the iSD and other measures of net intraindividual variability rests on the assumption that the observations are random draws from a single distribution. As a consequence, the use of such indices implies that the ordering of occasions is immaterial and that the same summary information would be obtained even when the data are reshuffled with respect to time. Thus, the constructs these measures represent are inherently about the possible range of behavior, not the progression of behavior.

In contrast, when crucial aspects of the dynamic concept to be measured include, are defined by, or imply systematic progression or patterns of behavior, then models of time-structured intraindividual variability may be more appropriate. For example, negative feedback processes imply an ordering wherein discrepancies between current and preferred states reduce as time progresses forward e. Appropriate measures of such regulatory processes, in essence, require specificity of the time course and time-ordered patterning of short-term change — and imply specific models of time-structured intraindividual variability e.

As noted in the lists of measures and models above, there are many tools available for quantifying and studying both time-structured and net intraindividual variability. The same data can be analyzed in many ways, each of which invoke a particular set of assumptions and can be used to indicate one or more possible constructs. What is removed by one filter may be passed over by another. Thus, theory plays an exquisitely crucial role in how we choose from among the many analytical possibilities.

So does study design — particularly the timing and spacing of observations. Too many and the choice of filters becomes more difficult. The end result is that the study may lead one toward a set of measures and models that may or may not align with or indicate the intended dynamic characteristics or dynamic processes. See the Boker, Molenaar, and Nesselroade's comment for a cogent discussion of this problem.

Further, as with all research, the particular constructs and variables one is examining must be considered as but one portion of a system, an incomplete model of individual's total functionality. Intraindividual variability measures and models must be interpreted within a broader framework of interindividual and contextual differences, historical changes, and so on e. In sum, intraindividual variability research requires a lot of hard choices, many of which we, as a field, are not yet in a particularly informed position to make.

Viewing these difficulties as part of the usual challenges faced in behavioral research, the point we wish to underscore is that there are many measures and models that can be used to extract and describe short-term change. These methods offer the possibility to articulate and examine a multitude of conceptually interesting dynamic characteristics and processes. As the study of intraindividual variability proceeds and expands, and more concepts and methods are developed and used, we have the opportunity to outline precise definitions of the individual characteristics and processes of interest and to carefully tether them to the quantitative and qualitative assumptions on which the correspondent indices or models and inquiries are based.

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In our view, the tighter and more efficient the tie, the better. It offers the possibility to measure and model dynamic characteristics and dynamic processes. At the micro-time scale researchers in many areas make use of methods wherein multiple reports or assessments are obtained over a relatively short span of time, including diary, ecological momentary assessment EMA , multi-trial e. But this is only part of the story of aging. Nesselroade suggested making use of a measurement-burst design that involves measuring individuals on multiple time scales.

At the micro-time level, observations are obtained from one or more individuals at closely spaced intervals e. At the macro-time level, these same individuals are measured again at a wider interval e. Thus, the multi-time-scale repeated measures design combines the benefits of short-term longitudinal studies and the study of net and time-structured intraindividual variability with those of long-term longitudinal studies and the study of aging.

To illustrate, consider the bursts of measurement depicted in the A circles of Figure 1. Both bursts of intraindividual variability were obtained from the same person. The burst on the left was assessed at an earlier age and showed fluctuations characterized by low levels of dispersion. The burst on the right was assessed at a later age, at which time the person exhibited a more disperse set of short-term fluctuations in their behavior. The long line connecting the bursts obtained from this individual implies intraindividual change, aging , of the dynamic characteristic measured in the burst e.

In parallel, the bursts of measurement depicted in the B circles imply intraindividual, age-related change in the dynamic process indicated by the amplitude and frequency of oscillations captured by a sinusoidal model of time-structured intraindividual variability. Reviewing and elaborating the potential of measurement-burst designs for investigating human behavior, Sliwinski highlighted that the design's multi-time scale features augment the information obtained from conventional single time-scale studies.

These include improved precision and power for estimating long-term change e. In line with our presentation of the study of intraindividual variability as affording the opportunity to measure and model both net and time-structured intraindividual variability, we add that the measurement-burst design also provides the opportunity to track long-term changes in the dynamic processes that underlie behavior.

Of course, burst designs also come with problems. Sliwinski noted some of the drawbacks, including: We see this last issue as one of the primary obstacles in designing a successful burst study. When, how often, and what should we measure? Good answers are not readily available beyond — measure as often as possible for as long as possible e.

Unable to conduct intensive measurements of all variables, all the time and still having a participant or two left we are faced with the difficult task of making very specific predictions about when and how the constructs and processes of interest interact and manifest. In many cases, we will be wrong, and the data will lead us astray. But, with faith and a diligent accounting and reporting of how behavior manifests over micro-, macro-, exo- and other time scales might just bring together some of the pieces of the puzzle needed for further understanding of the variety and complexities of development.

As a part of this collection of articles on intraindividual variability and aging, our purpose has been to introduce some concepts and indicate why and how the study of short-term intraindividual variability can be used as a tool for examining the aging of dynamic properties of human behavior. In doing so, we hope to have illustrated three points: Across a wide variety of domains, the study of intraindividual variability is vibrant and pushing forward our understanding of the dynamic nature of individual functioning and its age-related change.

With diligence and planning, we might even then be able to splice those clips together to describe, explain, predict and potentially modify the complex stories that unfold over a life span — development in process. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Thanks to Lauren Molloy and Frank Infurna for help in collecting and organizing literature, and to Ron Spiro and the reviewers for their thoughtful contributions and shaping. Nesselroade for the wonderful conversations from which exciting ideas continually emerge. The following manuscript is the final accepted manuscript.

It has not been subjected to the final copyediting, fact-checking, and proofreading required for formal publication. It is not the definitive, publisher-authenticated version. The American Psychological Association and its Council of Editors disclaim any responsibility or liabilities for errors or omissions of this manuscript version, any version derived from this manuscript by NIH, or other third parties. The published version is available at www.

We thus use the term random to highlight that such fluctuations are treated as being unstructured in relation to time. In physics and chemistry, both quasi-static processes and steady-state processes are defined. National Center for Biotechnology Information , U. Author manuscript; available in PMC Dec 1. Nilam Ram a, b and Denis Gerstorf a, b. Author information Copyright and License information Disclaimer. The publisher's final edited version of this article is available at Psychol Aging. See other articles in PMC that cite the published article.

Intraindividual Variability First, we clarify some concepts and terminology. Open in a separate window. Dynamic Characteristics and Processes Theoretical accounts of individual development include descriptions of both individuals' inherent capacity for change and the change processes that individuals' engage as they respond to endogenous and exogenous influences e. Dynamic Characteristics Borrowing and expanding on terminology and concepts used in other disciplines e. Dynamic Characteristics and Aging Once extracted from features of short-term change, the specific aspects of individual functioning indicated by these dynamic constructs can be examined with respect to aging.

Dynamic process methodology in the social and developmental sciences

Dynamic Processes At another level of inquiry, psychologists and developmentalists make good use of constructs that describe systematic changes in behavior, including regulation, homeostasis, adaptation, accommodation, differentiation, learning, and metamorphosis. Dynamic Processes and Aging Again, once obtained and modeled, the specific aspects of the dynamic processes supporting individual functioning that are indicated by model parameters can be examined with respect to aging.

Time-Structured and Net Intraindividual Variability In the preceding section, we drew a conceptual distinction between constructs used to describe individuals' inherent capacity for change — dynamic characteristics e. Time-Structured Intraindividual Variability Dynamic processes e. Measures and Models The methods used to measure and model time-structured intraindividual variability all make heavy use of the time-ordered nature of the data. Net Intraindividual Variability Dynamic characteristics e. Measures and Models Assuming independent and identically distributed observations, a plethora of summary statistics can be used to quantify how the repeated observations obtained from a single individual are dispersed or distributed across scores or categories.

Aligning Constructs and Methods In summarizing this section on the methods being used to examine time-structured and net intraindividual variability, we highlight the need for care in the alignment of theoretical conceptions and the methods meant to render them operational e. Synopsis As a part of this collection of articles on intraindividual variability and aging, our purpose has been to introduce some concepts and indicate why and how the study of short-term intraindividual variability can be used as a tool for examining the aging of dynamic properties of human behavior.

Intraindividual variability may not always indicate vulnerability in elders' cognitive performance. Theoretical propositions of lifespan developmental psychology: On the dynamics between growth and decline. Lifespan theory in developmental psychology. Handbook of child psychology Vol. Theoretical models of human development. Wiley; New York, NY: Introduction to research methods. Lifespan development and the brain: The perspective of biocultural co-constructivism. Cambridge University Press; New York: Science, behavior, and culture. Methodological issues in aging research.

Emotion regulation in recently bereaved widows: A dynamical systems approach. Differential structural equation models of intraindividual variability. New methods for the analysis of change. American Psychological Association; Washington, D. A method for modeling the intrinsic dynamics of intraindividual variability: Recovering the parameters of simulated oscillators in multi-wave panel data. Issues in intraindividual variability: Individual differences in equilibria and dynamics over multiple time scales.

Capturing life as it is lived. Annual Review of Psychology. Effects of daily stress on negative mood. Tenacious goal pursuit and flexible goal adjustment: Explication and age-related analysis of assimilative and accommodative strategies of coping. The influence of a concurrent cognitive task on the compensatory stepping response to a perturbation in balance-impaired and healthy elders. Representing psychological processes with dynamic factor models: Some promising uses and extensions of ARMA time series models.

A festschrift to Roderick P. Ecological momentary assessment in aging research: Journal of Psychiatry Research. Emotional behavior in long-term marriage. Emotional experience in everyday life across the adult lifespan. Journal of Personality and Social Psychology. Measurement in relation to state-dimension, trait change, lability, and process concepts. Handbook of multivariate experimental psychology. Their structure, growth, and action. P-technique demonstrated in determining psychophysical source traits in a normal individual.

Dynamic structure of emotions among individuals with Parkinson's disease. Capturing weekly fluctuation in emotion using a latent differential structural approach. Emotion regulation as a scientific construct: Challenges and directions for child development research. Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model.

Best methods for the analysis of change. Collins LM, Sayer A. Csikszentmihalyi M, Larson R. Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease. Intraindividual variability in perceived control in an older sample: The MacArthur successful aging studies. Mixed emotional experience in the face of a meaningful ending. The maturing architecture of the brain's default network.

Proceeding of the National Academy of Science. Modeling the clocks that time us. Modeling affective processes in dyadic relations via dynamic factor analysis. Humans as self-constructing living systems: A developmental perspective on personality and behavior. Sage; Newbury Park, CA: Normally occuring environmental and behavioral influences on gene activity: From central dogma to probalistic epigenesis. The mathematics of marriage: Integrating biological, behavioral, and social levels of analysis in early child development: Progress, problems, and prospects.

A key to evolutionary design. Integrating development, evolution and cognition. Problems in measuring change. Heckhausen J, Schulz R. A life-span theory of control. Assessing psychological change in adulthood: An overview of methodological issues. The state-space approach to modeling dynamic processes. Models for intensive longitudinal data. Oxford University Press; New York: Design and analysis of longitudinal studies of aging.