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.
Ronald E. Smith and Frank L. Smoll
Coaches occupy a central role in sport, fulfilling instructional, organizational, strategic, and social relationship functions, and their relationships with athletes influence both skill development and psychosocial outcomes of sport participation. This review presents the major theoretical models and empirical results derived from coaching research, focusing on the measurement and correlates of coaching behaviors and on intervention programs designed to enhance coaching effectiveness.
A strong empirical literature on motor skill development has addressed the development of technical sport skills, guided in part by a model that divides the skill acquisition process into cognitive, associative, and autonomous phases, each requiring specific coaching knowledge and instructional techniques. Social-cognitive theory’s mediational model, the multidimensional model of sport leadership, achievement goal theory, and self-determination theory have been highly influential in research on the psychosocial aspects of the sport environment. These conceptual models have inspired basic research on the antecedents and consequences of defined coaching behaviors as well as applied research on coach training programs designed to enhance athletes’ sport outcomes. Of the few programs that have been systematically evaluated, outcomes such as enjoyment, liking for coach and teammates, team cohesion, self-esteem, performance anxiety, athletes’ motivational orientation, and sport attrition can be influenced in a salutary fashion by a brief intervention with specific empirically derived behavioral guidelines that focus on creating a mastery motivational climate and positive coach-athlete interactions. However, other existing programs have yet to demonstrate efficacy in controlled outcome research.
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.
Rebecca A. Zakrajsek and Jedediah E. Blanton
It is important for sport and exercise psychology (SEP) professionals to demonstrate that the interventions they employ make a difference. Assessing the degree of an intervention’s effectiveness depends first and foremost on the nature and scope of the intervention (i.e., the objective of the intervention) and its targeted group. Traditionally, interventions have been quite varied between the fields of sport psychology and exercise psychology; a common thread however, can be seen as an enhancement of the sport or exercise experience, along with an attempt to help the individual better self-regulate engagement with the targeted behavior or mindset. The central aim of enhancing the experience and increased self-regulation is oriented toward performance enhancement within sport psychology interventions, whereas within exercise psychology interventions the orientation is toward physical-activity adoption and better exercise program adherence. Although the two fields may have different objectives, it can be argued that sport psychology interventions—specifically psychological skills training (PST) interventions—can inform SEP professionals’ research and applied practices with both the sport and exercise populations.
Psychological skills training includes the strategies and techniques used to develop psychological skills, enhance sport performance, and facilitate a positive approach to competition. Since the early 1980s, a growing body of evidence has supported that the PST interventions SEP professionals employ do make a difference. In particular, evidence from research in sport contexts supports the use of a multimodal approach to PST interventions—combining different types of psychological strategies (e.g., goal-setting, self-talk, imagery, relaxation)—because a multimodal approach has demonstrated positive effects on both psychological skills and sport performance. The research investigating the effectiveness of PST interventions in enhancing performance has primarily centered on adult athletes who compete at competitive or elite levels. Elite athletes are certainly important consumers of SEP services; however, SEP professionals have rightfully challenged researchers and practitioners to target other consumers of SEP services who they argue are as deserving of PST as elite athletes. For example, young athletes and coaches are two populations that have traditionally been overlooked in the PST research. PST interventions targeting young athletes can help them to develop (at the start of their sporting careers) the type of psychological skills that facilitate a positive approach to competition and better abilities to self-regulate their emotional responses to stressful competitive situations. Coaches are also performers with unique needs who could benefit from PST interventions. Researchers have begun to target these two populations and the results might be considered the most intriguing aspects of the current PST literature. Future research related to PST interventions should target exercise populations. Exercise professionals often operate as coaches in healthy behavior change (e.g., strength and conditioning coaches, personal trainers, etc.) and as such should also employ, and monitor responses to, PST.
To facilitate further development and growth of PST intervention research in both sport and exercise settings, SEP professionals are encouraged to include a comprehensive evaluation of program effectiveness. In particular, four major areas to consider when evaluating PST programs are (a) the quality of the PST service delivery (e.g., the knowledge, delivery style, and characteristics of the SEP professional); (b) assessment of the sport psychological strategies participants used as a result of the PST program; (c) participants’ perceptions of the influence of the PST program on their psychological skills, performance, and enjoyment; and (d) measurement of participants psychological skills, performance, and enjoyment as a result of the PST program.
Christiane A. Hoppmann, Theresa Pauly, Victoria I. Michalowski, and Urs M. Nater
Everyday salivary cortisol is a popular biomarker that is uniquely suited to address key lifespan developmental questions. Specifically, it can be used to shed light on the time-varying situational characteristics that elicit acute stress responses as individuals navigate their everyday lives across the adult lifespan (intraindividual variability). It is also well suited to identify more stable personal characteristics that shape the way that individuals appraise and approach the stressors they encounter across different life phases (interindividual differences). And it is a useful tool to disentangle the mechanisms governing the complex interplay between situational and person-level processes involving multiple systems (gain-loss dynamics). Applications of this biomarker in areas of functioning that are core to lifespan developmental research include emotional experiences, social contextual factors, and cognition. Methodological considerations need to involve careful thought regarding sampling frames, potential confounding variables, and data screening procedures that are tailored to the research question at hand.
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.
Rebecca K. Dickinson, Tristan J. Coulter, and Clifford J. Mallett
As a basic psychological framework, humanistic theory emphasizes a strong interest in human welfare, values, and dignity. It involves the study and understanding of the unique whole person and how people can reach a heightened sense of self through the process of self-actualization. The focus within humanism to encourage and foster people to be “all they can be” and develop a true sense of self links to a strengths-based approach in sports coaching and the defining principles of positive psychology. In the field of sport and performance psychology, positive psychology has been influential as a discipline concerned with the optimal functioning and human flourishing of performers. Since the 2000s, many sport and performance psychologists have embraced positive psychology as a theoretical basis for examining consistent and superior human performance. However, in the modern history of psychological science, positive psychology is not a new phenomenon; rather, it stems from humanism—the traditional “third wave” in psychology (after the dominance of psychoanalytic and behaviorist approaches).
Sport is recognized as a potentially influential context through which people at all levels and backgrounds can thrive. The tendency to focus on performance outcomes, however—winning and losing—often overshadows the potential of sport to achieve this aspirational goal. As evidence of this view, many high-performing athletes are commenting on their distressing experiences to reach the top and the “culture of fear” they have been exposed to as they pursue their own and others’ (e.g., institutional) ambitions (e.g., medaling at the Olympic Games). Humanism concerns itself with the quality of a person’s life, which includes, but also extends beyond such objective and classifying achievements. It is a person-centered approach to understanding the individual and his or her psychological, emotional, and behavioral reality. It seeks to help people define this reality more clearly in such a way that will help them feel good and perform at a high level. Humanism has been, therefore, an important school of thought for improving the lives and experiences of people who play sport as well as those who perform in various other contexts.
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.
Nasreen A. Sadeq and Victor Molinari
The need for facilities that provide residential aged care is expected to increase significantly in the near future as the global population ages at an unprecedented rate. Many older adults will need to be placed in a residential care setting, such as an assisted living facility (ALF) or nursing home, when their caregivers can no longer effectively manage serious medical or psychiatric conditions at home. Although the types of residential care settings worldwide vary considerably, long-term care residents (LTC) and staff benefit from environmental and cultural changes in LTC settings. Unlike traditional medical models of LTC, culture change advocates for a shift toward holistic, person-centered care that takes place in homelike environments and accounts for the psychosocial needs of residents. Carving out a role for family members and training professional caregivers to address behavioral problems and quality-of-life issues remain a challenge. In LTC settings, preliminary research indicates that implementing person-centered changes addressing resident and caregiver needs may lead to better health outcomes, as well as increased satisfaction among patients, families, and staff. With the burgeoning world population of older adults, it is incumbent that they be provided with optimal humane culturally sensitive care.
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.
Matthew S. Fritz and Houston F. Lester
Mediator variables are variables that lie between the cause and effect in a causal chain. In other words, mediator variables are the mechanisms through which change in one variable causes change in a subsequent variable. The single-mediator model is deceptively simple because it has only three variables: an antecedent, a mediator, and a consequent. Determining that a variable functions as a mediator is a difficult process, however, because causation can be inferred only when many strict assumptions are met, including, but not limited to, perfectly reliable measures, correct temporal design, and no omitted confounders. Since many of these assumptions are difficult to assess and rarely met in practice, the significance of a statistical test of mediation alone usually provides only weak evidence of mediation.
New methodological approaches are constantly being developed to circumvent these limitations. Specifically, new methods are being created for the following purposes: (1) to assess the impact of violating assumptions (e.g., sensitivity analyses) and (2) to make fewer assumptions and provide more flexible analysis techniques (e.g., Bayesian analysis or bootstrapping) that may be more robust to assumption violations. Despite these advances, the importance of the design of a study cannot be overstated. A statistical analysis, no matter how sophisticated, cannot redeem a study that measured the wrong variables or used an incorrect temporal design.
Nicole D. Anderson
Healthy aging is accompanied by decrements in episodic memory and working memory. Significant efforts have therefore been made to augment episodic and working memory in healthy older adults. Two principal approaches toward memory rehabilitation adults are restorative approaches and compensatory approaches. Restorative approaches aim to repair the affected memory processes by repeated, adaptive practice (i.e., the trained task becomes more difficult as participants improve), and have focused on recollection training, associative memory training, object-location memory training, and working memory training. The majority of these restorative approaches have been proved to be efficacious, that is, participants improve on the trained task, and there is considerable evidence for maintenance of training effects weeks or months after the intervention is discontinued. Transfer of restorative training approaches has been more elusive and appears limited to other tasks relying on the same domains or processes. Compensatory approaches to memory strive to bypass the impairment by teaching people mnemonic and lifestyle strategies to bolster memory performance. Specific mnemonic strategy training approaches as well as multimodal compensatory approaches that combine strategy training with counseling about other factors that affect memory (e.g., memory self-efficacy, relaxation, exercise, and cognitive and social engagement) have demonstrated that older adults can learn new mnemonics and implement them to the benefit of memory performance, and can adjust their views and expectations about their memory to better cope with the changes that occur during healthy aging. Future work should focus on identifying the personal characteristics that predict who will benefit from training and on developing objective measures of the impact of memory rehabilitation on older adults’ everyday functioning.
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.